Temporary and transient tables in Snowflake share several characteristics, notably, neither table type has a Fail-safe period. Fail-safe is a feature that provides additional data protection beyond the Time Travel period. However, this feature does not apply to temporary or transient tables. Additionally, for both types of tables, the data retention period effectively ends when the tables are dropped. This means that once these tables are deleted, their data is not recoverable, distinguishing them from permanent tables, which benefit from Snowflake's Time Travel and Fail-safe features.References: Snowflake Documentation on Table Types

The RESULT_SCAN table function is used in Snowflake to perform additional processing on the results of a previously-run query. It allows users to reference the result set of a previous query by its query ID, enabling further analysis or transformations without re-executing the original query.

References:

Snowflake Documentation: RESULT_SCAN

The minimum Snowflake edition that supports database replication is the Enterprise edition. Database replication allows data to be replicated between different Snowflake accounts or regions, providing high availability and disaster recovery capabilities.

References:

Snowflake Documentation: Database Replication

To avoid timeouts during large data uploads with the PUT command in Snowflake, it is recommended to:

Compress files before loading: Compressed files are smaller and upload faster, reducing the risk of timeouts.

Increase the PARALLEL option value: This option allows more simultaneous upload threads, improving upload speed and efficiency for large datasets.

Semi-structured file formats and table staging do not directly impact timeouts, while enabling overwrite does not prevent timeouts but rather controls overwriting of existing files.

The Remote Disk I/O statistic in the Query Profile reflects time spent waiting on remote disk access, which can occur when data needs to be retrieved from external storage (remote). This metric is crucial for identifying bottlenecks related to I/O delays, often suggesting a need for performance optimization in data retrieval paths.

The other options relate to caching and network activity, but Remote Disk I/O specifically measures the wait time for data access from remote storage locations.

A pre-signed URL provides a way to access objects stored in a stage without requiring authentication to Snowflake. This URL contains all the necessary information for access control, including an expiration time, and it’s signed by the service's credentials. This mechanism allows users or applications to access specific files stored in a cloud storage location (stage) directly, bypassing the need to authenticate into Snowflake, ensuring secure and temporary access to the data.References: Snowflake Documentation on Stages and Data Loading

Snowflake's Continuous Data Protection (CDP) features encompass several mechanisms designed to protect data and ensure its availability and recoverability. Among these features, the one that has associated costs is Fail-safe.

Fail-safe is an additional layer of protection that kicks in after the Time Travel period expires. While Time Travel allows users to access historical data within a defined retention period (which can vary from 1 to 90 days depending on the Snowflake edition), Fail-safe provides a further 7 days (for a total of 7 additional days beyond the Time Travel period) during which Snowflake retains the data. This period is primarily intended for Snowflake's internal operations to recover data in the event of extreme scenarios, such as significant operational failures, and is not directly accessible by customers for data recovery purposes.

The associated costs with Fail-safe arise because Snowflake continues to store the data beyond the customer-specified Time Travel period, thereby incurring additional storage costs. It's important to note that while users do not incur direct costs for enabling Fail-safe (as it is an automatic feature of Snowflake), the extended storage of data during this period contributes to overall storage costs.

References:

Snowflake Documentation on Continuous Data Protection: Continuous Data Protection (CDP)

Snowflake Documentation on Fail-safe: Understanding Fail-safe

In Snowflake, a Share is the object that contains all the information required to share a database with other Snowflake accounts. Shares are used to securely share data stored in Snowflake tables and views, enabling data providers to grant data consumers access to their datasets without duplicating data. When a database is shared, it can include one or more schemas, and each schema can contain tables, views, or both.

References:

Snowflake Documentation on Shares: Shares

In Snowflake, a tag can be associated with only one masking policy for each data type. This means that for a given data type, you can define a single masking policy to be applied when a tag is used. Tags and masking policies are part of Snowflake's data classification and governance features, allowing for data masking based on the context defined by the tags.

References:

Snowflake Documentation: Tag-Based Masking Policies

To handle a large number of complex queries, configuring the warehouse in auto-scale mode by increasing the number of warehouse clusters is recommended. This setup allows Snowflake to dynamically add clusters as demand increases, ensuring better performance and concurrency. Increasing the number of clusters provides scalability for concurrent users and heavy workloads, improving response times without impacting individual query performance.

 A network policy in Snowflake is used to restrict access to the Snowflake account from unauthorized or unknown sources. It allows administrators to specify allowed IP address ranges, thus preventing access from any IP addresses not listed in the policy1.

 To validate data unloaded using the COPY INTO command, a Snowflake user can use the VALIDATION_MODE parameter within the SQL statement to test the files for errors without loading them3.

Snowflake stores metadata for rows in micro-partitions, including the range of values for each column and the number of distinct values1.

 Snowflake supports the storage of semi-structured data using the ARRAY and VARIANT data types. The ARRAY data type can directly contain VARIANT, and thus indirectly contain any other data type, including itself. The VARIANT data type can store a value of any other type, including OBJECT and ARRAY, and is often used to represent semi-structured data formats like JSON, Avro, ORC, Parquet, or XML34.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

 To view and modify resource monitors, the least privileges needed are MONITOR and MODIFY. These privileges allow a user to monitor credit usage and make changes to resource monitors3.

The Statistic output in the Query Profile Overview of Snowflake provides detailed insights into the performance of different parts of the query. Specifically, it highlights the "Most expensive nodes," which are the operations or steps within the query execution that consume the most resources, such as CPU and memory. Identifying these nodes helps in pinpointing performance bottlenecks and optimizing query execution by focusing efforts on the most resource-intensive parts of the query.

References:

Snowflake Documentation on Query Profile Overview: It details the components of the profile overview, emphasizing how to interpret the statistics section to improve query performance by understanding which nodes are most resource-intensive.

QUSTION NO: 582

How do secure views compare to non-secure views in Snowflake?

A. Secure views execute slowly compared to non-secure views.

B. Non-secure views are preferred over secure views when sharing data.

C. Secure views are similar to materialized views in that they are the most performant.

D. There are no performance differences between secure and non-secure views.

Answer: D

Secure views and non-secure views in Snowflake are differentiated primarily by their handling of data access and security rather than performance characteristics. A secure view enforces row-level security and ensures that the view definition is hidden from the users. However, in terms of performance, secure views do not inherently execute slower or faster than non-secure views. The performance of both types of views depends more on other factors such as underlying table design, query complexity, and system workload rather than the security features embedded in the views themselves.

References:

Snowflake Documentation on Views: This section provides an overview of both secure and non-secure views, clarifying that the main difference lies in security features rather than performance, thus supporting the assertion that there are no inherent performance differences.

QUSTION NO: 583

When using SnowSQL, which configuration options are required when unloading data from a SQL query run on a local machine? {Select TWO).

A. echo

B. quiet

C. output_file

D. output_format

E. force_put_overwrite

Answer: C, D

When unloading data from SnowSQL (Snowflake's command-line client), to a file on a local machine, you need to specify certain configuration options to determine how and where the data should be outputted. The correct configuration options required are:

C. output_file: This configuration option specifies the file path where the output from the query should be stored. It is essential for directing the results of your SQL query into a local file, rather than just displaying it on the screen.

D. output_format: This option determines the format of the output file (e.g., CSV, JSON, etc.). It is crucial for ensuring that the data is unloaded in a structured format that meets the requirements of downstream processes or systems.

These options are specified in the SnowSQL configuration file or directly in the SnowSQL command line. The configuration file allows users to set defaults and customize their usage of SnowSQL, including output preferences for unloading data.

References:

Snowflake Documentation: SnowSQL (CLI Client) at Snowflake Documentation

Snowflake Documentation: Configuring SnowSQL at Snowflake Documentation

QUSTION NO: 584

How can a Snowflake user post-process the result of SHOW FILE FORMATS?

A. Use the RESULT_SCAN function.

B. Create a CURSOR for the command.

C. Put it in the FROM clause in brackets.

D. Assign the command to RESULTSET.

Answer: A

first run SHOW FILE FORMATS

then SELECT * FROM TABLE(RESULT_SCAN(LAST_QUERY_ID(-1)))

https://docs.snowflake.com/en/sql-reference/functions/result_scan#usage-notes

QUSTION NO: 585

Which file function gives a user or application access to download unstructured data from a Snowflake stage?

A. BUILD_SCOPED_FILE_URL

B. BUILD_STAGE_FILE_URL

C. GET_PRESIGNED_URL

D. GET STAGE LOCATION

Answer: C

The function that provides access to download unstructured data from a Snowflake stage is:

C. GET_PRESIGNED_URL: This function generates a presigned URL for a single file within a stage. The generated URL can be used to directly access or download the file without needing to go through Snowflake. This is particularly useful for unstructured data such as images, videos, or large text files, where direct access via a URL is needed outside of the Snowflake environment.

Example usage:

SELECT GET_PRESIGNED_URL('stage_name', 'file_path');

This function simplifies the process of securely sharing or accessing files stored in Snowflake stages with external systems or users.

References:

Snowflake Documentation: GET_PRESIGNED_URL Function at Snowflake Documentation

QUSTION NO: 586

When should a multi-cluster virtual warehouse be used in Snowflake?

A. When queuing is delaying query execution on the warehouse

B. When there is significant disk spilling shown on the Query Profile

C. When dynamic vertical scaling is being used in the warehouse

D. When there are no concurrent queries running on the warehouse

Answer: A

A multi-cluster virtual warehouse in Snowflake is designed to handle high concurrency and workload demands by allowing multiple clusters of compute resources to operate simultaneously. The correct scenario to use a multi-cluster virtual warehouse is:

A. When queuing is delaying query execution on the warehouse: Multi-cluster warehouses are ideal when the demand for compute resources exceeds the capacity of a single cluster, leading to query queuing. By enabling additional clusters, you can distribute the workload across multiple compute clusters, thereby reducing queuing and improving query performance.

This is especially useful in scenarios with fluctuating workloads or where it's critical to maintain low response times for a large number of concurrent queries.

References:

Snowflake Documentation: Multi-Cluster Warehouses at Snowflake Documentation

QUSTION NO: 587

A JSON object is loaded into a column named data using a Snowflake variant datatype. The root node of the object is BIKE. The child attribute for this root node is BIKEID.

Which statement will allow the user to access BIKEID?

A. select data:BIKEID

B. select data.BIKE.BIKEID

C. select data:BIKE.BIKEID

D. select data:BIKE:BIKEID

Answer: C

In Snowflake, when accessing elements within a JSON object stored in a variant column, the correct syntax involves using a colon (:) to navigate the JSON structure. The BIKEID attribute, which is a child of the BIKE root node in the JSON object, is accessed using data:BIKE.BIKEID. This syntax correctly references the path through the JSON object, utilizing the colon for JSON field access and dot notation to traverse the hierarchy within the variant structure.References: Snowflake documentation on accessing semi-structured data, which outlines how to use the colon and dot notations for navigating JSON structures stored in variant columns.

QUSTION NO: 588

Which Snowflake tool is recommended for data batch processing?

A. SnowCD

B. SnowSQL

C. Snowsight

D. The Snowflake API

Answer: B

For data batch processing in Snowflake, the recommended tool is:

B. SnowSQL: SnowSQL is the command-line client for Snowflake. It allows for executing SQL queries, scripts, and managing database objects. It's particularly suitable for batch processing tasks due to its ability to run SQL scripts that can execute multiple commands or queries in sequence, making it ideal for automated or scheduled tasks that require bulk data operations.

SnowSQL provides a flexible and powerful way to interact with Snowflake, supporting operations such as loading and unloading data, executing complex queries, and managing Snowflake objects from the command line or through scripts.

References:

Snowflake Documentation: SnowSQL (CLI Client) at Snowflake Documentation

QUSTION NO: 589

How does the Snowflake search optimization service improve query performance?

A. It improves the performance of range searches.

B. It defines different clustering keys on the same source table.

C. It improves the performance of all queries running against a given table.

D. It improves the performance of equality searches.

Answer: D

The Snowflake Search Optimization Service is designed to enhance the performance of specific types of queries on large tables. The correct answer is:

D. It improves the performance of equality searches: The service optimizes the performance of queries that use equality search conditions (e.g., WHERE column = value). It creates and maintains a search index on the table's columns, which significantly speeds up the retrieval of rows based on those equality search conditions.

This optimization is particularly beneficial for large tables where traditional scans might be inefficient for equality searches. By using the Search Optimization Service, Snowflake can leverage the search indexes to quickly locate the rows that match the search criteria without scanning the entire table.

References:

Snowflake Documentation: Search Optimization Service at Snowflake Documentation

QUSTION NO: 590

What compute resource is used when loading data using Snowpipe?

A. Snowpipe uses virtual warehouses provided by the user.

B. Snowpipe uses an Apache Kafka server for its compute resources.

C. Snowpipe uses compute resources provided by Snowflake.

D. Snowpipe uses cloud platform compute resources provided by the user.

Answer: C

Snowpipe is Snowflake's continuous data ingestion service that allows for loading data as soon as it's available in a cloud storage stage. Snowpipe uses compute resources managed by Snowflake, separate from the virtual warehouses that users create for querying data. This means that Snowpipe operations do not consume the computational credits of user-created virtual warehouses, offering an efficient and cost-effective way to continuously load data into Snowflake.

References:

Snowflake Documentation: Understanding Snowpipe

QUSTION NO: 591

What is one of the characteristics of data shares?

A. Data shares support full DML operations.

B. Data shares work by copying data to consumer accounts.

C. Data shares utilize secure views for sharing view objects.

D. Data shares are cloud agnostic and can cross regions by default.

Answer: C

Data sharing in Snowflake allows for live, read-only access to data across different Snowflake accounts without the need to copy or transfer the data. One of the characteristics of data shares is the ability to use secure views. Secure views are used within data shares to restrict the access of shared data, ensuring that consumers can only see the data that the provider intends to share, thereby preserving privacy and security.

References:

Snowflake Documentation: Understanding Secure Views in Data Sharing

QUSTION NO: 592

Which DDL/DML operation is allowed on an inbound data share?

A. ALTER TA3LE

B. INSERT INTO

C. MERGE

D. SELECT

Answer: D

In Snowflake, an inbound data share refers to the data shared with an account by another account. The only DDL/DML operation allowed on an inbound data share is SELECT. This restriction ensures that the shared data remains read-only for the consuming account, maintaining the integrity and ownership of the data by the sharing account.

References:

Snowflake Documentation: Using Data Shares

QUSTION NO: 593

In Snowflake, the use of federated authentication enables which Single Sign-On (SSO) workflow activities? (Select TWO).

A. Authorizing users

B. Initiating user sessions

C. Logging into Snowflake

D. Logging out of Snowflake

E. Performing role authentication

Answer: B C

Federated authentication in Snowflake allows users to use their organizational credentials to log in to Snowflake, leveraging Single Sign-On (SSO). The key activities enabled by this setup include:

B. Initiating user sessions: Federated authentication streamlines the process of starting a user session in Snowflake by using the existing authentication mechanisms of an organization.

C. Logging into Snowflake: It simplifies the login process, allowing users to authenticate with their organization's identity provider instead of managing separate credentials for Snowflake.

References:

Snowflake Documentation: Configuring Federated Authentication

QUSTION NO: 594

A user wants to upload a file to an internal Snowflake stage using a put command.

Which tools and or connectors could be used to execute this command? (Select TWO).

A. SnowCD

B. SnowSQL

C. SQL API

D. Python connector

E. Snowsight worksheets

Answer: B, E

To upload a file to an internal Snowflake stage using a PUT command, you can use:

B. SnowSQL: SnowSQL, the command-line client for Snowflake, supports the PUT command, allowing users to upload files directly to Snowflake stages from their local file systems.

E. Snowsight worksheets: Snowsight, the web interface for Snowflake, provides a user-friendly environment for executing SQL commands, including the PUT command, through its interactive worksheets.

References:

Snowflake Documentation: Loading Data into Snowflake using SnowSQL

Snowflake Documentation: Using Snowsight

Multi-Factor Authentication (MFA) provides increased login security for users connecting to Snowflake. Snowflake’s MFA is powered by Duo Security service, which adds an additional layer of security during the login process.

The purpose of the Snowflake SPLIT_TO_TABLE function is to split a string based on a specified delimiter and flatten the results into rows. This table function is useful for transforming a delimited string into a set of rows that can be further processed or queried5.

The LATERAL modifier for the FLATTEN function allows joining information outside the object (such as other columns in the source table) with the flattened data, creating a lateral view that correlates with the preceding tables in the FROM clause2345. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Running a virtual warehouse is required when loading data into a table and when unloading data from a table because these operations require compute resources that are provided by the virtual warehouse23.

 A Pre-signed URL should be used to access files stored in a Snowflake stage without requiring authentication into Snowflake. Pre-signed URLs are simple HTTPS URLs that provide temporary access to a file via a web browser, using a pre-signed access token. The expiration time for the access token is configurable, and this type of URL allows users or applications to directly access or download the files without needing to authenticate into Snowflake5.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

 In a managed access schema, the schema owner retains the OWNERSHIP privilege of any tables added to the schema. This means that while object owners have certain privileges over the objects they create, only the schema owner can manage privilege grants on these objects1.

The SECURITYADMIN role in Snowflake can manage any object grant globally, including modifying and revoking grants. This role has the necessary privileges to oversee and control access to all securable objects within the Snowflake environment4.

The statistics that can be used to identify queries with inefficient pruning are ‘Partitions scanned’ and ‘Partitions total’. These statistics indicate how much of the data was actually needed and scanned versus the total available, which can highlight inefficiencies in data pruning34.

When federated authentication is activated in Snowflake, users authenticate via an external identity provider (IdP) rather than using Snowflake-managed credentials. Therefore, a user with a password defined by Snowflake will be unable to enter a password and must use their IdP credentials to log in.

QUSTION NO: 579

What value provides information about disk usage for operations where intermediate results do not fit in memory in a Query Profile?

A. IO

B. Network

C. Pruning

D. Spilling

Answer: D

In Snowflake, when a query execution requires more memory than what is available, Snowflake handles these situations by spilling the intermediate results to disk. This process is known as "spilling." The Query Profile in Snowflake includes a metric that helps users identify when and how much data spilling occurs during the execution of a query. This information is crucial for optimizing queries as excessive spilling can significantly slow down query performance. The value that provides this information about disk usage due to intermediate results not fitting in memory is appropriately labeled as "Spilling" in the Query Profile.

References:

Snowflake Documentation on Query Profile and Performance: This section explains the various components of the query profile, including the spilling metric, which indicates disk usage for operations where intermediate results exceed available memory.

 In Snowflake, if a temporary table and a permanent table have the same name within the same schema, the temporary table takes precedence over the permanent table within the session where the temporary table was created4.

Snowflake’s architecture allows for the independent scaling of compute resources, meaning you can increase or decrease the computational power as needed without affecting storage. This separation also means that storage can grow independently of compute usage, allowing for more flexible and cost-effective data management. 

The parameter DATA_RETENTION_TIME_IN_DAYS can be set at the account level to define the minimum number of days Snowflake retains historical data for Time Travel1.

The ACCESS_HISTORY view in Snowflake is utilized to support compliance auditing. It provides detailed information on data access within Snowflake, including reads and writes by user queries. This view is essential for regulatory compliance auditing as it offers insights into the usage of tables and columns, and maintains a direct link between the user, the query, and the accessed data1.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

 When the DROP MANAGED ACCOUNT command is executed in Snowflake, it removes the managed account, including all objects created within the account, and access to the account is immediately restricted2.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

 A directory table in Snowflake is an object layered on a stage that is used to store file-level metadata. It is not a separate database object but is conceptually similar to an external table because it stores metadata about the data files in the stage5.

 Once an internal stage is dropped in Snowflake, it cannot be recovered or restored using Time Travel or UNDROP commands. The only option is to recreate the dropped stage

The LOOP type of loop in Snowflake Scripting does not have a built-in termination condition and requires a BREAK statement to stop executing4.

In Snowflake, to extract a specific value from a VARIANT column, you use the column name followed by a colon and then the key. The keys are case-sensitive. Therefore, to extract the employee’s name from the “Data” column, the correct syntax is data:employee.name.

The purpose of a Query Profile is to provide a detailed analysis of a particular query’s execution plan, including the mechanics, behavior, and performance. It helps in identifying potential performance bottlenecks and areas for optimization

 Virtual warehouses in Snowflake can be started and stopped at any time, providing flexibility in managing compute resources. They can also be resized at any time, even while running, to accommodate varying workloads910. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 In Snowflake, a user with the SECURITYADMIN role or higher can activate and enforce a network policy for all users in an account. Additionally, a role that has ownership of the network policy can also activate and enforce it

The GOVERNANCE_VIEWER role in the ACCOUNT_USAGE schema provides visibility into policy-related information within Snowflake. This role is specifically designed to access views that display object metadata and usage metrics related to governance12.

In Snowflake, permanent tables and external tables can be shared with other accounts using Secure Data Sharing. Temporary tables, transient tables, and UDTFs are not shareable objects

 A user with the OWNERSHIP privilege on a user object or the CREATE USER privilege on the account can create or manage other users in a Snowflake account56.

The BUILD_STAGE_FILE_URL function generates a Snowflake-hosted file URL to a staged file using the stage name and relative file path as inputs2.

 One of the characteristics of materialized views in Snowflake is that they allow multiple tables to be joined in the underlying query. This enables the pre-computation of complex queries involving joins, which can significantly improve the performance of subsequent queries that access the materialized view4. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The query acceleration service in Snowflake is designed to benefit queries that involve large scans and selective filters. This service can offload portions of the query processing work to shared compute resources, which can handle these types of workloads more efficiently by performing more work in parallel and reducing the wall-clock time spent in scanning and filtering2. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The parameter STATEMENT_QUEUED_TIMEOUT_IN_SECONDS sets the limit for a query to wait in the queue in order to get its chance of running on the warehouse. The query will quit after reaching this limit. By default, the value of this parameter is 0 which mean the queries will wait indefinitely in the waiting queue

https://community.snowflake.com/s/article/Warehouse-Concurrency-and-Statement-Timeout-Parameters#:~:text=The%20parameter%20STATEMENT_QUEUED_TIMEOUT_IN_SECONDS%20sets%20the,indefinitely%20in%20the%20waiting%20queue .

 Query compilation in Snowflake occurs in the Cloud Services layer. This layer is responsible for coordinating and managing all aspects of the Snowflake service, including authentication, infrastructure management, metadata management, query parsing and optimization, and security. By handling these tasks, the Cloud Services layer enables the Compute layer to focus on executing queries, while the Storage layer is dedicated to persistently storing data.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Snowflake Architecture1

Micro-partitioning is a Snowflake function that is maintained separately from the data and supports features such as Time Travel, Secure Data Sharing, and pruning. It allows Snowflake to efficiently manage and query large datasets by organizing them into micro-partitions1.

 External stages can be altered and dropped in Snowflake. An external stage points to an external location, such as an S3 bucket, where data files are stored. Users can modify the stage’s definition or drop it entirely if it’s no longer needed. This is in contrast to table stages, which are tied to specific tables and cannot be altered or dropped independently.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Stages1

To generate a report listing all Snowflake logins within the last 90 days, the ACCOUNT_USAGE.LOGIN_HISTORY view should be used. This view provides information about login attempts, including successful and unsuccessful logins, and is suitable for security audits4.

Snowflake supports two geospatial data types: GEOGRAPHY and GEOMETRY. The GEOGRAPHY data type is used to store geospatial data that models the Earth as a perfect sphere, which is suitable for global geospatial data. This data type follows the WGS 84 standard and is used for storing points, lines, and polygons on the Earth’s surface. The GEOMETRY data type, on the other hand, represents features in a planar (Euclidean, Cartesian) coordinate system and is typically used for local spatial reference systems. Since the question specifically asks about geospatial data, which commonly refers to Earth-related spatial data, the correct answer is GEOGRAPHY3. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 Even if a developer is granted ownership of a table with a masking policy, they will not be able to modify the table to read the masked data if their role does not have the necessary permissions. Ownership of a table does not automatically confer the ability to alter masking policies, which are designed to protect sensitive data. Masking policies are applied at the schema level and require specific privileges to modify12.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Masking Policies

The auto-suspend and auto-resume settings in Snowflake apply to the entire virtual warehouse. These settings allow the warehouse to automatically suspend when it’s not in use, helping to save on compute costs. When queries or tasks are submitted to the warehouse, it can automatically resume operation. This functionality is designed to optimize resource usage and cost-efficiency.

References:

SnowPro Core Certification Exam Study Guide (as of 2021)

Snowflake documentation on virtual warehouses and their settings (as of 2021)

Parquet is a columnar storage file format that is optimized for performance in Snowflake. It is designed to be efficient for both storage and query performance, particularly for complex queries on large datasets. Parquet files support efficient compression and encoding schemes, which can lead to significant savings in storage and speed in query processing, making it the most performant file format for loading data into Snowflake.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Data Loading1

Snowpipe data load history is retained for 64 days. This retention period allows users to review and audit the data load operations performed by Snowpipe over a significant period of time, which can be crucial for troubleshooting and ensuring data integrity.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Snowpipe1

 Materialized Views in Snowflake are designed to store the result of a query and can be refreshed to maintain up-to-date data. However, they have certain limitations, one of which is that they cannot be defined using a JOIN clause. This means that a Materialized View can only be created based on a single source table and cannot combine data from multiple tables using JOIN operations.

References:

Snowflake Documentation on Materialized Views

SnowPro® Core Certification Study Guide

 The COPY INTO command in Snowflake can be configured to output data into a single file by setting the MAX_FILE_NUMBER option to 1. This option limits the number of files generated by the command, ensuring that only one file is created regardless of the amount of data being exported.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Data Unloading

Assigning the custom role to the SYSADMIN role is considered a best practice because it allows the SYSADMIN role to manage objects created by the custom role. This is important for maintaining proper access control and ensuring that the SYSADMIN can perform necessary administrative tasks on objects created by users with the custom role.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Section 1.3 - SnowPro Core Certification Study Guide1

Snowflake’s Time Travel feature is used for both querying historical data in tables and restoring and cloning historical data in databases, schemas, and tables3. It allows users to access historical data within a defined period (1 day by default, up to 90 days for Snowflake Enterprise Edition) and is a key feature for data recovery and management. References: [COF-C02] SnowPro Core Certification Exam Study Guide

When a CLUSTER BY clause is added to a Snowflake table, it specifies one or more columns to organize the data within the table’s micro-partitions. This clustering aims to colocate data with similar values in the same or adjacent micro-partitions. By doing so, it enhances the efficiency of query pruning, where the Snowflake query optimizer can skip over irrelevant micro-partitions that do not contain the data relevant to the query, thereby improving performance.

References:

Snowflake Documentation on Clustering Keys & Clustered Tables1.

Community discussions on how source data’s ordering affects a table with a cluster key

 Snowflake’s architecture allows for the use of a query result cache that stores the results of queries for a period of time. If the same query is run again and the underlying data has not changed, Snowflake can retrieve the result from this cache without needing to re-run the query on an active warehouse, thus saving on compute resources.

Snowflake automatically rotates encryption keys when they are more than 30 days old. Active keys are retired, and new keys are created. This process is part of Snowflake’s comprehensive security measures to ensure data protection and is managed entirely by the Snowflake service without requiring user intervention.

References:

Understanding Encryption Key Management in Snowflake

Fail-safe is a feature in Snowflake that provides an additional layer of data protection. After the Time Travel retention period ends, Fail-safe offers a non-configurable 7-day period during which historical data may be recoverable by Snowflake. This period is designed to protect against accidental data loss and is not intended for customer access.

References: Understanding and viewing Fail-safe | Snowflake Documentation

 To find information about query activity from 90 days ago, a Snowflake user should use the account_usage.query_history_archive view. This view is designed to provide access to historical query data beyond the default 14-day retention period found in the standard query_history view. It allows users to analyze and audit past query activities for up to 365 days after the date of execution, which includes the 90-day period mentioned.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Account Usage Schema1

A clustering key in Snowflake is used to co-locate similar data within the same micro-partitions to improve query performance, especially for large tables where data is not naturally ordered or has become fragmented due to extensive DML operations. The appropriate use of a clustering key can lead to improved scan efficiency and better column compression, resulting in faster query execution times.

The indicators that it may be appropriate to use a clustering key for a table include:

D. Queries on the table are running slower than expected: This can happen when the data in the table is not well-clustered, leading to inefficient scans during query execution.

E. The clustering depth for the table is large: A large clustering depth indicates that the table’s data is spread across many micro-partitions, which can degrade query performance as more data needs to be scanned.

References:

Snowflake Documentation on Clustering Keys & Clustered Tables

Snowflake Documentation on SYSTEM$CLUSTERING_INFORMATION

Stack Overflow discussion on cluster key selection in Snowflake

The correct command to view all roles granted to a specific user in Snowflake is SHOW GRANTS TO USER ;. This command lists all access control privileges that have been explicitly granted to the specified user. References: SHOW GRANTS | Snowflake Documentation

 Clustering is a technique used in Snowflake to improve the performance of queries. It involves organizing the data in a table into micro-partitions based on the values of one or more columns. This organization allows Snowflake to efficiently prune non-relevant micro-partitions during a query, which reduces the amount of data scanned and improves query performance.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Clustering

 In Snowflake, a reader account is a type of account that is intended for consuming shared data rather than performing any data management or DDL operations. The CREATE SHARE command is used to share data from your account with another account, which is not a capability provided to reader accounts. Reader accounts are typically restricted from creating shares, as their primary purpose is to read shared data rather than to share it themselves.

References:

Snowflake Documentation on Reader Accounts

SnowPro® Core Certification Study Guide

 In the query profiler view, the components that represent areas that can be used to help optimize query performance include ‘Bytes scanned’ and ‘Number of partitions scanned’. ‘Bytes scanned’ indicates the total amount of data the query had to read and is a direct indicator of the query’s efficiency. Reducing the bytes scanned can lead to lower data transfer costs and faster query execution. ‘Number of partitions scanned’ reflects how well the data is clustered; fewer partitions scanned typically means better performance because the system can skip irrelevant data more effectively.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Query Profiling1

When a virtual warehouse in Snowflake is resized, specifically when it is increased in size, the additional compute resources become immediately available to all running and queued queries. This means that the performance of these queries can improve due to the increased resources. Conversely, when the size of a warehouse is reduced, the compute resources are not removed until they are no longer being used by any current operations1.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Virtual Warehouses2

SnowSQL is the command-line client for Snowflake that allows users to execute SQL queries and perform all DDL and DML operations, including staging files for bulk data loading. It is specifically designed for scripting and automating tasks.

References:

SnowPro Core Certification Exam Study Guide

Snowflake Documentation on SnowSQL

https://docs.snowflake.com/en/user-guide/snowsql-use.html

Data Robot is recognized as a machine learning and data science partner within the Snowflake Partner Ecosystem. It provides an enterprise AI platform that enables users to build and deploy accurate predictive models quickly. As a partner, Data Robot integrates with Snowflake to enhance data science capabilities2.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Machine Learning & Data Science Partners

https://docs.snowflake.com/en/user-guide/ecosystem-analytics.html

 Snowflake’s architecture is designed to handle high concurrency workloads through several features, two of which are particularly effective:

B. The use of warehouse auto scaling: This feature allows Snowflake to automatically adjust the compute resources allocated to a virtual warehouse in response to the workload. If there is an increase in concurrent queries, Snowflake can scale up the resources to maintain performance.

D. Use of multi-clustered warehouses: Multi-clustered warehouses enable Snowflake to run multiple clusters of compute resources simultaneously. This allows for the distribution of queries across clusters, thereby reducing the load on any single cluster and improving the system’s ability to handle a high number of concurrent queries.

These features ensure that Snowflake can manage varying levels of demand without manual intervention, providing a seamless experience even during peak usage.

References:

Snowflake Documentation on Virtual Warehouses

SnowPro® Core Certification Study Guide

 Organizing files into logical paths can significantly improve the efficiency of data loading from an external stage. This practice helps in managing and locating files easily, which can be particularly beneficial when dealing with large datasets or complex directory structures1.

Snowflake’s data caching features include the ability to clear the least-recently used data when the data cache is full to make room for new data. Additionally, the RESULT_SCAN table function can access and filter the contents of the query result cache, allowing users to retrieve and work with the results of previous queries. The other statements are incorrect: the data cache is not saved on the remote storage layer when a virtual warehouse is suspended, users can access queries from the query result cache that were run by other users, and there is no setting called USE_METADATA_CACHE in Snowflake. References: Caching in the Snowflake Cloud Data Platform, Optimizing the warehouse cache

 Row level security in Snowflake is available starting with the Enterprise edition. This feature allows for the creation of row access policies that can control access to data at the row level within tables and views

When a running virtual warehouse in Snowflake is suspended and then restarted, the minimum amount of time it will incur charges for is 60 seconds2.

Snowpipe via REST API can reference both named internal stages within Snowflake and external stages, such as Amazon S3, Google Cloud Storage, or Microsoft Azure1. This means that Snowpipe is not limited to only external stages as a source for data loading.

References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation1

The SQL commands that consume a stream and advance the stream offset are those that result in changes to the data, such as UPDATE and INSERT operations. Specifically, ‘UPDATE TABLE FROM STREAM’ and ‘INSERT INTO TABLE SELECT FROM STREAM’ will consume the stream and move the offset forward, reflecting the changes made to the data.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

The Snowflake Enterprise edition includes database replication and failover for business continuity and disaster recovery, as well as extended time travel capabilities for longer data retention periods1.

 Snowflake supports unloading data in JSON and delimited file formats such as CSV and TSV. These formats are commonly used for data interchange and are supported by Snowflake for unloading operations

The LOGIN_HISTORY view in the ACCOUNT_USAGE schema provides information about login attempts, including both successful and failed logins. This view can be used to review the failed login attempts of a specific user for the past 30 days. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Shares in Snowflake are named objects that encapsulate all the information required to share databases, schemas, tables, secure views, and secure UDFs. These objects can be added to a share by granting privileges on them to the share via a database role

When loading data into Snowflake, it is recommended to organize the data into single files with 100-250 MB of compressed data per file. This size range is optimal for parallel processing and can help in achieving better performance during data loading operations. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The SQL command used to delete files from a Snowflake internal stage is REMOVE. This command can be used to remove files from either an internal or external stage within Snowflake

To go back to the first version of a transient table created three days prior, one can use Time Travel if the DATA_RETENTION_TIME_IN_DAYS was set to at least 3 days. This allows the user to access historical data within the specified retention period. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 Protegrity is listed as a data tokenization integration partner for Snowflake. This partnership allows Snowflake users to utilize Protegrity’s tokenization solutions within the Snowflake environment3.

References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation

 The Snowflake Cloud Services layer manages various services, including authentication and metadata management. This layer ties together all the different components of Snowflake to process user requests, manage sessions, and control access3.

An explain plan is beneficial because it allows for the evaluation of how a query will be processed without the need to actually execute the query. This can help in understanding the query’s performance implications and potential bottlenecks without consuming resources that would be used if the query were run

 The Snowflake Cloud Services layer is a collection of services that coordinate activities across Snowflake, tying together all the different components to process user requests, from login to query dispatch1.

References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation1

Snowflake credits are the unit of measure for the compute resources used in Snowflake. The number of credits consumed depends on the size of the virtual warehouse and the time it is running. Larger warehouses consume more credits per hour than smaller ones, and credits are billed for the time the warehouse is active, regardless of the actual usage within that time.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

The list command in Snowflake is used to validate and display the list of files in a specified stage. When a user has unloaded data to a stage, running the list @file__stage command will show all the files that have been uploaded to that stage, allowing the user to verify the data that was unloaded.

References:

Snowflake Documentation on Stages

SnowPro® Core Certification Study Guide

The COPY command in Snowflake allows for the reordering of columns as they are loaded into a table, and it also permits the omission of columns from the source file during the load process. This provides flexibility in handling the schema of the data being ingested. References: [COF-C02] SnowPro Core Certification Exam Study Guide

In Snowflake, to create a materialized view, the user must have the necessary privileges on the schema where the view will be created. These privileges are granted through roles, not directly to individual users. Therefore, the correct process is to grant the role to the user and then grant the privilege to create the materialized view to the role itself.

The statement GRANT ROLE MYROLE TO USER USER1; grants the specified role to the user, allowing them to assume that role and exercise its privileges. The subsequent statement CREATE MATERIALIZED VIEW ON SCHEMA MYDB.MYSCHEMA TO MYROLE; grants the privilege to create a materialized view within the specified schema to the role MYROLE. Any user who has been granted MYROLE can then create materialized views in MYDB.MYSCHEMA.

References:

Snowflake Documentation on Roles

Snowflake Documentation on Materialized Views

The primary responsibility of Snowflake’s virtual warehouses is to execute queries. Virtual warehouses are one of the key components of Snowflake’s architecture, providing the compute power required to perform data processing tasks such as running SQL queries, performing joins, aggregations, and other data manipulations.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Virtual Warehouses1

In Snowflake, Streams are the objects that track Data Manipulation Language (DML) changes made to tables, such as inserts, updates, and deletes. Streams record these changes along with metadata about each change, enabling actions to be taken using the changed data. This process is known as change data capture (CDC)2.

 In all Snowflake editions, two key capabilities are universally available:

B. Automatic encryption of all data: Snowflake automatically encrypts all data stored in its platform, ensuring security and compliance with various regulations. This encryption is transparent to users and does not require any configuration or management.

D. Object-level access control: Snowflake provides granular access control mechanisms that allow administrators to define permissions at the object level, including databases, schemas, tables, and views. This ensures that only authorized users can access specific data objects.

These features are part of Snowflake’s commitment to security and governance, and they are included in every edition of the Snowflake Data Cloud.

References:

Snowflake Documentation on Security Features

SnowPro® Core Certification Exam Study Guide

 Snowflake’s default Multi-Factor Authentication (MFA) service is powered by Duo Security. Users are required to install the Duo Mobile application on their devices to use MFA for increased login security when connecting to Snowflake. This service is managed entirely by Snowflake, and users do not need to sign up separately with Duo1.

 When a pipe is recreated using the CREATE OR REPLACE PIPE command, the load history of the pipe is reset. This means that Snowpipe will consider all files in the stage as new and will attempt to load them, even if they were loaded previously by the old pipe2.

 The minimum edition of Snowflake required to use a SCIM security integration is the Enterprise Edition. SCIM integrations are used for automated management of user identities and groups, and this feature is available starting from the Enterprise Edition of Snowflake. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake supports the VARIANT and ARRAY data types for semi-structured data. VARIANT can store values of any other type, including OBJECT and ARRAY, making it suitable for semi-structured data formats like JSON. ARRAY is used to store an ordered list of elements

 In the Snowflake Data Marketplace, the types of data listings available include ‘Vendor’, which refers to the providers of data, and ‘Personalized’, which indicates customized data offerings tailored to specific consumer needs45.

The Snowflake Cloud Services layer performs a variety of tasks, including the management of metadata and the parsing and optimization of queries. This layer is responsible for coordinating activities across Snowflake, including user session management, security, and query compilation3.

Best practices for using the ACCOUNTADMIN role include ensuring that all users with this role use Multi-factor Authentication (MFA) for added security. Additionally, it is recommended to assign the ACCOUNTADMIN role to at least two users to avoid delays in case of password recovery issues, but to as few users as possible to maintain strict control over account-level operations4.

The parse_json() function in Snowflake interprets an input string as a JSON document and produces a VARIANT value containing the JSON document. This function is specifically designed for parsing strings that contain valid JSON information3.