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Question # 4

Max. Score: 2

Al-enabled medical devices are used nowadays for automating certain parts of the medical diagnostic processes. Since these are life-critical process the relevant authorities are considenng bringing about suitable certifications for these Al enabled medical devices. This certification may involve several facets of Al testing (I - V).

I.Autonomy

II.Maintainability

III.Safety

IV.Transparency

V.Side Effects

Which ONE of the following options contains the three MOST required aspects to be satisfied for the above scenario of certification of Al enabled medical devices?

SELECT ONE OPTION

A.

Aspects II, III and IV

B.

Aspects I, II, and III

C.

Aspects III, IV, and V

D.

Aspects I, IV, and V

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Question # 5

Which ONE of the following options describes the LEAST LIKELY usage of Al for detection of GUI changes due to changes in test objects?

SELECT ONE OPTION

A.

Using a pixel comparison of the GUI before and after the change to check the differences.

B.

Using a computer vision to compare the GUI before and after the test object changes.

C.

Using a vision-based detection of the GUI layout changes before and after test object changes.

D.

Using a ML-based classifier to flag if changes in GUI are to be flagged for humans.

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Question # 6

Which ONE of the following hardware is MOST suitable for implementing Al when using ML?

SELECT ONE OPTION

A.

64-bit CPUs.

B.

Hardware supporting fast matrix multiplication.

C.

High powered CPUs.

D.

Hardware supporting high precision floating point operations.

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Question # 7

Which ONE of the following options represents a technology MOST TYPICALLY used to implement Al?

SELECT ONE OPTION

A.

Search engines

B.

Procedural programming

C.

Case control structures

D.

Genetic algorithms

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Question # 8

A ML engineer is trying to determine the correctness of the new open-source implementation *X", of a supervised regression algorithm implementation. R-Square is one of the functional performance metrics used to determine the quality of the model.

Which ONE of the following would be an APPROPRIATE strategy to achieve this goal?

SELECT ONE OPTION

A.

Add 10% of the rows randomly and create another model and compare the R-Square scores of both the model.

B.

Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.

C.

Compare the R-Square score of the model obtained using two different implementations that utilize two different programming languages while using the same algorithm and the same training and testing data.

D.

Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.

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Question # 9

Which ONE of the following combinations of Training, Validation, Testing data is used during the process of learning/creating the model?

SELECT ONE OPTION

A.

Training data - validation data - test data

B.

Training data - validation data

C.

Training data • test data

D.

Validation data - test data

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Question # 10

Which ONE of the following options does NOT describe a challenge for acquiring test data in ML systems?

SELECT ONE OPTION

A.

Compliance needs require proper care to be taken of input personal data.

B.

Nature of data constantly changes with lime.

C.

Data for the use case is being generated at a fast pace.

D.

Test data being sourced from public sources.

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Question # 11

A software component uses machine learning to recognize the digits from a scan of handwritten numbers. In the scenario above, which type of Machine Learning (ML) is this an example of?

SELECT ONE OPTION

A.

Reinforcement learning

B.

Regression

C.

Classification

D.

Clustering

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Question # 12

An image classification system is being trained for classifying faces of humans. The distribution of the data is 70% ethnicity A and 30% for ethnicities B, C and D. Based ONLY on the above information, which of the following options BEST describes the situation of this image classification system?

SELECT ONE OPTION

A.

This is an example of expert system bias.

B.

This is an example of sample bias.

C.

This is an example of hyperparameter bias.

D.

This is an example of algorithmic bias.

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