Free ISTQB CT-AI Practice Exam with Questions & Answers | Set: 4

Name: How to Pass CT-AI Exams
Brand: Examstrack
SKU: ct-ai
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Questions 31

Which ONE of the following tests is MOST likely to describe a useful test to help detect different kinds of biases in ML pipeline?

SELECT ONE OPTION

Options:

Testing the distribution shift in the training data for inappropriate bias.

Test the model during model evaluation for data bias.

Testing the data pipeline for any sources for algorithmic bias.

Check the input test data for potential sample bias.

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Questions 32

Which of the following is a dataset issue that can be resolved using pre-processing?

Options:

Insufficient data

Invalid data

Wanted outliers

Numbers stored as strings

Questions 33

Which ONE of the following describes a situation of back-to-back testing the LEAST?

SELECT ONE OPTION

Options:

Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.

Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for same data

Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.

Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.

Answer:

Explanation:

Back-to-back testing is a method where the same set of tests are run on multiple implementations of the system to compare their outputs. This type of testing is typically used to ensure consistency and correctness by comparing the outputs of different implementations under identical conditions. Let's analyze the options given:

A. Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.

This option describes a scenario where two different implementations of the same type of model are being compared using the same dataset. This is a typical back-to-back testing situation.

B. Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for the same data.

This option involves comparing a custom implementation with a standard implementation, which is also a typical back-to-back testing scenario to validate the custom model against a known benchmark.

C. Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.

This option involves comparing two different types of models (a neural network and a decision tree). This is not a typical scenario for back-to-back testing because the models are inherently different and would not be expected to produce identical results even on the same data.

D. Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.

This option involves comparing the outputs of the same model on slightly different datasets. This could be seen as a form of robustness testing or sensitivity analysis, but not typical back-to-back testing as it doesn’t involve comparing multiple implementations.

Based on this analysis, optionCis the one that describes a situation of back-to-back testing the least because it compares two fundamentally different models, which is not the intent of back-to-back testing.

Questions 34

Which of the following approaches would help overcome testing challenges associated with probabilistic and non-deterministic AI-based systems?

Options:

Run the test several times to ensure that the AI always returns the same correct test result

Decompose the system test into multiple data ingestion tests to determine if the AI system is getting a sufficient volume of input data

Decompose the system test into multiple data ingestion tests to determine if the AI system is getting precise and accurate input data

Run the test several times to generate a statistically valid test result to ensure that an appropriate number of answers are accurate

Questions 35

An engine manufacturing facility wants to apply machine learning to detect faulty bolts. Which of the following would result in bias in the model?

Options:

Selecting training data purposely excluding specific faulty conditions

Selecting training data by purposely including all known faulty conditions

Selecting testing data from a different dataset than the training dataset

Selecting testing data from a boat manufacturer's bolt longevity data

Questions 36

An airline has created an ML model to project fuel requirements for future flights. The model imports weather data such as wind speeds and temperatures, calculates flight routes based on historical routings from air traffic control, and estimates loads from average passenger and baggage weights. The model performed within an acceptable standard for the airline throughout the summer but as winter set in, the load weights became less accurate. After some exploratory data analysis, it became apparent that luggage weights were higher in the winter than in summer.

Which of the following statements BEST describes the problem and how it could have been prevented?

Options:

The model suffers from drift and therefore should be regularly tested to ensure that any occurrences of drift are detected soon enough for the problem to be mitigated

The model suffers from drift and therefore the performance standard should be eased until a new model with more transparency can be developed

The model suffers from corruption and therefore should be reloaded into the computer system being used, preferably with a method of version control to prevent further changes

The model suffers from a lack of transparency and therefore should be regularly tested to ensure that any progressive errors are detected soon enough for the problem to be mitigated