ISQI ISQI-CT-AI Online Practice Questions and Exam Preparation

ISQI ISQI-CT-AI Online Questions & Answers

• Question 31:

  Which of the following is a technique used in machine learning?

  A. Decision trees
  B. Equivalence partitioning
  C. Boundary value analysis
  D. Decision tables
  A. Decision trees

  ---

  Decision trees are a fundamental technique used in supervised machine learning. The ISTQB CT-AI syllabus describes a decision tree as a tree-like machine learning model in which internal nodes represent decisions, branches represent possible outcomes, and leaf nodes represent predictions or classifications. Decision trees are commonly used for both classification and regression tasks.

  The other options are software testing techniques rather than machine learning techniques. Equivalence partitioning and boundary value analysis are black-box test design techniques, and decision tables are used to model business rules and logical conditions in testing.

  Therefore, Option A is the correct answer.

• Question 32:

  A motorcycle engine repair shop owner wants to detect a leaking exhaust valve and fix it before it fails and causes catastrophic damage to the engine. The shop developed and trained a predictive model with historical data files from known healthy engines and ones which experienced a catastrophic failure due to exhaust valve failure. The shop evaluated 200 engines using this model and then disassembled the engines to assess the true state of the valves, recording the results in the confusion matrix below.

  

  What is the precision of this predictive model?

  A. 90.0%
  B. 94.5%
  C. 98.9%
  D. 94.2%
  A. 90.0%

  ---

  The ISTQB CT-AI syllabus defines precision as:

  Precision = TP / (TP + FP)

  Precision measures the proportion of predicted positive cases that are actually positive.

  From the confusion matrix:

  True Positives (TP) = 90

  False Positives (FP) = 10

  Calculation:

  Precision = 90 / (90 + 10)

  = 90 / 100 = 0.90 = 90.0%

  Therefore, the precision of the predictive model is 90.0%, which corresponds to Option A.

  The other options do not match the precision formula defined in the syllabus and therefore are incorrect.

• Question 33:

  Which statement regarding the use of training, validation, and test data sets is correct?

  A. If only limited data is available, validation and test data sets can be combined in multiple ways during training.
  B. If limited data is available, it may be better to work without a separate test data set.
  C. Optimally, the data should be distributed equally between the training, validation, and test data sets.
  D. The data in the test data set must be equivalent to the data in the training data sets and to the data in the validation data sets.
  D. The data in the test data set must be equivalent to the data in the training data sets and to the data in the validation data sets.

  ---

  The ISTQB CT-AI syllabus (Section 3.2 - Model Evaluation) specifies the correct usage of training, validation, and test data sets. It emphasizes that the test data set must be representative of the real operational data and must be equivalent in distribution to the training and validation data sets, ensuring a fair and unbiased evaluation. Option D precisely matches this requirement.

  Option A contradicts the syllabus because validation and test data sets serve different purposes: validation is used for model tuning, while the test data set is used for final evaluation. Combining them undermines the reliability of results. Option B is incorrect because even with limited data, the syllabus recommends maintaining a test data set or using techniques such as cross-validation rather than eliminating testing. Option C is incorrect because an equal distribution between training, validation, and test

  data sets is not recommended; typically, the training data set is significantly larger (for example, 70-80%).

  Thus, Option D is the only statement aligned with the syllabus guidance.

• Question 34:

  Which ONE of the following situations MOST clearly indicates concept drift?

  A. The training dataset contains mislabeled records
  B. The model accuracy drops after deployment due to seasonal changes in input data
  C. The model overfits the training data
  D. The model lacks sufficient transparency
  B. The model accuracy drops after deployment due to seasonal changes in input data

  ---

• Question 35:

  Consider a machine learning model where the model is attempting to predict if a patient is at risk for stroke. The model collects information on each patient regarding their blood pressure, red blood cell count, smoking status, history of heart disease, cholesterol level, and demographics. Then, using a decision tree the model predicts whether or not the associated patient is likely to have a stroke in the near future. Once the model is created using a training dataset, it is used to predict a stroke in 80 additional patients. The table below shows a confusion matrix on whether or not the model made a correct or incorrect prediction.

  

  The testers have calculated what they believe to be an appropriate functional performance metric for the model. They calculated a value of 2/3 or 0.6667. Which metric did the testers calculate?

  A. F1-score
  B. Precision
  C. Recall
  D. Accuracy
  D. Accuracy

  ---

  From the confusion matrix: True Positives (TP) = 15 False Positives (FP) = 10

  False Negatives (FN) = 5 True Negatives (TN) = 50 First, calculate precision and recall, as defined in the ISTQB CT-AI syllabus (Section 3.2 - Functional Performance Criteria): Precision = TP / (TP + FP) = 15 / (15 + 10) = 15 / 25 = 0.6 Recall = TP / (TP + FN) = 15 / (15 + 5) = 15 / 20 = 0.75 The F1-score is the harmonic mean of precision and recall: F1 = 2 ?(Precision ?Recall) / (Precision + Recall)

  = 2 ?(0.6 ?0.75) / (0.6 + 0.75) = 0.9 / 1.35 = 0.6667

  Accuracy, for reference, would be:

  Accuracy = (TP + TN) / (TP + TN + FP + FN)

  = (15 + 50) / 80 = 0.8125 Since the calculated value 0.6667 exactly matches the F1-score, the correct answer is F1-score.

• Question 36:

  A beer company is trying to understand how much recognition its logo has in the market. It plans to do this by monitoring images on various social media platforms using a pre-trained neural network for logo detection. This particular model has been trained by looking for words, as well as matching colors on social media images. The company logo has a large word across the middle with a bold blue and magenta border. Which associated risk is most likely to occur when using this pre-trained model?

  A. There is no risk, as the model has already been trained.
  B. Insufficient function: the model was not trained to check for colors or words.
  C. Improper data preparation.
  D. Inherited bias: the model could have inherited unknown defects.
  D. Inherited bias: the model could have inherited unknown defects.

  ---

  According to the ISTQB CT-AI syllabus, pre-trained models may inherit biases, limitations, or defects from the data sets, assumptions, and processes used during their original training. Section 1.8.3 - Pre-trained Models explains that users of a pre-trained model typically do not have full visibility into or control over the original training data or training process. As a result, such models can carry forward unknown biases or inaccuracies that may not be appropriate for a new context or use case.

  In this scenario, although the pre-trained model appears technically suitable for detecting logos based on words and colors, the most significant risk is that it may have inherited unknown defects or biases from its original training, which could affect recognition accuracy or reliability when applied to this specific brand and market context.

  Therefore, Option D correctly identifies the most likely associated risk.

• Question 37:

  Which ONE of the following options is the MOST APPROPRIATE stage of the ML workflow to set model and algorithm hyperparameters?

  A. Evaluating the model
  B. Deploying the model
  C. Tuning the model
  D. Data testing
  C. Tuning the model

  ---

  Setting model and algorithm hyperparameters is performed during the model tuning stage of the ML workflow.

  Evaluating the model focuses on measuring performance against defined metrics and acceptance criteria, not on changing hyperparameters.

  Deploying the model occurs after training and tuning are complete; hyperparameters must already be finalized at this point.

  Tuning the model involves adjusting hyperparameters to optimize model performance and generalization, making this the correct stage.

  Data testing focuses on validating the quality, consistency, and suitability of the data rather than on configuring model parameters.

  The ISTQB CT-AI syllabus describes model tuning as the phase where hyperparameters are selected and optimized.

  Therefore, Option C (Tuning the model) is the correct answer.

• Question 38:

  Which of the following descriptions of quality aspects of a data set is correct?

  A. The quality aspect "Incomplete data" describes the fact that data is missing, for example, for a certain time interval.
  B. The quality aspect "Data not preprocessed" describes the fact that the collected data was recorded incorrectly.
  C. The quality aspect "Irrelevant data" describes the fact that irrelevant data does not affect the ML model.
  D. The quality aspect "Unbalanced data" describes the fact that the data used should be as up-to-date as possible.
  A. The quality aspect "Incomplete data" describes the fact that data is missing, for example, for a certain time interval.

  ---

  The ISTQB CT-AI syllabus describes several data quality aspects that affect machine learning performance. In Section 2.2 - Data Preparation, it explains that data sets may suffer from issues such as incomplete data, irrelevant data, incorrect data, unbalanced data, or data lacking preprocessing. "Incomplete data" refers to situations in which parts of the required data are missing, for example because certain time periods, records, or data sources were not captured. This matches Option A exactly.

  Option B is incorrect because "data not preprocessed" refers to data that has not undergone steps such as cleaning, normalization, or transformation, rather than data that was recorded incorrectly. Option C is incorrect because irrelevant data does negatively affect machine learning models by introducing noise and unnecessary features, which can degrade learning performance. Option D is incorrect because "unbalanced data" refers to disproportionate class distributions, not to how recent or up to date the

  data is.

  Thus, Option A is the only statement that correctly matches the syllabus definition of this data quality aspect.

• Question 39:

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

  A. Insufficient data
  B. Invalid data
  C. Wanted outliers
  D. Numbers stored as strings
  D. Numbers stored as strings

  ---

  The ISTQB CT-AI syllabus explains that data pre-processing includes activities such as data cleaning and data transformation. Data transformation covers changes to the format or representation of data, for example converting data types or encodings so that they can be correctly processed by machine learning algorithms.

  Specifically, the syllabus states that transformation may include converting categorical values to numerical values or changing data representations, such as numbers stored as strings. This directly corresponds to Option D, which describes a dataset issue that can be resolved through pre-processing.

  Option A is incorrect because insufficient data is a data availability problem that cannot be solved by pre-processing. Option B is incorrect because invalid data is typically addressed through data cleaning rather than transformation. Option C is incorrect because wanted outliers are intentionally retained and should not be removed or altered during pre-processing.

  Thus, Option D is the correct answer.

• Question 40:

  Which ONE of the following BEST describes reinforcement learning?

  A. Learning from labeled input-output pairs
  B. Learning by grouping similar data points
  C. Learning through interaction with an environment using rewards
  D. Learning by minimizing regression error
  C. Learning through interaction with an environment using rewards

  ---