AI-900 Practice Test 1 — 10 Questions

Question 1

A company deploys an AI system to screen job applications. The system is a complex neural network that learns patterns from historical hiring data. A rejected candidate asks for an explanation, but the development team cannot describe how the decision was reached. Which Microsoft responsible AI principle is most directly violated?

Accepted Answer

Transparency. The system's inability to explain how it reached a decision violates the transparency principle, which requires AI systems to be understandable and interpretable. Complex neural networks often act as black boxes, making it impossible to provide meaningful explanations to users, directly contradicting Microsoft's responsible AI guideline that decisions should be explainable.

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Fairness

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Privacy and security

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Reliability and safety

Question 2

What is 'model versioning' and why is it essential in MLOps?

Accepted Answer

Tracking each iteration of a trained model for rollback, A/B testing, auditing, and reproducibility. Model versioning is the practice of tracking each iteration of a trained model, including its hyperparameters, training data snapshot, and evaluation metrics. In MLOps, it is essential because it enables rollback to a previous model if a new version performs poorly, supports A/B testing by comparing multiple model versions in production, provides an audit trail for compliance, and ensures reproducibility by capturing the exact code, data, and environment used to train each version.

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Updating the Python version used to run ML training scripts

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Releasing new features of the Azure ML service as versioned API updates

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Managing multiple versions of training data used by different model experiments

Question 3

What is 'AI transparency' in Microsoft's Responsible AI principles?

Accepted Answer

Ensuring people understand when they're interacting with AI, how it works, and what its limitations are. Option B is correct because AI transparency, as defined in Microsoft's Responsible AI principles, is about ensuring that users understand when they are interacting with an AI system, how the system makes decisions, and what its limitations are. This principle focuses on clear communication and documentation, not on open-sourcing code or financial reporting.

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Making AI model source code publicly available as open source

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Reporting all AI project costs transparently in financial statements

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Making all training data publicly available for independent researchers to audit

Question 4

A company uses Azure OpenAI Service to generate marketing copy. They notice that sometimes the generated text contains repetitive phrases or gets stuck in loops. They want to reduce this behavior without changing the overall creativity of the model. Which parameter should they adjust?

Accepted Answer

Increase the frequency_penalty parameter.. Increasing the frequency_penalty parameter reduces the likelihood of the model repeating the same phrases by penalizing tokens that have already appeared in the generated text. This directly addresses the repetitive loops without altering the overall creativity, as frequency_penalty specifically targets token frequency rather than randomness or diversity.

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Decrease the temperature parameter.

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Increase the presence_penalty parameter.

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Decrease the top_p parameter.

Question 5

A data scientist is training a regression model to predict house prices using features like square footage, number of bedrooms, and location. After evaluating the model on a test set, the data scientist wants to select a metric that measures the average magnitude of prediction errors in the same units as the target variable (price). Which evaluation metric should the data scientist use?

Accepted Answer

Root Mean Squared Error (RMSE). Root Mean Squared Error (RMSE) is the correct metric because it measures the average magnitude of prediction errors in the same units as the target variable (price). RMSE is computed as the square root of the average squared differences between predicted and actual values, which brings the error metric back to the original unit (e.g., dollars), making it directly interpretable for regression tasks like house price prediction.

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Accuracy

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F1 Score

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Precision

Question 6

A company uses Azure OpenAI Service to generate marketing copy. They want to ensure that the generated text does not contain offensive language or harmful stereotypes, even if the prompt inadvertently leads the model in that direction. Which Azure OpenAI feature should they configure to help prevent such outputs?

Accepted Answer

Content filtering. Content filtering in Azure OpenAI Service uses a set of pre-built, configurable filters to detect and block harmful content categories such as hate, violence, sexual, and self-harm. This feature operates at the service level, intercepting both prompts and completions to prevent offensive language or harmful stereotypes from being generated, regardless of how the prompt is phrased.

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Prompt engineering

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Fine-tuning

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Few-shot learning

Question 7

A medical research team wants to analyze MRI scans to identify and measure the precise boundaries of tumors. They need to assign each pixel in the image to a class (e.g., tumor, healthy tissue, background). Which Azure Computer Vision capability should they use?

Accepted Answer

Semantic Segmentation. Semantic segmentation assigns a class label to every pixel in an image, making it the correct choice for precisely delineating tumor boundaries in MRI scans. Azure Computer Vision's semantic segmentation capability outputs a pixel-level mask, enabling the research team to differentiate tumor, healthy tissue, and background at the finest granularity.

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Object Detection

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Image Classification

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Optical Character Recognition

Question 8

A marketing team uses Azure OpenAI Service to generate marketing copy. They notice the generated text is often repetitive, using the same phrases and words multiple times. Which parameter should they increase to directly reduce this repetition?

Accepted Answer

Frequency penalty. Frequency penalty directly reduces repetition by penalizing tokens that have already appeared in the generated text. A higher frequency penalty value (e.g., 0.5 to 1.0) decreases the likelihood of the model reusing the same phrases or words, making the output more diverse and less repetitive.

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Temperature

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Top-p

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Max tokens

Question 9

A data scientist trains a classification model to predict whether an email is spam or not. The model achieves 98% accuracy on the test set, but upon inspection, it classifies all emails as 'not spam' because the dataset has 95% non-spam emails. What is the most likely issue?

Accepted Answer

Data imbalance. The model achieves 98% accuracy by simply predicting all emails as 'not spam', which reflects the 95% majority class in the dataset. This is a classic symptom of class imbalance, where the model learns to exploit the skewed distribution rather than learning meaningful patterns to distinguish spam from non-spam. In Azure Machine Learning, techniques like SMOTE or stratified sampling are used to mitigate this issue.

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Overfitting

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Underfitting

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Feature scaling error

Question 10

A quality assurance team at a software company uses Azure OpenAI Service to generate compliance reports. They need the model to produce the exact same output for a given prompt every time the API is called, to ensure reproducibility during testing. Which parameter should they set to achieve this deterministic behavior?

Accepted Answer

Set temperature to 0. Setting temperature to 0 forces the model to choose the most likely token at each step, eliminating randomness and producing deterministic outputs for the same prompt. This is essential for reproducibility in testing scenarios where identical results are required across API calls.

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Set frequency penalty to 1

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Set top_p to 1

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Set max_tokens to the expected output length