AI-900 Exam Simulation — 100 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.

Answer

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.

Answer

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.

Answer

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

Answer

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

Question 11

A data scientist trains a binary classification model to detect fraudulent credit card transactions. The dataset contains 99.5% legitimate transactions and 0.5% fraudulent transactions. The model predicts every transaction as legitimate and achieves 99.5% accuracy on the test set. Which metric would best reveal that the model is failing to identify any fraudulent transactions?

Accepted Answer

Recall. Recall (also known as sensitivity) measures the proportion of actual positive cases correctly identified by the model. In this scenario, the model predicts all transactions as legitimate, so it correctly identifies zero fraudulent transactions, giving a recall of 0%. Accuracy alone is misleading because the dataset is highly imbalanced (99.5% legitimate, 0.5% fraudulent), and a 99.5% accuracy can be achieved by simply predicting the majority class. Recall directly reveals the model's failure to detect any fraud.

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Precision

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F1 score

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Mean Absolute Error (MAE)

Question 12

A developer uses Azure OpenAI Service to generate product reviews for an e-commerce site. The developer notices that the model often repeats the same phrases within the same review, making the output sound unnatural. Which parameter should the developer adjust to reduce this repetition?

Accepted Answer

Frequency_penalty. 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. A higher frequency_penalty value (e.g., 0.5 to 1.0) discourages the model from reusing the same words or phrases, making the output more diverse and natural. This directly addresses the issue of repetitive phrasing in product reviews.

Answer

Temperature

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Top_p

Answer

Max_tokens

Question 13

A data scientist trains a regression model to predict house prices. The model has a mean absolute error (MAE) of $5,000 on the test set. Which statement best interprets this metric?

Accepted Answer

On average, the model's predictions are $5,000 away from the actual prices.. Option A is correct because Mean Absolute Error (MAE) measures the average absolute difference between predicted and actual values. An MAE of $5,000 means that, on average, each prediction deviates from the true house price by $5,000. This is a standard interpretation of MAE in regression metrics.

Answer

The model is accurate 95% of the time.

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The model's predictions are within $5,000 of the actual prices for 50% of the houses.

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The square root of the average squared error is $5,000.

Question 14

A data scientist trains a regression model to predict house prices using features like bedrooms, square footage, and location. The model achieves an R-squared of 0.95 on the test set. However, when deployed to predict prices in a new city with different property characteristics, the predictions are very inaccurate. Which concept best explains this poor performance?

Accepted Answer

Overfitting. The model achieved an R-squared of 0.95 on the test set, indicating excellent performance on data from the same distribution. However, when deployed to a new city with different property characteristics, the predictions were very inaccurate. This is a classic symptom of overfitting, where the model has learned noise and patterns specific to the training data (e.g., city-specific price trends) that do not generalize to unseen data from a different distribution.

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Underfitting

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High bias

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Data drift

Question 15

A logistics company receives thousands of handwritten shipping labels daily. They need an automated solution to extract the destination address, sender name, and package weight from these labels. Which prebuilt Azure Computer Vision capability should they use?

Accepted Answer

Optical Character Recognition (OCR). Option A is correct because Azure Computer Vision's Optical Character Recognition (OCR) API is specifically designed to extract printed or handwritten text from images. In this scenario, the handwritten shipping labels contain textual data (destination address, sender name, package weight), and OCR can read and digitize that text for automated processing. The other options address different visual tasks—object detection, classification, or facial recognition—none of which extract text content.

Answer

Object detection

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Image classification

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Facial recognition

Question 16

A manufacturing company wants to use Azure Computer Vision to inspect products on an assembly line for defects. They have a labeled dataset with images of defective and non-defective products. They need to not only classify products as defective or not, but also identify the exact location of the defect (e.g., a crack) in the image. Which Azure Computer Vision capability should they use?

Accepted Answer

Custom Vision object detection. Custom Vision object detection is the correct choice because it not only classifies images (defective vs. non-defective) but also localizes defects by drawing bounding boxes around them. The labeled dataset with defect locations directly supports training a model to output both class labels and spatial coordinates, which is exactly what object detection provides.

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Custom Vision image classification

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Azure Face API

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Optical Character Recognition (OCR)

Question 17

A developer wants to build a virtual assistant that can understand user intents such as 'Book a flight' or 'Check weather' and extract relevant entities like destination and date. The developer has a small set of labeled example utterances. Which Azure AI Language feature should the developer use?

Accepted Answer

Conversational Language Understanding (CLU). Conversational Language Understanding (CLU) is the correct Azure AI Language feature because it is specifically designed to extract both intents (e.g., 'Book a flight') and entities (e.g., destination, date) from user utterances. The developer has a small set of labeled examples, which CLU can use to train a custom model for intent recognition and entity extraction, making it ideal for building a virtual assistant.

Answer

Custom Question Answering

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Custom Text Classification

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Named Entity Recognition (NER)

Question 18

A museum wants to automatically transcribe handwritten labels on historical artifacts. The handwriting varies in style and may include numbers and special characters. Which Azure Computer Vision capability should they use?

Accepted Answer

Optical Character Recognition (OCR). Optical Character Recognition (OCR) is the correct choice because it is specifically designed to extract printed or handwritten text from images, including numbers and special characters. Azure Computer Vision's OCR API can handle varied handwriting styles and convert them into machine-readable text, making it ideal for transcribing historical artifact labels.

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Image captioning

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Facial recognition

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Object detection

Question 19

What is 'conversation history' and why is it important in multi-turn chatbot interactions?

Accepted Answer

The sequence of prior messages included in the prompt so the model can maintain context across turns. Conversation history is the sequence of prior messages included in the prompt to a language model, allowing it to maintain context across multiple turns in a dialogue. This is critical because the model itself has no inherent memory; without the history, each turn would be treated as an isolated query, breaking the flow of a multi-turn interaction. By appending previous user inputs and assistant responses to the prompt, the model can reference earlier statements and provide coherent, context-aware replies.

Answer

A database log of all chatbot conversations for compliance auditing

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A summary of frequently asked questions generated from past user interactions

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The total number of messages a user has sent to a chatbot over their lifetime

Question 20

A developer is using Azure OpenAI Service to generate product descriptions. They want the output to be highly focused and deterministic, with less randomness. Which parameter should they decrease?

Accepted Answer

Temperature. Temperature controls the randomness of the model's output. Lowering the temperature (e.g., from 1.0 to 0.2) makes the model more deterministic by reducing the probability of sampling less likely tokens, resulting in more focused and predictable responses.

Answer

Max tokens

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Top-p

Answer

Frequency penalty