AI-900 Practice Exam 62 — 20 Questions

Question 1

A data scientist is training a model to classify customer reviews as positive, negative, or neutral. The dataset contains 10,000 reviews, but only 500 of them are negative. The data scientist wants to ensure the model performs well on the minority class (negative reviews). Which technique should the data scientist consider to address the class imbalance?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use a resampling technique like SMOTE or random oversampling of the minority class. Resampling techniques like SMOTE (Synthetic Minority Oversampling Technique) or random oversampling directly address class imbalance by generating synthetic samples or duplicating existing samples from the minority class (negative reviews). This balances the training dataset, preventing the model from being biased toward the majority class (positive/neutral reviews) and improving recall for the minority class.

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Increase the learning rate

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Add more features to the model

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Use L1 regularization (Lasso)

Question 2

What is 'Azure AI Custom Vision' and how does it differ from Azure AI Vision?

Accepted Answer

Azure AI Vision offers pre-built general models; Custom Vision lets you train models for your specific categories. Azure AI Vision provides pre-trained models for common computer vision tasks like object detection, OCR, and image analysis without requiring custom training data. Azure AI Custom Vision, on the other hand, allows you to upload your own labeled images and train a model to recognize specific categories or objects that are unique to your business scenario. This distinction makes B correct because it highlights the key difference: pre-built general models versus custom-trained models.

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Azure AI Vision is for video; Custom Vision is for still images only

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Custom Vision is more expensive because it uses more advanced AI algorithms

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Azure AI Vision requires GPU compute; Custom Vision runs on CPU only

Question 3

A news agency needs to automatically process thousands of news articles and extract specific structured information: the publication date, the author's name, and the headline. None of these fields are standard predefined entities in Azure AI Language. Which Azure AI Language feature should they use to train a model to extract these custom entities?

Accepted Answer

Custom Named Entity Recognition (Custom NER). Custom Named Entity Recognition (Custom NER) is the correct choice because it allows you to train a model to extract domain-specific entities—such as publication date, author name, and headline—that are not part of Azure's predefined entity catalog. By providing labeled examples, Custom NER learns to identify these custom fields from unstructured text, enabling automated extraction from thousands of news articles.

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Key phrase extraction

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Sentiment analysis

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Language detection

Question 4

What is a foundation model in the context of AI?

Accepted Answer

A large general-purpose AI model trained at scale that can be adapted to many downstream tasks. A foundation model is a large-scale, general-purpose AI model trained on vast and diverse datasets, enabling it to be adapted or fine-tuned for a wide range of downstream tasks such as text generation, translation, and image recognition. This definition aligns with option B, as foundation models like GPT-4 or BERT are designed for broad applicability rather than a single task.

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A small specialized model optimized for a single specific task

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The underlying hardware infrastructure for running AI workloads

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A model that has been certified as ethically sound by regulators

Question 5

What is 'model explainability' using SHAP values in Azure Machine Learning?

Accepted Answer

Calculating each feature's contribution to a specific prediction to explain why the model made that decision. SHAP (SHapley Additive exPlanations) values are a game-theoretic approach that assigns each feature an importance value for a particular prediction. Option B is correct because SHAP values quantify the contribution of each input feature to the model's output, providing a local explanation for why a specific decision was made. This is distinct from global feature importance or model simplification.

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Explaining the model's predictions using a simplified version of the model that is easier to interpret

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Displaying the model's source code so users can verify what computations are performed

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Testing the model on a separate evaluation dataset to report overall accuracy

Question 6

What is computer vision and give three real-world application examples.

Accepted Answer

Computer vision enables machines to understand visual data — used in autonomous driving, medical imaging, and retail automation. Computer vision is a field of AI that enables machines to interpret and make decisions based on visual data from the world, such as images and videos. The three examples given—autonomous driving (e.g., detecting pedestrians and lane markings), medical imaging (e.g., analyzing X-rays for tumors), and retail automation (e.g., self-checkout systems recognizing products)—are classic real-world applications that demonstrate the breadth of computer vision beyond simple text recognition.

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Computer vision is limited to text recognition only; it cannot detect objects

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Computer vision only works on satellite imagery for geographic analysis

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Computer vision requires extremely expensive hardware unavailable in the cloud

Question 7

A manufacturing company uses overhead cameras on an assembly line to check that each part is present in the correct location on a circuit board. The system must not only confirm the part is there but also draw a box around each part to show its exact position. Which Azure Computer Vision capability should they use?

Accepted Answer

Object Detection. Object Detection is the correct capability because it not only identifies whether a specific object (like a circuit board part) is present in an image but also returns bounding box coordinates that indicate the exact location of each detected object. This meets the requirement to both confirm the part's presence and draw a box around it.

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Optical Character Recognition (OCR)

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Image Classification

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Face Detection

Question 8

What is the Azure AI Language feature 'custom named entity recognition' used for?

Accepted Answer

Training models to recognize domain-specific entity types unique to your business. Custom named entity recognition (NER) in Azure AI Language allows you to train a model to identify and extract domain-specific entities that are not covered by the pre-built entity catalog. This is achieved by providing labeled example data, which the service uses to learn the unique entity types relevant to your business, such as product codes, internal document IDs, or specialized medical terms.

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Automatically renaming Azure resources with appropriate names

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Replacing personally identifiable information with pseudonyms

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Detecting when text contains company-specific named brands

Question 9

A healthcare organization needs to automatically redact personally identifiable information (PII) such as patient names and addresses from unstructured medical notes, while keeping clinical terms like disease names and medications. They want to use prebuilt Azure AI Language features without any custom training. Which combination of features should they use?

Accepted Answer

PII detection and Text Analytics for Health. PII detection identifies and redacts personally identifiable information like patient names and addresses, while Text Analytics for Health extracts clinical entities such as diseases and medications from unstructured medical notes. Both are prebuilt Azure AI Language features that require no custom training, making them ideal for this healthcare redaction scenario.

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Key phrase extraction and sentiment analysis

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Named Entity Recognition (NER) and language detection

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Entity linking and conversational language understanding

Question 10

What is the Azure AI Evaluation SDK used for in generative AI development?

Accepted Answer

Systematically measuring quality (groundedness, relevance, coherence) and safety of generative AI responses. The Azure AI Evaluation SDK is specifically designed to systematically measure the quality and safety of generative AI responses. It evaluates key metrics such as groundedness (how well the response aligns with source data), relevance, and coherence, as well as safety aspects like content filtering and harm detection. This makes it essential for validating and improving generative AI applications before deployment.

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Evaluating the environmental impact of AI model training

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Evaluating Azure subscription costs for AI workloads

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A peer review system for human evaluation of AI responses

Question 11

A hotel chain receives thousands of online guest reviews each month. The management wants to automatically determine whether the overall feedback for each review is positive, negative, or neutral to identify areas for improvement. Which Azure AI Language feature should they use?

Accepted Answer

Sentiment analysis. Sentiment analysis is the correct Azure AI Language feature because it evaluates text to determine the overall sentiment—positive, negative, or neutral—at the document or sentence level. This directly matches the hotel chain's requirement to classify each review's feedback automatically, enabling them to identify areas for improvement based on sentiment trends.

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Key phrase extraction

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Named entity recognition

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Language detection

Question 12

A data scientist is training a logistic regression model to predict customer churn using a small dataset with 500 records and 200 features. The model achieves 97% accuracy on the training set but only 65% on a held-out test set, indicating severe overfitting. The data scientist wants to reduce overfitting by automatically eliminating irrelevant features. Which technique should the data scientist apply?

Accepted Answer

Apply L1 regularization (Lasso) to the model. L1 regularization (Lasso) adds a penalty equal to the absolute value of the magnitude of coefficients, which can shrink some coefficients exactly to zero. This performs automatic feature selection by eliminating irrelevant features, directly addressing the overfitting caused by having 200 features on only 500 records. The high training accuracy (97%) versus low test accuracy (65%) is a classic sign of overfitting that L1 regularization mitigates by reducing model complexity.

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Apply L2 regularization (Ridge) to the model

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Use k-fold cross-validation to select the best model

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Increase the number of training samples by data augmentation

Question 13

What is the Azure Machine Learning workspace?

Accepted Answer

The top-level Azure ML resource that organizes experiments, models, compute, and deployments. The Azure Machine Learning workspace is the top-level resource in Azure that serves as a centralized hub for managing all machine learning activities. It organizes experiments, models, compute targets, and deployments, providing a unified environment for the entire ML lifecycle. This is the correct answer because the workspace is the foundational resource that ties together all other Azure ML components.

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A web-based IDE for writing machine learning code in Python

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A virtual machine pre-configured with ML tools and libraries

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A dedicated GPU cluster for distributed deep learning training

Question 14

A hospital collects patient experience feedback in free-text form. They need to automatically (1) extract specific mentions of symptoms (e.g., 'headache', 'fever', 'fatigue') from the text, and (2) determine the overall emotional tone of each feedback (e.g., positive, negative, neutral). Which combination of Azure AI Language features should they use?

Accepted Answer

A. Entity recognition and sentiment analysis. The hospital needs two distinct NLP capabilities: extracting specific symptom mentions (entity recognition) and determining emotional tone (sentiment analysis). Azure AI Language's entity recognition identifies named entities like symptoms, while sentiment analysis evaluates text for positive, negative, or neutral sentiment. Together, they directly address both requirements without extraneous features.

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B. Key phrase extraction and question answering

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C. Language detection and text classification

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D. Summarization and translation

Question 15

A bank deploys an AI system to approve loan applications. The system was trained on historical data that contains systematic biases against certain ethnic groups. Despite awareness of this bias, the bank proceeds with deployment, expecting the system to correct itself over time. Which Microsoft responsible AI principle is most directly violated?

Accepted Answer

Fairness. The bank knowingly deployed an AI system trained on biased historical data, expecting it to self-correct. This directly violates the Fairness principle, which requires AI systems to treat all groups equitably and avoid discrimination. Microsoft's responsible AI framework mandates that biases be actively identified and mitigated before deployment, not left to chance.

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Reliability and safety

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Transparency

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Privacy and security

Question 16

A manufacturing company uses cameras on an assembly line to inspect products for cosmetic defects such as scratches, dents, or color inconsistencies. They need to classify each product as 'defective' or 'non-defective' and also identify the precise region (e.g., a specific area of the product surface) that contains the defect. Which Azure Computer Vision capability should they use?

Accepted Answer

Semantic segmentation. Semantic segmentation is the correct choice because it assigns a class label (e.g., 'defective' or 'non-defective') to every pixel in the image, enabling the model to not only classify the product but also delineate the exact boundary of the defect region. This pixel-level precision is required to identify the precise area of the product surface containing the scratch, dent, or color inconsistency.

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Image classification

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Object detection

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Optical Character Recognition (OCR)

Question 17

A company wants to build a chatbot that can engage in free-form conversations with customers, answering questions and providing information without being limited to a fixed set of responses. Which type of AI model is most suitable?

Accepted Answer

Generative language model. A generative language model is the most suitable for building a chatbot that engages in free-form conversations because it can generate novel, contextually relevant responses based on the input it receives, rather than selecting from a fixed set of predefined answers. This capability is essential for handling the open-ended nature of customer queries, where the chatbot must produce coherent and varied responses dynamically.

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Classification model

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Regression model

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Object detection model

Question 18

A company uses Azure OpenAI Service to generate executive summaries of lengthy reports. The generated summaries sometimes include information that was not present in the original report, making them unreliable. Which Azure OpenAI Service feature should the company use to anchor the model to the provided report content?

Accepted Answer

Use the 'Add your data' feature (also known as 'Azure OpenAI on your data'). The 'Add your data' feature (Azure OpenAI on your data) allows the model to ground its responses in the specific content you provide, such as the original report. This prevents the model from generating information not present in the source, addressing the hallucination issue directly by restricting the model's knowledge base to the uploaded documents.

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Increase the temperature parameter

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Increase the frequency_penalty parameter

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Use the system message to instruct the model to only use provided content

Question 19

A creative agency wants to use Azure OpenAI to generate unique images for social media campaigns based on text descriptions. Which Azure OpenAI model should they use for this purpose?

Accepted Answer

DALL-E 3. DALL-E 3 is the correct choice because it is the Azure OpenAI model specifically designed for generating images from natural language text descriptions. It uses a diffusion-based architecture to create high-quality, unique visuals that align with the provided prompts, making it ideal for creative social media campaigns.

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GPT-4

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Codex

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Whisper

Question 20

A company deploys an AI-powered voice assistant that only supports English. The assistant is used in a country where the official languages are English, French, and Dutch. Many users who speak French or Dutch cannot use the assistant effectively. Which Microsoft responsible AI principle is most directly relevant to this situation?

Accepted Answer

Inclusiveness. The assistant's inability to support French and Dutch users directly violates the inclusiveness principle, which requires AI systems to be designed for all users regardless of language, ability, or background. By supporting only English in a multilingual country, the system excludes a significant portion of the target audience, failing to provide equitable access.

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Fairness

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Reliability and safety

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Transparency