AI-900 Practice Exam 63 — 20 Questions

Question 1

What is 'model deployment' in Azure Machine Learning?

Accepted Answer

Making a trained model available as a callable endpoint for applications to use. Model deployment in Azure Machine Learning is the process of taking a trained model and hosting it as a web service endpoint (e.g., via Azure Kubernetes Service or Azure Container Instances) so that applications can send data to it and receive predictions in real time or batch mode. This makes the model operational and accessible for inference, which is the core purpose of deployment.

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Uploading training data to Azure Blob Storage for model training

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Distributing the training job across multiple compute nodes

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Publishing a model to the Azure Marketplace for other organisations to purchase

Question 2

What is Azure Cognitive Search (Azure AI Search) and what role does it play in AI applications?

Accepted Answer

An enterprise search and retrieval service that powers RAG by indexing documents for semantic search. Azure Cognitive Search (now Azure AI Search) is a cloud-based enterprise search service that provides full-text search, vector search, and hybrid search capabilities. In AI applications, it plays a critical role in Retrieval Augmented Generation (RAG) by indexing documents and enabling semantic search, which allows AI models to retrieve relevant information from private data sources to ground their responses.

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A service for searching Azure subscription costs and billing records

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A web crawler that indexes public internet content like Bing

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A service for searching through Azure ML model training logs

Question 3

A company wants to build an FAQ bot that can answer questions based on its internal knowledge base. The questions from users are often phrased in different ways. They want to match the user's intent to pre-defined answers without training a custom model. Which Azure AI Language feature should they use?

Accepted Answer

Custom Question Answering. Custom Question Answering (formerly QnA Maker) is the correct choice because it allows you to ingest a knowledge base (e.g., FAQs, manuals) and match user questions to pre-defined answers using a built-in ranking model, without training a custom ML model. It handles varied phrasing through semantic understanding and returns the best answer from the curated content, directly addressing the requirement to match intent to pre-defined answers without custom training.

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Language Understanding (LUIS)

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Translator

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Sentiment Analysis

Question 4

What is 'grounding' in the context of Azure OpenAI and Retrieval-Augmented Generation?

Accepted Answer

Anchoring model responses to specific, retrieved source documents to improve factual accuracy. Grounding in Azure OpenAI and Retrieval-Augmented Generation (RAG) refers to the practice of anchoring the model's responses to specific, retrieved source documents. This ensures that the generated output is factually accurate and verifiable, reducing the risk of hallucination by constraining the model to use only the provided context.

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Connecting the model to electrical ground to prevent static during training

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The process of converting floating-point weights to integer values for deployment

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Setting the baseline performance metrics before model fine-tuning begins

Question 5

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). 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.

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Object detection

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Image classification

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Facial recognition

Question 6

A medical research organization needs to process thousands of clinical trial documents to automatically extract specific medical entities such as diseases, symptoms, medications, and dosages. They want to use a prebuilt Azure AI Language capability that is already trained on medical domain data. Which Azure AI Language feature should they use?

Accepted Answer

Text Analytics for Health. Text Analytics for Health is a prebuilt Azure AI Language capability specifically trained on medical domain data, enabling extraction of entities like diseases, symptoms, medications, and dosages from clinical trial documents without requiring custom model training. It is designed for healthcare and life sciences use cases, making it the correct choice for processing thousands of clinical documents automatically.

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Key Phrase Extraction

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Named Entity Recognition (NER)

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Custom Text Classification

Question 7

What is the Azure AI Custom Vision service's 'compact' domain used for?

Accepted Answer

Producing exportable models optimized for deployment on edge devices with limited compute. The Azure AI Custom Vision service's 'compact' domain is specifically designed to produce models that can be exported to formats like TensorFlow, ONNX, or CoreML for deployment on edge devices with limited compute, memory, and power. This domain trades some accuracy for a smaller model footprint, enabling real-time inference on devices such as cameras, drones, or IoT gateways.

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Training models on a compact (small) dataset with fewer than 50 images

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Creating more compact API responses with less metadata

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Training models that use less storage in Azure blob containers

Question 8

What is 'k-fold cross-validation' specifically and how is k=10 different from k=5?

Accepted Answer

k=10 provides more reliable performance estimates at 2x the compute cost vs k=5. k-fold cross-validation splits the dataset into k equal folds, training on k-1 folds and validating on the remaining fold, repeating this process k times. With k=10, each model is trained on 90% of the data and validated on 10%, while k=5 uses 80% for training and 20% for validation. The key difference is that k=10 yields a performance estimate with lower variance (more reliable) because it averages over more folds, but it requires approximately twice the computational cost (10 training runs vs. 5).

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k=10 always produces a better model than k=5 because it uses more training data

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k=5 and k=10 produce identical results because the total data is the same

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k=10 requires 10 times more labelled data than k=5

Question 9

A data scientist trains a binary classification model to distinguish between images of cats and dogs. On the test set, the model achieves 98% accuracy, but a deeper inspection reveals that the test set contains 95% cats and 5% dogs, and the model predicts 'cat' for every single image. Which metric should the data scientist prioritize to get a more realistic evaluation of the model's performance on this imbalanced dataset?

Accepted Answer

F1-score. The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both when classes are imbalanced. In this scenario, accuracy is misleadingly high (98%) because the model always predicts the majority class (cat), achieving high accuracy without actually learning to distinguish cats from dogs. The F1-score penalizes the model for its poor recall on the minority class (dogs), giving a more realistic evaluation of its performance.

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Precision

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Recall

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Accuracy

Question 10

A museum wants to automatically generate detailed descriptions of artwork for a mobile app. For each painting, the app should produce a natural-language description that includes the dominant colors, the objects present in the scene, and whether the scene is indoor or outdoor. Which Azure Computer Vision capability is best suited for this task?

Accepted Answer

Image Analysis (Describe Image / Dense Captions). Image Analysis with the Describe Image or Dense Captions API is specifically designed to generate human-readable sentences summarizing the content of an image, including dominant colors, objects, and scene attributes like indoor/outdoor. This capability uses pre-trained deep learning models to produce natural-language descriptions, making it the ideal choice for the museum's requirement of detailed, automated artwork descriptions.

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Optical Character Recognition (OCR)

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Face API

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Object Detection

Question 11

What is the context window in a large language model?

Accepted Answer

The maximum amount of text an LLM can process in a single interaction. The context window defines the maximum number of tokens (words, subwords, or characters) that a large language model can accept as input in a single prompt or interaction. This includes both the user's input and any prior conversation history, and it directly limits how much information the model can consider when generating a response.

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The visual display area where AI responses appear in a chat interface

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The number of seconds before a model response times out

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The geographic region where the AI model is hosted

Question 12

A social media company uses an AI system to automatically filter hate speech. After deployment, they discover the system flags posts from a specific ethnic group at a much higher rate than posts from other groups, even when the content is similar. Which Microsoft responsible AI principle is most directly relevant?

Accepted Answer

Fairness. (Fairness) because the AI system is producing biased outcomes by disproportionately flagging posts from a specific ethnic group despite similar content. This directly violates the fairness principle, which requires AI systems to treat all groups equitably and avoid discrimination based on sensitive attributes like ethnicity.

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Reliability and safety

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Privacy and security

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Inclusiveness

Question 13

A data scientist trains a regression model to predict daily electricity consumption (in kWh) for a commercial building. The business team needs a metric that heavily penalizes large prediction errors (outliers) more than small errors. Which metric should the data scientist report to best meet this requirement?

Accepted Answer

Root Mean Squared Error (RMSE). Root Mean Squared Error (RMSE) is the correct metric because it squares the residuals before averaging, which disproportionately amplifies the impact of large errors (outliers) compared to small errors. This aligns directly with the business requirement to heavily penalize large prediction errors in the regression model for daily electricity consumption.

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Mean Absolute Error (MAE)

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R-squared

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Mean Absolute Percentage Error (MAPE)

Question 14

What is 'Microsoft 365 Copilot' and how does it use Azure OpenAI?

Accepted Answer

GPT-4o integrated into Word, Excel, Teams, and Outlook with access to your Microsoft Graph data. Microsoft 365 Copilot is an AI assistant that integrates GPT-4o (a large language model from Azure OpenAI) directly into Microsoft 365 apps like Word, Excel, Teams, and Outlook. It uses Azure OpenAI's generative AI capabilities to process natural language prompts and, critically, combines that with access to your Microsoft Graph data (emails, calendar, documents, etc.) to produce contextually relevant responses. This makes it a generative AI workload that augments productivity by understanding and acting on your personal and organizational data.

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A Microsoft 365 license tier that includes more storage and video conferencing features

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An AI model trained exclusively on Microsoft's internal corporate data

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A Microsoft Teams feature that automatically generates meeting agendas before each call

Question 15

A marketing team wants to use AI to automatically create new product descriptions that are original and varied, simulating human-like writing. Which type of AI model is best suited for this task?

Accepted Answer

Generative model. Generative AI models, such as GPT (Generative Pre-trained Transformer), are specifically designed to create new, original content by learning the underlying patterns and distributions of training data. For the task of generating varied and human-like product descriptions, a generative model can produce novel text that mimics the style and structure of the training examples, unlike discriminative models which only classify or predict labels.

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Discriminative model

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Regression model

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Clustering model

Question 16

What does Azure AI Vision's 'smart crops' feature do?

Accepted Answer

Identifies the most important region for optimal thumbnail cropping at any aspect ratio. Azure AI Vision's smart crops feature uses AI to identify the most important region of an image and then crops it to any specified aspect ratio while keeping that region in focus. This is particularly useful for generating thumbnails that maintain visual context across different display sizes, such as social media previews or responsive web design.

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Identifies agricultural crops in satellite imagery

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Removes unwanted background elements from images

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Detects when an image has been cropped or edited

Question 17

A museum wants to create an interactive exhibit where visitors can take a photo of a painting. The system should then generate a descriptive caption (e.g., 'A woman with a pearl earring') and classify the painting as either a portrait or landscape. Which Azure Computer Vision capability should they use without needing to train a custom model?

Accepted Answer

Image Analysis. Image Analysis in Azure Computer Vision provides pre-built capabilities for extracting rich information from images, including generating human-readable captions (via the 'describe' operation) and classifying images into categories like 'portrait' or 'landscape' without requiring any custom training. This directly matches the museum's need for both caption generation and orientation classification using a pre-trained model.

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Custom Vision

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Face Detection

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Optical Character Recognition (OCR)

Question 18

What is 'model monitoring' in Azure Machine Learning after deployment?

Accepted Answer

Tracking deployed model performance and data drift over time to detect degradation. Model monitoring in Azure Machine Learning refers to the ongoing process of tracking a deployed model's performance metrics (such as accuracy or precision) and detecting data drift (changes in input data distribution) or concept drift (changes in the relationship between inputs and outputs) over time. This is critical because models can degrade in production even if they performed well during training, due to shifts in real-world data. Azure ML provides built-in monitoring capabilities, including drift detection and alerting, to ensure models remain reliable.

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Watching the training loss curve during model training to detect overfitting

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A dashboard showing the compute costs of running model inference in production

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Monitoring the uptime and latency of the model serving endpoint

Question 19

What is 'depth estimation' in computer vision and what are its applications?

Accepted Answer

Inferring the distance of objects from the camera to produce a spatial depth map. Depth estimation is a computer vision technique that infers the distance of objects from the camera, producing a spatial depth map where each pixel represents a distance value. This is commonly achieved using stereo vision (two cameras) or monocular depth estimation (single camera with deep learning models). It is a core feature of Azure Computer Vision's spatial analysis capabilities, enabling applications like augmented reality, autonomous navigation, and 3D scene reconstruction.

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Measuring the depth of colour in an image (number of bits per pixel)

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Analysing how deeply a subject is embedded in a complex background scene

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Determining how much detail is captured in a photograph based on lens quality

Question 20

A library wants to automatically generate descriptive alt text for hundreds of historical photographs in their digital archive. For each photo, the system should produce a natural-language description that includes objects present (e.g., 'a horse', 'a carriage'), the action being performed (e.g., 'pulling'), and the scene type (e.g., 'city street'). Which Azure Computer Vision capability should they use?

Accepted Answer

Image Analysis (Describe image). The Image Analysis 'Describe image' capability is designed to generate human-readable captions that summarize the content of an image, including objects, actions, and scene context. This directly matches the library's requirement to produce natural-language descriptions for historical photographs, as it uses a combination of object detection and scene understanding to output a full sentence.

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Optical Character Recognition (OCR)

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Object detection

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Face detection