AI-900 Practice Test 31 — 15 Questions

Question 1

What is the Azure OpenAI 'content filter' and what categories of content does it cover?

Accepted Answer

Safety filters that detect and block hate speech, sexual, violent, and self-harm content in inputs and outputs. Azure OpenAI's content filter is a safety system that uses multi-level classification models to detect and block harmful content across four categories: hate, sexual, violence, and self-harm. It applies to both user prompts (inputs) and model completions (outputs), ensuring responsible AI usage.

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A feature that limits the length of API responses to control costs

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A spam filter that removes irrelevant or off-topic user messages

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A filter that removes personally identifiable information from model outputs

Question 2

A warehouse uses AI to monitor inventory. They need to detect the presence and location of specific objects (e.g., forklifts, pallets) in real-time video feeds. Which Azure Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection is the correct choice because it identifies specific objects (e.g., forklifts, pallets) within an image or video frame and returns bounding box coordinates indicating their location. This capability is designed for real-time spatial awareness, which directly matches the warehouse's need to detect both the presence and position of objects in video feeds.

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Image classification

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OCR (optical character recognition)

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Facial recognition

Question 3

What does Azure AI Vision's 'people detection' (spatial analysis) feature track?

Accepted Answer

Counting, tracking movement, and measuring occupancy of people in defined zones from video. Azure AI Vision's spatial analysis (people detection) tracks the movement of people in video feeds, counting individuals and measuring how long they stay in defined zones. It does not identify specific people, detect masks or safety equipment, or measure body dimensions. This feature is designed for occupancy monitoring and flow analysis in physical spaces.

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Identifying the names of specific people in video footage

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Detecting whether people are wearing masks or safety equipment

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Measuring individual people's heights and body dimensions

Question 4

A news agency wants to automatically extract the names of people and organizations mentioned in thousands of articles. They plan to use a prebuilt Azure AI Language feature that requires no custom training. Which feature should they use?

Accepted Answer

Named entity recognition. Named entity recognition (NER) is the correct feature because it is specifically designed to identify and categorize entities such as people, organizations, locations, and dates from unstructured text. The news agency's requirement to extract names of people and organizations from articles matches NER's prebuilt capability, which requires no custom training and is available in Azure AI Language.

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Sentiment analysis

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Key phrase extraction

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Language detection

Question 5

What is 'AI in education' and how are Azure AI services applied to learning?

Accepted Answer

Personalised learning, pronunciation assessment, automated grading, tutoring chatbots, and accessibility tools. It accurately describes the application of Azure AI services to education, including personalized learning via Azure Machine Learning, pronunciation assessment with Azure Speech Services, automated grading using Azure Cognitive Services, tutoring chatbots built with Azure Bot Service, and accessibility tools leveraging Azure Cognitive Services like Computer Vision and Text-to-Speech. These services enhance learning without replacing human educators.

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Using AI to replace teachers in classrooms with fully automated instruction

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Generating educational certificates automatically when students complete online courses

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Using AI to monitor student attention levels during online classes via webcam

Question 6

What is 'invoice analysis' in Azure AI Document Intelligence?

Accepted Answer

Extracting vendor, customer, line items, dates, and totals from vendor invoice images. Invoice analysis in Azure AI Document Intelligence is a prebuilt model specifically designed to extract structured data from vendor invoices. It uses optical character recognition (OCR) and deep learning to identify and extract key fields such as vendor name, customer name, line items, invoice date, due date, and totals. This enables automated data entry and downstream processing without manual effort.

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Analysing invoice data to predict future payment defaults by customers

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Generating invoices from pricing data stored in a database

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Comparing invoice totals against purchase orders to detect discrepancies

Question 7

What does 'deep learning' refer to in machine learning?

Accepted Answer

Machine learning using neural networks with many layers to learn hierarchical representations. Deep learning is a subset of machine learning that uses neural networks with multiple layers (deep neural networks) to automatically learn hierarchical representations of data. Each layer extracts increasingly abstract features, enabling the model to capture complex patterns without manual feature engineering. This is why option B is correct.

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Machine learning that requires an internet connection to function

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A technique for training models on extremely large datasets only

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Machine learning that digs deeply into structured databases

Question 8

What is the purpose of 'image moderation' using Azure AI Content Safety?

Accepted Answer

Detecting and categorizing harmful content in images (sexual, violent, hate) for automatic content filtering. Azure AI Content Safety's image moderation is designed to detect and categorize harmful content such as sexual, violent, and hate-related material within images. This enables automatic content filtering to ensure compliance with safety policies, which is a core computer vision workload for content moderation.

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Adjusting image brightness and contrast for better display quality

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Verifying that images meet minimum quality standards for AI training

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Compressing images to reduce bandwidth during content delivery

Question 9

What is Azure AI Speech's real-time speech recognition feature used for?

Accepted Answer

Converting live spoken audio into text with low latency. Azure AI Speech's real-time speech recognition feature is designed to convert live spoken audio into text with low latency, enabling applications like live captioning, voice commands, and transcription during meetings. It uses streaming APIs (e.g., the Speech SDK's RecognizeOnceAsync or StartContinuousRecognitionAsync) to process audio chunks as they arrive, returning partial and final results with minimal delay.

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Generating spoken audio from written text in real time

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Translating real-time audio between languages

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Identifying who is speaking from their voice

Question 10

A game development company uses Azure OpenAI Service to automatically generate in-game dialog for non-player characters (NPCs) based on character profiles. They need to ensure the generated text does not contain offensive language or harmful suggestions. Which Azure OpenAI Service feature should they configure to prevent this?

Accepted Answer

Content filters. Content filters in Azure OpenAI Service allow you to define categories of harmful content (e.g., hate, violence, self-harm) and set severity thresholds. When generating NPC dialog, the service automatically evaluates each output against these filters and blocks or flags any text that violates the configured policies, ensuring offensive language or harmful suggestions are prevented.

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Model deployment

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Token limit

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Prompt engineering

Question 11

What is the role of a label (also called target or ground truth) in supervised machine learning?

Accepted Answer

The correct output or answer associated with each training example that the model learns to predict. In supervised machine learning, the label (also called target or ground truth) is the known correct output for each training example. The model uses these labels during training to learn the mapping from input features to outputs, enabling it to make accurate predictions on new, unseen data. This is fundamental to supervised learning, where the algorithm minimizes the error between its predictions and the ground truth labels.

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A category of input features used by the model

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A text description attached to a model explaining what it does

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A tag applied to Azure ML resources for organization

Question 12

What is 'fraud detection' as an AI workload and what type of ML technique does it typically use?

Accepted Answer

Using anomaly detection and classification models to identify fraudulent transactions in real time. Fraud detection is an AI workload that identifies suspicious or anomalous patterns in transaction data to flag potential fraud. It typically uses anomaly detection (to spot outliers deviating from normal behavior) and classification models (e.g., logistic regression, random forest, or neural networks) to label transactions as legitimate or fraudulent in real time, enabling rapid intervention.

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Generating synthetic fraudulent data to train security awareness training content

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Verifying digital signatures on financial documents to confirm their authenticity

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Encrypting financial data to prevent fraudsters from intercepting it

Question 13

A healthcare organization deploys an AI system that analyzes patient genetic data to recommend personalized treatments. To ensure patient data is protected from unauthorized access during use, which Microsoft responsible AI principle is most directly relevant?

Accepted Answer

Privacy and security. The scenario explicitly focuses on protecting patient genetic data from unauthorized access, which directly falls under the Privacy and security principle. This principle ensures that data is encrypted, access-controlled, and compliant with regulations like HIPAA or GDPR, preventing breaches during AI model inference or storage.

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Fairness

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Reliability and safety

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Transparency

Question 14

A developer is using Azure OpenAI Service to generate Python code snippets. They notice that the generated code often contains repetitive function definitions and loops. Which parameter should be increased to reduce this repetition?

Accepted Answer

Frequency penalty. The frequency penalty parameter reduces repetition by penalizing tokens that have already appeared in the generated text, making the model less likely to reuse the same functions or loops. Increasing this value directly discourages the model from generating repetitive patterns, which is exactly the issue described.

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Temperature

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Max tokens

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Top P

Question 15

A data science team trains several machine learning models for a regression task. They observe that Model A has low training error and low test error. Model B has low training error but high test error. Model C has high training error and high test error. Which model would most likely benefit from an ensemble technique that averages the predictions of multiple models?

Accepted Answer

Model B (low training error, high test error). Model B exhibits low training error but high test error, which is a classic sign of overfitting. Ensemble techniques like averaging predictions from multiple models reduce variance and improve generalization, making them most beneficial for overfit models. In Azure Machine Learning, you can use an ensemble pipeline or AutoML's VotingEnsemble to combine diverse models and lower test error.

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Model A (low training error, low test error)

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Model C (high training error, high test error)

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None of the models would benefit from an ensemble technique