AI-900 Practice Test 32 — 15 Questions

Question 1

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 2

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 3

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 4

What is 'conversational language understanding' (CLU) in Azure AI Language?

Accepted Answer

A model that extracts user intent and entities from conversational text to drive chatbot logic. Conversational language understanding (CLU) is a feature of Azure AI Language that enables you to build custom models to extract user intents (e.g., 'BookFlight') and entities (e.g., 'destination city') from natural language utterances. This extracted information drives the logic of a chatbot or virtual assistant, allowing it to determine what action to take. Option B correctly describes this core purpose.

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A chatbot that understands multiple languages and auto-translates responses

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A service that generates conversation transcripts from audio recordings

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A tool for analysing the sentiment of customer conversations in real time

Question 5

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 6

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 7

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 8

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 9

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

Question 10

What is 'predictive maintenance' as an AI workload?

Accepted Answer

Using AI to predict equipment failures before they occur, enabling timely maintenance. Predictive maintenance uses AI (typically machine learning models trained on historical sensor data, failure logs, and operational parameters) to forecast when equipment is likely to fail. By identifying patterns and anomalies that precede breakdowns, it enables proactive intervention—reducing unplanned downtime and maintenance costs. This is a classic AI workload because it relies on predictive analytics rather than fixed schedules or reactive fixes.

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Scheduling regular maintenance based on a fixed calendar without using any AI

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Maintaining an AI model's accuracy by regularly retraining on new data

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Using AI to automatically fix bugs in software systems without human intervention

Question 11

A global social media platform wants to automatically detect the language of user posts to route them to appropriate content moderators. The posts are short and often contain mixed scripts. Which Azure AI Language feature should they use?

Accepted Answer

Language detection. Language detection is the correct Azure AI Language feature because it is specifically designed to identify the primary language of text, including short and mixed-script content. This allows the platform to automatically route posts to moderators who speak the detected language, directly addressing the requirement.

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Sentiment analysis

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Key phrase extraction

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Entity recognition

Question 12

What is the purpose of Azure AI Translator?

Accepted Answer

To translate text between 100+ languages using neural machine translation. Azure AI Translator is a cloud-based neural machine translation service that translates text between over 100 languages and dialects. It uses deep learning models to produce high-quality, context-aware translations, making option B the correct answer.

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To convert spoken audio from one language to written text in another language

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To detect the sentiment of multilingual text

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To extract structured data from multilingual documents

Question 13

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 14

What is 'speech synthesis markup language' (SSML) used for in Azure AI Speech?

Accepted Answer

An XML markup language for controlling TTS voice characteristics like pitch, rate, pauses, and pronunciation. SSML is an XML-based markup language that allows you to fine-tune text-to-speech (TTS) output by controlling prosodic elements such as pitch, speaking rate, volume, and pronunciation. It also supports inserting pauses, specifying phonetic pronunciations, and adjusting emphasis, making it essential for generating natural-sounding speech in Azure AI Speech.

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A programming language for writing speech recognition algorithms

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A system for transcribing speech in real time to a database

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A security protocol for encrypting speech API calls

Question 15

What is 'supply chain optimisation' as an AI workload?

Accepted Answer

Using AI for demand forecasting, route optimisation, and inventory management across the supply chain. Supply chain optimisation as an AI workload involves using machine learning models to analyse historical data and real-time variables for demand forecasting, route optimisation, and inventory management. This reduces costs, improves delivery times, and minimises waste by dynamically adjusting to changes in supply and demand.

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Using AI to write optimised supplier contracts with better negotiation terms

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Automating supplier onboarding by extracting information from registration documents

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Monitoring supply chain staff performance using AI-powered productivity tracking