AI-900 Practice Test 31 — 15 Questions

Question 1

A bank uses an AI system to approve loan applications. The bank wants to ensure that applicants can understand why a loan was approved or rejected. Which Microsoft responsible AI principle is most directly relevant to this requirement?

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

Accepted Answer

Transparency. The requirement that applicants can understand why a loan was approved or rejected directly aligns with the Transparency principle, which mandates that AI systems be interpretable and that decisions be explainable to users. In this context, the bank must provide clear reasoning for loan outcomes, enabling applicants to comprehend the factors influencing the decision, such as credit score thresholds or income verification rules.

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Fairness

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Inclusiveness

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Reliability and Safety

Question 2

What is prompt injection and why is it a security concern for AI systems?

Accepted Answer

A security attack where malicious inputs try to override AI system instructions or bypass guardrails. Prompt injection is a security attack where a malicious user crafts input that overrides the AI system's original instructions or bypasses its guardrails, causing the model to behave in unintended ways. This is a critical security concern because it can lead to data exfiltration, unauthorized actions, or the generation of harmful content, undermining the trust and safety of AI deployments.

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A technique for making AI prompts shorter and more efficient

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The process of adding examples to prompts to improve model performance

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A method of injecting training data into a model after deployment

Question 3

What is transfer learning and how does it apply to NLP models?

Accepted Answer

Using knowledge from a large pre-trained model as the starting point for learning a new, related task. Transfer learning in NLP involves taking a large pre-trained model (like BERT or GPT) that has been trained on a massive corpus and fine-tuning it on a smaller, task-specific dataset. This approach dramatically reduces the amount of labeled data and training time needed, while leveraging the linguistic knowledge already captured by the base model. In Azure, services like Azure Cognitive Service for Language use transfer learning to provide high-accuracy custom models with minimal training data.

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Moving a trained model from one Azure region to another for deployment

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Transferring labeled training data between different ML projects

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Automatically translating ML models from Python to other programming languages

Question 4

What is 'text-to-speech' (TTS) in Azure AI Speech?

Accepted Answer

Converting written text into synthesised spoken audio. Text-to-speech (TTS) in Azure AI Speech converts written text into natural-sounding synthesized spoken audio. It uses deep neural networks to generate human-like speech from input text, enabling applications like voice assistants and audiobook narration.

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Extracting text from speech audio recordings

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Translating spoken text from one language to another in real time

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Detecting the emotional tone of speech audio to classify speaker sentiment

Question 5

What is the purpose of Azure AI Speech's speaker recognition feature?

Accepted Answer

To identify who is speaking based on their unique voice characteristics. Azure AI Speech's speaker recognition feature is designed to identify and verify individuals based on their unique vocal characteristics, such as pitch, tone, and speech patterns. This is achieved through voice biometrics, where the service creates a unique voiceprint for each speaker and matches it against enrolled profiles. Option B correctly captures this purpose, distinguishing it from transcription or audio processing tasks.

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To transcribe spoken audio into text

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To detect whether audio contains speech or background noise

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To improve audio quality by removing background noise

Question 6

A social media company uses Azure OpenAI Service to automatically generate captions for user-uploaded images. The company has a strict content policy that prohibits any generated captions containing profanity, hate speech, or self-harm references. Which feature of the Azure OpenAI Service should the company configure to automatically block such harmful content?

Accepted Answer

Content filtering. Content filtering is the correct feature because it is specifically designed to detect and block harmful content such as profanity, hate speech, and self-harm references in both input prompts and generated outputs. Azure OpenAI Service's content filtering system uses multi-class classification models to enforce responsible AI policies automatically, without requiring custom training or manual moderation.

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Temperature parameter

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Top-p parameter

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Max-tokens parameter

Question 7

What is 'Azure AI Vision's landmark detection' and what does it return?

Accepted Answer

Identifying well-known physical landmarks (Eiffel Tower, Big Ben) in photographs with a confidence score. Azure AI Vision's landmark detection is a pre-built computer vision capability that identifies well-known physical landmarks (e.g., Eiffel Tower, Big Ben) in images. It returns the landmark name along with a confidence score indicating the likelihood of the match, enabling applications like automated photo tagging or travel content enrichment.

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Detecting important milestones in a project timeline using AI

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Creating highlighted markers on maps showing user-defined points of interest

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Detecting major architectural features of any building regardless of whether it is famous

Question 8

What is 'batch document translation' in Azure AI Translator and what file formats does it support?

Accepted Answer

Asynchronous translation of Word, PDF, Excel, and HTML documents preserving their layout. Azure AI Translator's batch document translation is an asynchronous operation that translates entire documents (such as Word, PDF, Excel, and HTML) while preserving their original layout and structure. This is distinct from real-time or synchronous translation, as batch translation processes files in bulk via a job-based API, making it ideal for large-scale or non-interactive scenarios.

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Translating database records in batches by sending SQL queries to the translation API

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Translating phone call transcripts in batches after calls are completed

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A real-time API that translates one document page at a time as users scroll

Question 9

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 10

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 11

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 12

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 13

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 14

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 15

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