AI-102 Practice Test 22 — 15 Questions

Question 1

A developer is using Azure Cognitive Service for Language to perform sentiment analysis on customer reviews. The service returns sentiment labels (positive, negative, neutral) and confidence scores. For a particular review, the service returns 'positive' with a confidence score of 0.55. The developer wants to ensure that only high-confidence results are used. What should the developer do?

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

Accepted Answer

Configure a minimum confidence threshold of 0.75 in the application logic.. The developer must implement a confidence threshold in the application logic to filter out low-confidence results. The Azure Cognitive Service for Language returns confidence scores between 0 and 1 for each sentiment label, and the developer can set a minimum threshold (e.g., 0.75) to ensure only high-confidence predictions are used. This approach does not require retraining the model or modifying the input text, as the threshold is applied post-inference in the client code.

Answer

Use the Text Analytics for Health API instead.

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Retrain the sentiment analysis model with additional labeled data.

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Adjust the input text by removing ambiguous phrases.

Question 2

A healthcare organization uses Azure AI Language to analyze clinical notes. They need to detect protected health information (PHI) such as patient names and dates of birth, and also identify medical conditions. Which Azure AI Language feature should they use?

Accepted Answer

Health Text Analytics (preview) in Azure AI Language. Health Text Analytics (preview) in Azure AI Language is the correct feature because it is specifically designed to extract and structure medical information from unstructured clinical text, including protected health information (PHI) such as patient names and dates of birth, as well as medical conditions. This preview API extends the Text Analytics for health capabilities with enhanced PHI detection and entity linking, making it the appropriate choice for healthcare compliance and clinical analysis.

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Conversation Analysis

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Custom Text Classification

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Text Analytics for health (general availability)

Question 3

Refer to the exhibit. You are implementing an Azure AI Search index for semantic search with vector support. The index includes a field 'descriptionVector' of type Collection(Edm.Single) with 1536 dimensions. When you run a vector search query, you notice that results are not sorted by relevance. What is the most likely cause?

Accepted Answer

The index does not have a vector configuration with a similarity metric. Azure AI Search requires a vector configuration with a similarity metric (e.g., cosine, dotProduct, euclidean) to compute relevance scores for vector search results. Without this configuration, the search engine cannot sort results by relevance, leading to unsorted or default ordering. The similarity metric is defined in the index's vector configuration and is essential for ranking vector query results.

Answer

The 'descriptionVector' field is not searchable

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The vector dimensions do not match the embedding model output

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The 'descriptionVector' field is set as retrievable: false

Question 4

You are developing a solution to detect and redact personally identifiable information (PII) in images of documents. Which THREE Azure services can you use together?

Accepted Answer

Azure AI Language (PII detection). Options B, C, and D are correct because Azure AI Document Intelligence and Azure AI Vision (Read API) can extract text from images of documents, and Azure AI Language (PII detection) can identify PII in the extracted text, enabling redaction. Option A (Custom Vision) is incorrect because it is designed for image classification and object detection, not PII detection. Option E (Video Indexer) is incorrect because it is for analyzing videos, not static images.

Answer

Azure Custom Vision

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Azure Video Indexer

Question 5

A developer wants to integrate a pre-built AI model that can extract key information from invoices, such as vendor name, invoice date, and total amount. Which Azure AI service should they use?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is the correct service because it is specifically designed to extract structured data from documents like invoices, including fields such as vendor name, invoice date, and total amount. It uses pre-built models trained on common document types, making it ideal for this use case without requiring custom training.

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Azure AI Language

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Azure OpenAI Service

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Azure Cognitive Search

Question 6

A company is building a computer vision solution using Azure AI Vision to analyze images of retail shelves. The solution must detect product presence and read expiration dates. Which TWO Azure AI Vision features should be used?

Accepted Answer

Object detection. Object detection (C) is correct because it identifies and locates specific products on retail shelves by drawing bounding boxes around detected items, which directly addresses the requirement to detect product presence. Optical Character Recognition (OCR) (D) is correct because it extracts printed or handwritten text from images, enabling the reading of expiration dates on product packaging. Together, these two features fulfill both core requirements of the solution.

Answer

Face detection

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Brand detection

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Image captioning

Question 7

A company wants to generate product descriptions for thousands of items using an Azure OpenAI GPT-4 model. They need to ensure the descriptions match a consistent brand voice. Which approach is most efficient and cost-effective?

Accepted Answer

Set a system message with brand voice guidelines and use few-shot examples. Setting a system message with brand voice guidelines and providing few-shot examples allows the GPT-4 model to consistently apply the desired tone and style across all product descriptions without retraining. This approach is efficient and cost-effective as it avoids the high compute and data preparation costs of fine-tuning, while still enabling precise control over output through in-context learning.

Answer

Write a separate prompt for each product category

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Use Azure OpenAI on your data with a vector database of brand guidelines

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Fine-tune a base model on existing product descriptions

Question 8

Your company uses Microsoft Copilot for Microsoft 365. You need to ensure that Copilot only accesses data from approved SharePoint sites and does not use any other organizational data. What should you configure?

Accepted Answer

Apply sensitivity labels to the approved SharePoint sites.. Sensitivity labels can be configured to restrict Microsoft Copilot for Microsoft 365 to only access content from approved SharePoint sites. By applying a sensitivity label that includes the 'mark content' or 'encrypt content' setting with a specific scope, you can use the 'Microsoft Copilot for Microsoft 365' condition under 'Access control' to block Copilot from processing data from unlabeled or non-approved sites. This ensures Copilot respects the label's policy and excludes all other organizational data.

Answer

Configure Conditional Access policies in Microsoft Entra ID.

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Use Microsoft Purview Data Map to catalog the approved sites.

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Set up data retention policies in Microsoft Purview.

Question 9

Your knowledge mining solution ingests documents from multiple tenants. Each tenant's data must be isolated and searchable only by that tenant. You have a single Azure AI Search service. How should you implement multi-tenancy?

Accepted Answer

Use a single index with a tenant ID field and filter queries by that field. Azure AI Search supports multi-tenancy within a single service by using a shared index with a tenant ID field. Each document is tagged with a tenant identifier, and queries are scoped using OData `$filter` expressions (e.g., `$filter=tenantId eq 'tenant123'`). This ensures data isolation while keeping costs low and management simple, as only one search service and one index are needed.

Answer

Use separate skillsets for each tenant

Answer

Create a separate search service for each tenant

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Use separate data sources within the same index

Question 10

You are designing an Azure AI solution that uses Language Understanding (LUIS) for intent detection. The solution must handle multiple languages dynamically based on the user's locale. What should you do?

Accepted Answer

Create separate LUIS applications for each language and route based on locale.. LUIS does not natively support multi-language within a single application; each LUIS app is designed for a single language. To handle multiple languages dynamically, you must create separate LUIS applications for each language and route user utterances based on the detected locale, ensuring accurate intent and entity recognition per language.

Answer

Use Azure Translator to translate user input to English before sending to LUIS.

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Train a single LUIS app with utterances in all languages.

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Enable the 'Multi-Language' feature in the LUIS app.

Question 11

You are deploying a generative AI model using Azure AI Foundry. The model must be accessible only from within a specific virtual network. Additionally, you need to monitor all API calls for auditing. Which two configurations are required? (Choose two.)

Accepted Answer

Enable diagnostic settings to send logs to a Log Analytics workspace.. Enabling diagnostic settings to send logs to a Log Analytics workspace allows you to capture and audit all API calls made to the model deployment. This is essential for monitoring, security auditing, and compliance, as it records detailed telemetry such as request timestamps, caller IPs, and operation names. Option E is correct because disabling public network access and configuring a private endpoint ensures that the model is only accessible from within the specified virtual network, meeting the isolation requirement.

Answer

Assign a managed identity to the model deployment.

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Configure CORS to allow only the VNet's domain.

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Enable public network access from selected IP addresses.

Question 12

You need to generate realistic synthetic data using Azure OpenAI Service to train a machine learning model. The data must be diverse and cover edge cases. Which approach should you use?

Accepted Answer

Use prompt engineering with detailed instructions to generate varied examples.. Prompt engineering with detailed instructions allows you to explicitly control the diversity, structure, and edge-case coverage of generated synthetic data without requiring a pre-existing dataset. By crafting system messages and user prompts that specify variations in attributes, formats, and boundary conditions, you can produce a wide range of realistic examples that mimic real-world distributions, which is essential for training robust machine learning models.

Answer

Fine-tune the model on a small dataset of real examples.

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Use Azure OpenAI embeddings to generate similar data points.

Answer

Set a high temperature parameter only.

Question 13

You are deploying a custom question-answering solution using Azure AI Language. The solution must support multiple languages and provide answers from a set of FAQ documents. You need to ensure that the service can handle up to 1000 requests per minute with low latency. What should you configure?

Accepted Answer

Use the Standard tier and allocate 10 capacity units (CUs).. Azure AI Language's custom question-answering feature uses a Standard tier with capacity units (CUs) to handle throughput and latency. Each CU provides a baseline of 100 requests per minute (RPM), so 10 CUs deliver exactly 1000 RPM, meeting the requirement. The Standard tier also supports multiple languages and low-latency responses from FAQ documents.

Answer

Use Azure AI Search with a basic tier and replicas.

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Use the Free tier and enable autoscaling.

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Deploy a dedicated endpoint with provisioned throughput of 1000 TPS.

Question 14

You are deploying an Azure AI Search solution that indexes customer support tickets. The solution must support fuzzy search for misspelled keywords and boost results from premium customers. Which two features should you configure?

Accepted Answer

Scoring profiles. Scoring profiles allow you to boost search results based on specific criteria, such as customer tier, by assigning higher weights to certain fields or adding functions that increase scores for premium customers. This directly supports the requirement to prioritize results from premium customers.

Answer

Synonym maps

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Suggesters

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Custom analyzers

Question 15

You have defined the custom WebApiSkill shown in the exhibit. The skill calls an Azure Function that can process up to 10 documents per second. However, you notice that the skill is failing with 429 errors. What is the most likely cause?

Accepted Answer

The degreeOfParallelism of 3 causes too many concurrent requests, exceeding the function's capacity. The `degreeOfParallelism` of 3 causes the AI Search enrichment pipeline to invoke the Azure Function with up to 3 concurrent batches, each of size 5, resulting in up to 15 documents per second. Since the function can only handle 10 documents per second, this exceeds its capacity and triggers HTTP 429 (Too Many Requests) errors.

Answer

The timeout of 30 seconds is too short for the function to respond

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The batch size of 5 is too large, causing the function to receive too many documents at once

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The context '/document' is incorrect, causing all documents to be processed as one