AI-102 Plan and manage an Azure AI solution Practice Test 12 — 15 Questions

Question 1

A team is building a chatbot using Azure Bot Service and Language Understanding (LUIS). The chatbot must handle multiple languages. What should you configure?

Accepted Answer

Create separate LUIS applications for each language. LUIS does not natively support multilingual models within a single app. Each LUIS application is designed for a single language, so to handle multiple languages, you must create separate LUIS apps—one per language—and route user utterances to the appropriate app based on the detected language. This ensures accurate intent and entity recognition tailored to each language's linguistic patterns.

Answer

Configure a single LUIS app with multilingual utterances

Answer

Use Azure AI Search to index translated content

Answer

Use Azure Translator to translate utterances before calling LUIS

Question 2

You are responsible for an Azure AI solution that uses Custom Vision to classify manufacturing defects. The model must achieve high recall to avoid missing defects. The current model has high precision but low recall. Which action should you take?

Accepted Answer

Lower the probability threshold for the defect class. Lowering the probability threshold for the defect class means the model will classify an image as defective even when its confidence score is lower. This increases the number of true positives (defects caught), directly improving recall at the cost of potentially more false positives. In Custom Vision, the default probability threshold is 50%, and adjusting it downward is the standard technique to prioritize recall over precision.

Answer

Add more images of non-defective items to the training set

Answer

Increase the number of training iterations

Answer

Increase the probability threshold for the defect class

Question 3

Your organization wants to implement a document processing pipeline that extracts text from scanned PDFs and identifies named entities. Which two Azure AI services should you use?

Accepted Answer

Azure AI Language. Azure AI Document Intelligence (formerly Form Recognizer) is used to extract text from scanned PDFs via OCR, while Azure AI Language provides pre-built named entity recognition (NER) to identify entities like people, organizations, and locations. Together, they form a complete pipeline: Document Intelligence handles the image-to-text conversion, and Language processes the extracted text for entity extraction.

Answer

Azure AI Custom Vision

Answer

Azure AI Translator

Answer

Azure AI Speech

Question 4

You deploy a Custom Vision object detection model to classify vehicles. The model works well in good lighting but fails in low-light conditions. What is the most appropriate action?

Accepted Answer

Add images with different lighting conditions to the training set. The core issue is a data distribution mismatch: the model was trained primarily on well-lit images and lacks exposure to low-light examples. Adding images with diverse lighting conditions directly addresses this by enriching the training dataset, enabling the model to learn robust features for low-light scenarios. This aligns with the fundamental principle that Custom Vision models are only as good as the training data they receive.

Answer

Increase the probability threshold

Answer

Increase the number of training iterations

Answer

Use a domain-specific model for vehicles

Question 5

Your chatbot uses Azure Bot Service and QnA Maker. Users can ask questions in natural language, and the bot returns answers from a knowledge base. Users report that the bot sometimes returns irrelevant answers. What should you do first?

Accepted Answer

Review and edit the QnA pairs to add alternative phrasings. The core issue is that the bot returns irrelevant answers because the QnA Maker knowledge base lacks sufficient alternative phrasings to match the variety of user questions. By reviewing and editing QnA pairs to add alternative phrasings, you directly improve the synonym and paraphrase coverage, which increases the confidence score for correct matches and reduces irrelevant responses. This is the first and most fundamental troubleshooting step before considering more complex integrations.

Answer

Create multiple QnA Maker knowledge bases for different topics

Answer

Integrate LUIS to detect user intent

Answer

Use Azure AI Search to index the knowledge base

Question 6

You manage an Azure AI Search service that indexes legal documents. The search latency is high, and you need to improve query performance without reducing index size. Which action should you take?

Accepted Answer

Increase the number of replicas. Increasing the number of replicas distributes query load across multiple copies of the index, which directly improves query throughput and reduces latency. Replicas are designed for scaling query operations without changing the index size or storage capacity.

Answer

Upgrade to a higher pricing tier

Answer

Increase the number of partitions

Answer

Reduce the number of searchable fields

Question 7

Your company uses Azure OpenAI to generate code snippets. Developers need to ensure that the generated code does not contain security vulnerabilities. What should you implement?

Accepted Answer

Integrate a static code analysis tool into the CI/CD pipeline to scan generated code. Integrating a static code analysis tool (e.g., Microsoft Defender for DevOps, SonarQube, or Checkmarx) into the CI/CD pipeline allows automated scanning of generated code for security vulnerabilities before deployment. This approach directly addresses the requirement to ensure generated code is free of vulnerabilities, as Azure OpenAI content filters are not designed to detect code-level security flaws like SQL injection or buffer overflows.

Answer

Set usage quotas to limit the number of code generation requests

Answer

Fine-tune the model on a dataset of secure code examples

Answer

Configure Azure OpenAI content filters to block vulnerable code

Question 8

Your team develops a document translation solution using Azure AI Translator. The solution must translate documents while preserving formatting and layout. Which feature should you use?

Accepted Answer

Azure AI Translator Document Translation. Azure AI Translator Document Translation is specifically designed to translate entire documents while preserving the original formatting, structure, and layout. Unlike Text Translation, which handles only plain text strings, Document Translation processes files (e.g., PDF, Word, Excel) and returns a translated version with the same formatting, making it the correct choice for this requirement.

Answer

Azure AI Translator Custom Translator

Answer

Azure AI Document Intelligence

Answer

Azure AI Translator Text Translation

Question 9

Your company uses Azure OpenAI Service to generate marketing content. You need to ensure that the generated content does not contain offensive language. Which feature should you enable?

Accepted Answer

Azure AI Content Safety filters.. Azure AI Content Safety filters are specifically designed to detect and block offensive, inappropriate, or harmful language in text and images. By enabling these filters on your Azure OpenAI Service deployment, you can configure severity thresholds for categories like hate, self-harm, sexual, and violence content, ensuring generated marketing content meets safety policies.

Answer

Audit logging for all API calls.

Answer

Data encryption at rest.

Answer

Rate limiting on the endpoint.

Question 10

Which TWO monitoring metrics should you track to ensure the health and performance of an Azure AI Search service used for a customer-facing product catalog?

Accepted Answer

Throttled search queries count.. Option A, throttled search queries count, is correct because throttling directly indicates that the service is hitting its query-per-second (QPS) capacity limits, which degrades the customer-facing catalog experience and signals a need to scale replicas or partitions. Option D, search latency (average and P99), is correct because latency is the primary performance indicator for a user-facing search experience; tracking both average and P99 reveals tail-latency problems that average alone would hide. Option B is not among the correct answers because indexer execution history and duration relates to data ingestion pipelines, not the query-time health of a customer-facing catalog. Option C is not correct because storage used in GB is a capacity metric that does not reflect query performance or service health for end users. Option E is not correct because counting only successful search requests gives no insight into failures, throttling, or latency, and a rising success count can mask underlying performance degradation.

Answer

Indexer execution history and duration.

Answer

Storage used in GB.

Answer

Number of successful search requests.

Question 11

Your organization uses Azure AI Document Intelligence to extract data from invoices. The solution must identify custom fields not present in the prebuilt models, such as 'purchase order number' located in varying positions across documents. What should you do?

Accepted Answer

Train a custom extraction model using labeled sample invoices.. Azure AI Document Intelligence (formerly Form Recognizer) allows you to train a custom extraction model using labeled sample invoices. This approach enables the model to learn custom fields like 'purchase order number' that appear in varying positions, which prebuilt models cannot handle. By providing labeled examples, the model generalizes to extract the field accurately from new documents.

Answer

Use the layout model and apply manual post-processing.

Answer

Use Azure AI Forms Recognizer with prebuilt receipt model.

Answer

Use the prebuilt invoice model with field merging.

Question 12

Which THREE practices should be followed to secure an Azure AI solution that uses Azure OpenAI Service and Azure AI Search?

Accepted Answer

Use managed identities to authenticate between Azure OpenAI and Azure AI Search.. Option B is correct because managed identities let Azure OpenAI and Azure AI Search authenticate to each other through Microsoft Entra ID without embedding secrets or connection strings in code, eliminating credential leakage and rotation overhead. Option D is correct because requiring HTTPS with TLS 1.2 or higher protects data in transit between client applications and the AI services, preventing interception or downgrade attacks on prompts, responses, and search queries. Option E is correct because enabling firewall rules and private endpoints on the AI service endpoints removes public internet exposure and restricts traffic to approved virtual networks, which is a core network-isolation control for Azure AI workloads. Option A is not appropriate because using API keys directly in application code exposes secrets in source control, logs, and memory even if the keys are originally stored in Azure Key Vault. Option C is not appropriate because placing AI services in a DMZ subnet with public IP addresses increases the attack surface rather than securing the solution; private endpoints and restricted access are preferred.

Answer

Store API keys in Azure Key Vault but use them directly in application code.

Answer

Place all AI services in a DMZ subnet with public IP addresses.

Question 13

You need to restrict access to an Azure AI Language resource so that only a specific virtual network can call the endpoint. Which configuration should you use?

Accepted Answer

Enable a service endpoint or private endpoint for the resource. Azure AI Language resources can be isolated to a specific virtual network by enabling either a service endpoint (via the Microsoft.CognitiveServices service tag) or a private endpoint (using Azure Private Link). This configuration ensures that only traffic originating from the designated VNet can reach the resource's endpoint, effectively blocking all public internet access. Service endpoints provide a direct, optimized route from the VNet to the resource, while private endpoints assign a private IP from the VNet to the resource, making it accessible only within the VNet.

Answer

Rotate the shared access keys

Answer

Assign a managed identity to the resource

Answer

Configure an IP firewall rule with the VNet's public IP range

Question 14

Your organization is using Azure AI Search with semantic ranking. Users report that search results are not showing relevant documents at the top. You need to improve relevance. What should you configure?

Accepted Answer

Enable semantic search configuration on the index. Semantic search configuration is required to enable semantic ranking, which uses deep learning models to re-rank search results based on contextual relevance rather than just keyword matching. Without this configuration, the index cannot leverage semantic ranking even if the service tier supports it, so enabling it directly addresses the user's complaint about irrelevant documents appearing at the top.

Answer

Add synonyms to the index

Answer

Define a custom scoring profile

Answer

Change the index analyzer to a different language analyzer

Question 15

You are responsible for an Azure AI multi-agent system built on Microsoft Foundry. The system experiences frequent timeout errors when agents call external APIs. You need to implement a resilient pattern. What should you do?

Accepted Answer

Implement retry logic with exponential backoff in the agent tool definitions. Implementing retry logic with exponential backoff in agent tool definitions is a standard resilience pattern for transient failures when calling external APIs. This approach, often using the Retry-After header or a custom backoff strategy, reduces load on the API and prevents cascading timeouts in a multi-agent system built on Microsoft Foundry. It aligns with the recommended practices for building robust AI solutions that depend on external services.

Answer

Disable retry attempts to avoid duplicate requests

Answer

Increase the global timeout for all agents

Answer

Switch to synchronous agent calls