AI-102 Practice Test 4 — 25 Questions

Question 1

A company uses this skillset in an Azure AI Search enrichment pipeline. They notice that the enrichment pipeline fails when processing a document larger than 5000 characters. What is the most likely cause?

Accepted Answer

The output field mapping is missing or incorrect. The enrichment pipeline fails because the output field mapping is missing or incorrect. When a skillset processes documents, the output of each skill must be explicitly mapped to an index field; if this mapping is absent or misconfigured, the pipeline cannot store the enriched data and fails, especially for larger documents that produce more output data.

Answer

The maximum page length is too small

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The default language code is not supported

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The text split mode should be 'sentences'

Question 2

A healthcare organization uses Azure Document Intelligence to process patient intake forms. They notice that the confidence scores for field extraction are low. What is the most likely cause?

Accepted Answer

The custom model was trained with only 10 labeled forms. Custom models in Azure Document Intelligence require a minimum of five labeled forms for training, but low confidence scores typically indicate insufficient training data. With only 10 labeled forms, the model lacks enough examples to generalize well across variations in handwriting, formatting, and field values, leading to poor extraction confidence.

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The document resolution is too low

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The document layout is not analyzed

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The batch processing size is too large

Question 3

A company uses Azure Document Intelligence to extract data from invoices. They deploy the model to a container for on-premises processing. After deployment, they notice that the container consumes more memory than expected. What should they do to optimize memory usage?

Accepted Answer

Set the 'Memory' environment variable to a lower value in the container configuration. Option A is correct because Azure Document Intelligence containers expose a 'Memory' environment variable that allows you to limit the container's memory allocation. By setting this variable to a lower value, you constrain the container's memory usage, which directly addresses the issue of higher-than-expected consumption. This is the recommended approach for optimizing memory in containerized deployments.

Answer

Use the 'Read' model instead of the 'Layout' model

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Use the cloud API instead of the container

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Reduce the batch size in the client application

Question 4

An organization is deploying a conversational AI solution using Azure OpenAI. They want to ensure the model's responses are grounded in their own knowledge base documents to reduce hallucinations. Which approach should they implement?

Accepted Answer

Integrate Azure Cognitive Search for retrieval-augmented generation (RAG). Option A is correct because Retrieval-Augmented Generation (RAG) with Azure Cognitive Search allows the model to dynamically retrieve relevant chunks from the organization's knowledge base documents at inference time. This grounds responses in authoritative, up-to-date content, directly reducing hallucinations by providing factual context rather than relying solely on the model's parametric memory.

Answer

Fine-tune the model on the knowledge base documents

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Implement Azure AI Content Safety filters

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Use prompt engineering to instruct the model to only use the knowledge base

Question 5

Which TWO actions are required to enable a custom chatbot built with Azure OpenAI to answer questions based on a company's internal PDF documents?

Accepted Answer

Ingest the PDFs into an Azure Cognitive Search index. Option D is correct because Azure Cognitive Search provides the indexing and retrieval capabilities needed to make PDF content searchable. By ingesting PDFs into an Azure Cognitive Search index, the chatbot can perform vector or keyword searches over the extracted text, enabling it to retrieve relevant passages to answer user questions. This is the standard approach for grounding a custom chatbot on proprietary documents without modifying the underlying model.

Answer

Use Azure AI Document Intelligence to extract text from PDFs before indexing

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Deploy Azure AI Content Safety to filter responses

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Fine-tune the GPT model on the PDF content

Question 6

A healthcare organization is deploying an Azure AI Document Intelligence solution to extract medical record data. The solution must comply with HIPAA and process sensitive patient data. The data must be encrypted at rest and in transit, and access must be audited. The solution will be used by multiple departments, each requiring different levels of access. Which combination of Azure services should the architect recommend?

Accepted Answer

Azure AI Document Intelligence with private endpoints, managed identity authentication, Azure Storage with customer-managed keys (CMK), and Azure RBAC for access control.. Option C is correct because it ensures HIPAA compliance by using private endpoints to keep traffic within the Azure backbone network, managed identity for secure authentication without storing credentials, customer-managed keys (CMK) for encryption at rest with full control over key rotation, and Azure RBAC to enforce least-privilege access across departments. This combination addresses encryption in transit (private endpoint + HTTPS), encryption at rest (CMK), and audited access (RBAC + Azure Monitor).

Answer

Azure AI Document Intelligence with public endpoint, Azure AD authentication, and Azure Storage with customer-managed keys (CMK).

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Azure AI Document Intelligence with public endpoint, Azure AD authentication, and Azure Storage with encryption at rest using Microsoft-managed keys.

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Azure AI Document Intelligence with service endpoints, shared access signature (SAS) tokens, and Azure Storage with encryption at rest using Microsoft-managed keys.

Question 7

You are developing a content moderation solution that uses Azure Content Moderator to review images uploaded by users. The solution must flag images for potential adult content. Which TWO actions should you take to achieve this goal?

Accepted Answer

Configure a human review loop using the Review tool. Option C is correct because Azure Content Moderator's Review tool enables human-in-the-loop review, which is essential for accurately flagging adult content when automated confidence scores are borderline. This allows human moderators to confirm or override the automated classification, ensuring compliance with content policies. Option D is correct because the Evaluate operation on the image endpoint directly analyzes images for adult and racy content using pre-trained models, returning a confidence score that can trigger further action.

Answer

Train a custom image classifier using Content Moderator's training API

Answer

Use the List Management API to add images to a blocklist

Answer

Use the OCR operation to extract text from images

Question 8

A legal firm is using Azure AI Language to analyze contracts. They need to extract key clauses, parties involved, and dates. The solution must be customizable to their specific contract types. Which TWO Azure AI Language features should they use?

Accepted Answer

Prebuilt NER for Legal. Prebuilt NER for Legal is correct because it provides out-of-the-box entity extraction tailored to legal documents, including parties, dates, and key clauses, without requiring custom training. This feature is specifically designed for legal use cases, making it ideal for a firm that needs to quickly extract standard legal entities from contracts.

Answer

Conversation summarization

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Key phrase extraction

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Entity linking

Question 9

Which TWO actions should you take to reduce the latency of an Azure AI Computer Vision OCR call on a large image?

Accepted Answer

Resize the image to a smaller resolution before calling the API.. Options B and D are correct. B: Resizing reduces processing time. D: Using the Read API asynchronously allows the client to poll, avoiding timeout. A: Increasing timeout doesn't reduce latency. C: Using CPU doesn't help. E: Cognitive Services container on-premises might add network latency.

Answer

Use a CPU-bound compute instance.

Answer

Increase the API timeout value.

Answer

Deploy the Cognitive Services container on-premises.

Question 10

Refer to the exhibit. You have a skillset with two skills. You run the indexer and find that the output field 'organizations' is empty for documents that clearly contain organization names. The 'keyPhrases' output is populated correctly. What is the most likely cause of the issue?

Accepted Answer

The skill is not configured with a 'defaultLanguageCode' and the documents are not in English.. The 'keyPhrases' output is populated correctly, indicating the text extraction and overall pipeline are functional. The Entity Recognition skill requires a 'defaultLanguageCode' to correctly identify entities; if it is not set and the documents are not in English, the skill may fail to extract organizations, resulting in an empty 'organizations' field. This is a known behavior where the skill defaults to English and cannot process other languages without explicit configuration.

Answer

The skill's 'name' property is set to '#1', which is invalid.

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The 'categories' property is misspelled; it should be 'entityCategories'.

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The input source '/document/content' is incorrect; it should be '/document/text'.

Question 11

You need to provide a team of data scientists with access to an Azure Machine Learning workspace. The data scientists should be able to create and run experiments, but they should not be able to delete the workspace or modify its configuration. Which built-in role should you assign?

Accepted Answer

Contributor. Option A is correct because the Contributor role allows creating and managing resources within the workspace, including experiments, but not deleting the workspace or modifying its configuration. Option B is wrong because Reader is read-only and cannot create experiments. Option C is wrong because User Access Administrator primarily manages user access and permissions, not resource creation. Option D is wrong because Owner grants full control, including the ability to delete the workspace and modify its configuration.

Answer

Reader

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User Access Administrator

Answer

Owner

Question 12

Which TWO actions should you take to ensure compliance with data privacy regulations when using Azure AI Language to process customer support transcripts that contain personally identifiable information (PII)?

Accepted Answer

Enable PII detection and redaction in the Azure AI Language service.. Option D is correct because Azure AI Language's PII detection and redaction feature is specifically designed to identify and mask personally identifiable information (PII) in text, such as names, addresses, and social security numbers, directly within the service. This built-in capability ensures that sensitive data is removed or obfuscated before storage or further processing, directly supporting compliance with data privacy regulations like GDPR or HIPAA.

Answer

Disable diagnostic logging for the resource.

Answer

Use customer-managed keys (CMK) for encryption.

Answer

Apply Azure AI Content Safety filters to anonymize personal data.

Question 13

Refer to the exhibit. You receive this error when calling an Azure Cognitive Services API. What is the most likely cause?

Accepted Answer

The subscription key is not valid for this Cognitive Services resource.. The error indicates that the subscription key provided is not associated with the Cognitive Services resource being accessed. In Azure Cognitive Services, each resource has a unique set of keys, and using a key from a different resource (even in the same region) will result in a 401 Unauthorized error with this specific message. Option D is correct because the key must match the resource's endpoint exactly.

Answer

The API endpoint is incorrect.

Answer

The subscription key has expired.

Answer

The subscription key is from a different region.

Question 14

Which TWO actions should you take to ensure compliance with data residency requirements when using Azure AI services across multiple regions?

Accepted Answer

Configure the AI service's data residency settings to restrict data to the region. Option B is correct because Azure AI services provide data residency settings that allow you to restrict data processing to a specific region, ensuring compliance with data residency requirements. Option E is correct because deploying separate instances of the AI service in each required region gives you full control over where data is stored and processed, which is a common pattern for meeting strict data residency mandates.

Answer

Use a global Azure AI service resource and configure geo-replication

Answer

Enable Azure Front Door to route traffic to the nearest region

Answer

Disable data replication for the AI service

Question 15

Match each Azure AI tool to its purpose.

Accepted Answer

Azure Cognitive Search: AI-powered cloud search service for indexing and querying diverse content.. Correct matches: Azure Cognitive Search is an AI search service; Azure Bot Service is for building bots; Azure Cognitive Services offers pre-built AI APIs. Common confusions include swapping Cognitive Search with Machine Learning, and Bot Service with Cognitive Services.

Answer

Azure Cognitive Search: Platform for building, training, and deploying machine learning models.

Answer

Azure Bot Service: Pre-built APIs for vision and speech.

Answer

Azure Cognitive Services: Tools to build and manage bots.

Question 16

Match each Azure AI concept to its description.

Accepted Answer

Azure AI Perception: Processes visual and auditory data to understand the world. The correct matches pair each Azure AI concept with its primary function. Common confusions occur between Perception and Content Safety (both deal with content analysis) and between Decision and Communication (both involve interaction but different purposes).

Answer

Azure AI Perception: Detects and monitors offensive or risky content

Answer

Azure AI Decision: Creates conversational agents and speech-based interactions

Question 17

Match each Azure AI term to its definition.

Accepted Answer

Azure Cognitive Services: A collection of AI services and APIs that help you build intelligent applications without needing direct AI or data science skills.. Correct matches: Azure Cognitive Services provides pre-built AI APIs; Azure Machine Learning is for ML lifecycle; Azure Bot Service for building bots. Common confusion involves swapping definitions of Cognitive Services and Machine Learning.

Answer

Azure Cognitive Services: A service for building and training machine learning models.

Answer

Azure Machine Learning: A collection of pre-built AI APIs for vision, speech, language, etc.

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Azure Bot Service: A service that provides pre-built AI models for natural language processing.

Question 18

A company is building a chatbot using Azure Language Service and wants to ensure that the chatbot can understand user intents and extract entities from user utterances. The chatbot must be able to handle multiple intents in a single utterance and must support pre-built entities such as numbers and dates. Which action should the developer take to configure the Language service accordingly?

Accepted Answer

Enable the 'Multiple intents' setting in the Language service project.. Option A is correct because the Azure Language Service's Conversational Language Understanding (CLU) project includes a 'Multiple intents' setting that, when enabled, allows the model to predict more than one intent per utterance. This is essential for handling compound user inputs where the user expresses multiple goals in a single sentence.

Answer

Upgrade the Language service tier from Standard to Custom.

Answer

Configure the project to use Orchestration workflow.

Answer

Set the project language to 'Multilingual' to enable entity recognition.

Answer

Define list entities for numbers and dates.

Question 19

You are developing a generative AI solution that uses Azure OpenAI Service. You need to control the creativity of the generated responses. Which parameter should you adjust?

Accepted Answer

temperature. The temperature parameter directly controls the randomness of token selection in the model's output. Lower values (e.g., 0.2) make the model more deterministic and focused, while higher values (e.g., 0.8) increase creativity and variability. This is the primary parameter for adjusting creativity in Azure OpenAI Service.

Answer

top_p

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max_tokens

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frequency_penalty

Question 20

Your organization uses Azure OpenAI Service to generate code snippets. You want to log all user prompts and model responses for auditing purposes. What should you configure?

Accepted Answer

Enable diagnostic settings in Azure OpenAI Service to send logs to a Log Analytics workspace.. Option C is correct because Azure OpenAI Service's diagnostic settings allow you to stream platform logs—including user prompts and model responses—to a Log Analytics workspace. This is the native, built-in mechanism for capturing and analyzing request/response data for auditing, without needing additional services or custom middleware.

Answer

Use Azure API Management to log requests and responses.

Answer

Store logs in Azure Key Vault.

Answer

Use Azure AI Search to index the prompts and responses.

AI-102 Practice Test 4 — 25 Questions

Exhibit

AI-102 Practice Test 4 — 25 Questions

Exhibit