AI-102 Practice Test 25 — 15 Questions

Question 1

You need to extract personally identifiable information (PII) from a set of text documents before indexing them in Azure AI Search. The PII must be redacted. Which Azure AI service and configuration should you use?

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

Accepted Answer

Use the built-in PII detection skill in Azure AI Search with redaction mode enabled. Azure AI Search provides a built-in PII detection skill that can automatically identify and redact PII entities (such as names, phone numbers, and email addresses) from text documents during the indexing pipeline. The skill supports a 'redactionMode' configuration that replaces detected PII with a placeholder (e.g., '***'), meeting the requirement to redact PII before indexing.

Answer

Use the entity recognition skill in Azure AI Search and map to a target field

Answer

Use Azure AI Document Intelligence with a custom model to identify PII fields

Answer

Use Azure AI Language's key phrase extraction to find PII

Question 2

You are building a generative AI application that uses Azure OpenAI Service. The application must access data from an Azure SQL database to answer user questions. You need to implement a solution that retrieves the most relevant data without exposing the database schema to the model. Which approach should you use?

Accepted Answer

Use a middle-tier service to query the database, then pass results to the model via Azure OpenAI On Your Data. It uses a middle-tier service to query the Azure SQL database and then passes only the relevant results to the Azure OpenAI model via the 'On Your Data' feature. This approach ensures the model never sees the database schema, protecting sensitive structural details while still providing the necessary context for answering user questions. It also allows the middle-tier to handle authentication, query optimization, and data filtering securely.

Answer

Configure the model to generate SQL queries and execute them against the database

Answer

Embed the entire database schema in the system prompt

Answer

Fine-tune the model with historical query results from the database

Question 3

You are using Azure AI Language to extract information from medical research papers. You need to identify terms like 'dosage', 'side effects', and 'contraindications' specific to the medical domain. Which capability should you use?

Accepted Answer

Custom Named Entity Recognition (NER). Custom Named Entity Recognition allows you to train a model to recognize custom entities like medical terms. Option A is wrong because prebuilt NER only recognizes general entities like person, location, etc. Option C is wrong because PII detection is for personal information. Option D is wrong because entity linking links to external knowledge bases.

Answer

Prebuilt Named Entity Recognition (NER)

Answer

PII detection

Answer

Entity linking

Question 4

A developer is tasked with integrating Azure OpenAI Service into an application that generates product descriptions. The developer needs to ensure that the generated content does not contain offensive language. Which Azure AI service should be used in addition to Azure OpenAI?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety (D) is the correct service because it provides built-in content moderation capabilities that can detect and filter offensive, inappropriate, or harmful language in text and images. By integrating Azure AI Content Safety with Azure OpenAI, the developer can automatically screen generated product descriptions for profanity, hate speech, or other offensive content before they are displayed to users, ensuring compliance with content policies.

Answer

Azure AI Search

Answer

Azure AI Vision

Answer

Azure AI Language

Question 5

Your knowledge mining solution uses Azure AI Search. Users complain that search results are not relevant. You have enabled semantic search but results still lack context. What should you do to improve relevance?

Accepted Answer

Ensure the index includes a semantic configuration with title and content fields. Semantic search in Azure AI Search requires a semantic configuration that explicitly maps the title and content fields to be used for semantic ranking. Without this configuration, the search engine cannot apply the deep neural network models that understand context and intent, so results remain based on keyword matching even when the semantic search feature is enabled.

Answer

Increase the number of partitions to handle more data

Answer

Configure a scoring profile with boosting based on metadata

Answer

Increase the number of replicas to improve query performance

Question 6

You have the above skillset in Azure AI Search. The indexer processes a document with 12,000 characters of content. How many entity recognition skill executions occur?

Accepted Answer

3. The Azure AI Search entity recognition skill has a maximum text length per execution of 5,000 characters. A document with 12,000 characters is split into chunks of up to 5,000 characters, resulting in three chunks (5,000 + 5,000 + 2,000). Each chunk triggers one skill execution, so three executions occur.

Answer

4

Answer

2

Answer

1

Question 7

Refer to the exhibit. The indexer using this skillset fails with an error that the skill cannot be executed. The Azure Function is deployed and responds correctly when tested directly. What is the most likely cause?

Accepted Answer

The skill endpoint is using HTTP instead of HTTPS.. Azure AI Search indexers that invoke custom skills via Azure Functions require the endpoint to use HTTPS. If the endpoint is configured with HTTP, the indexer will fail with a 'skill cannot be executed' error, even if the function itself works when tested directly. This is because the indexer enforces secure communication to protect data in transit.

Answer

The context is set incorrectly to '/document'.

Answer

The input source '/document/content' is invalid.

Answer

The skill's output targetName does not match the index field.

Question 8

You are designing a conversational AI solution using Microsoft Copilot Studio. The exhibit shows part of a topic configuration. What is the purpose of the 'triggers' section?

Accepted Answer

To define the conditions that activate the topic. In Microsoft Copilot Studio, the 'triggers' section defines the conditions (such as specific phrases, intents, or keywords) that activate a topic. This is the entry point for the conversation flow, ensuring the bot responds appropriately when a user's input matches the defined trigger phrases. Option C correctly identifies this purpose.

Answer

To define the response the bot sends

Answer

To define the authentication method

Answer

To define the entities to extract

Question 9

Your organization needs to monitor Azure AI services for unusual activity patterns that might indicate a security threat. Which Microsoft security solution should you use?

Accepted Answer

Microsoft Sentinel. Microsoft Sentinel is a cloud-native Security Information and Event Management (SIEM) and Security Orchestration Automated Response (SOAR) solution. It is specifically designed to ingest logs from Azure AI services, apply analytics to detect unusual activity patterns, and generate alerts for potential security threats, making it the correct choice for monitoring AI services for security anomalies.

Answer

Microsoft Intune

Answer

Microsoft Defender XDR

Answer

Microsoft Purview

Question 10

Which TWO Azure services can be used to monitor and analyze the usage costs of an Azure AI solution that includes multiple Cognitive Services accounts?

Accepted Answer

Azure Cost Management + Billing. Azure Cost Management + Billing (C) provides native tools to track, analyze, and optimize cloud spending across all Azure services, including multiple Cognitive Services accounts. It allows you to break down costs by resource, resource group, or tag, and set budgets with alerts. Azure Monitor metrics and logs (E) can capture custom metrics and diagnostic logs from Cognitive Services, enabling you to correlate usage patterns with cost data for deeper analysis.

Answer

Azure Service Health

Answer

Azure Policy

Answer

Azure Advisor

Question 11

Your company has an existing Azure AI Search index used for e-commerce product search. The index is populated by an indexer that runs every hour. Recently, the product team added a new field 'promotionPrice' that should be searchable. You need to update the index to include this field without reindexing all documents. The field should be of type Edm.Double, searchable, filterable, and sortable. What should you do?

Accepted Answer

Update the index schema to add the new field using the portal or REST API. Then update the indexer to map the field.. Azure AI Search allows you to add new fields to an existing index schema without reindexing all documents, as long as the new field is nullable or has a default value. After updating the index schema via the portal or REST API, you must update the indexer's field mappings to map the new 'promotionPrice' field from the data source to the index. The indexer will then populate the field for existing documents on its next run, and new documents will include the field automatically.

Answer

Add the new field to the data source mapping in the indexer. The indexer will automatically update the index schema.

Answer

Change the indexer schedule to run every 5 minutes to pickup the new field faster.

Answer

Delete the index and recreate it with the new field. Then run the indexer to repopulate all documents.

Question 12

You are testing an Azure OpenAI chat completion. The response shown in the exhibit is returned. What does the finish_reason of 'content_filter' indicate?

Accepted Answer

The model's response was blocked by the content filter.. The 'content_filter' finish_reason indicates that the Azure OpenAI content filtering system detected that the model's generated response violated one of the configured content policies (e.g., hate, violence, self-harm, sexual content). The response was therefore blocked before being returned to the user, and the finish_reason explicitly signals this filtering action rather than a normal completion or a stop due to token limits.

Answer

There was a system error during processing.

Answer

The user's prompt was flagged by the content filter.

Answer

The model refused to answer due to insufficient data.

Question 13

A news organization uses Azure Video Indexer to generate transcripts of live broadcasts. They notice that the speaker names are not appearing in the transcript. What is the most likely cause?

Accepted Answer

The speaker identification model has not been trained with voice samples.. Speaker names are missing because Azure Video Indexer's speaker identification feature requires pre-trained voice samples to match speakers to their identities. Without a custom voice model trained on known speakers' audio, the service can only label speakers as 'Speaker #1', 'Speaker #2', etc., but cannot assign actual names. This is a supervised learning process where the model must be trained with labeled voice samples before it can recognize and name speakers.

Answer

The video resolution is too low for OCR.

Answer

The video format is not supported.

Answer

The language is not set correctly.

Question 14

You are a content moderator for a social media platform that uses Azure Content Moderator. The platform has a custom blocklist of URLs (e.g., 'example.com/spam') and a custom term list for hate speech. Recently, users have been posting comments that contain a new form of hate speech not yet in the term list. The comments are being allowed through moderation. You need to update the solution to catch these new phrases as quickly as possible. What should you do?

Accepted Answer

Add the new phrases to the existing custom term list using the List Management API. Azure Content Moderator's custom term lists allow you to dynamically add new offensive terms or phrases via the List Management API, which immediately updates the moderation screening without retraining or redeploying any model. This provides the fastest way to catch new hate speech patterns as they emerge, as the term list is checked in real-time during content review.

Answer

Create a new custom model using Custom Vision to detect the new phrases in text

Answer

Retrain the image classification model using the new phrases as training data

Answer

Delete the existing term list and recreate it with the new phrases included

Question 15

You have configured a system message for an Azure OpenAI chat completion deployment as shown in the exhibit. Users are reporting that the assistant sometimes refuses to answer questions that are clearly within the scope of the provided data. What is the most likely issue?

Accepted Answer

The system message encourages the model to err on the side of caution, leading to false-negative refusals.. The system message likely contains overly cautious language (e.g., 'only answer if you are certain' or 'do not speculate'), which causes the model to refuse answering even when the data clearly supports the response. This is a known behavior in Azure OpenAI chat completions where the system message's tone and constraints directly influence refusal rates, leading to false-negative refusals.

Answer

The system message explicitly prohibits making up information, which is correct behavior.

Answer

The system message does not include instructions to use the provided data.

Answer

The temperature parameter is set too high, causing the model to hallucinate.