AI-102 Practice Test 2 — 30 Questions

Question 1

Refer to the exhibit. You have trained an object detection model in Azure Custom Vision. The model is published as 'defect-model'. You need to deploy this model to a Docker container for on-premises inference using the Azure IoT Edge runtime. What should you do first?

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

Accepted Answer

Export the model as a Docker container (e.g., TensorFlow) using the Custom Vision portal.. To deploy a Custom Vision model to an Azure IoT Edge device, you must first export the model as a Docker container (e.g., TensorFlow, ONNX, or DockerFile) from the Custom Vision portal. This export creates a container image that can be deployed to Azure Container Registry and then used as a module in an IoT Edge deployment. Without this export step, you cannot create the containerized module required for on-premises inference.

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Create an Azure Container Registry and push the Custom Vision base image.

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Use the Custom Vision prediction API to call the published endpoint from the edge device.

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Retrain the model with more images to improve mAP.

Question 2

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.

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Audit logging for all API calls.

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Data encryption at rest.

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Rate limiting on the endpoint.

Question 3

You are developing a chat application that uses Azure OpenAI GPT-4 to answer customer questions. You need to ensure the model does not generate harmful content. Which configuration should you set?

Accepted Answer

Enable the content filter in Azure OpenAI Service.. Azure OpenAI Service includes a built-in content filter that actively scans both input prompts and generated completions to detect and block harmful content such as hate speech, violence, or self-harm. This filter operates at the service level, providing a robust safety layer that cannot be bypassed by model configuration alone. While system prompts can guide behavior, they are not a reliable safeguard against adversarial inputs or model misuse.

Answer

Use a system prompt that instructs the model to be safe.

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Set the temperature parameter to 0.

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Set max_tokens to a low value.

Question 4

You are a developer at an e-commerce company. The company wants to build a product search feature that allows customers to search for products using natural language phrases like "red running shoes under $100". The product catalog is stored in Azure Cosmos DB and includes product descriptions, prices, and categories. The solution must use Azure AI Search and must extract entities from product descriptions to enable filtering (e.g., color, size, brand). The search must also support fuzzy matching for misspelled queries. You need to design the indexing pipeline. Which actions should you take?

Accepted Answer

Use Azure AI Language entity extraction as a custom skill, and enable fuzzy search in the index. Azure AI Language entity extraction as a custom skill in an Azure AI Search skillset extracts entities such as color, size, and brand from product descriptions, which can then be mapped to filterable index fields. Enabling fuzzy search (via the Lucene query syntax or the built-in fuzzy matching in the query parser) supports misspelled queries. This combination directly satisfies the requirement to extract entities for filtering and to support fuzzy matching.

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Use Azure AI Language key phrase extraction, and enable vector search

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Use Azure AI Document Intelligence to extract entities, and enable semantic ranking

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Use Azure AI Vision OCR to extract text, and enable synonyms

Question 5

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.

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To define the response the bot sends

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To define the authentication method

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To define the entities to extract

Question 6

A company is deploying an Azure AI solution that uses Azure Cognitive Services. The solution must comply with data residency requirements that mandate all customer data be stored within a specific geographic region. Which action should the company take when creating the Cognitive Services resource?

Accepted Answer

Select the appropriate region during resource creation.. Data residency requirements are satisfied by physically storing customer data within a specific geographic boundary. When creating an Azure Cognitive Services resource, selecting the appropriate region (e.g., 'West Europe' or 'East US') during the provisioning process ensures that all data processed and stored by that service instance remains within that Azure datacenter region. This is the fundamental and only guaranteed method to enforce data residency at the resource level.

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Apply a resource tag that specifies the region.

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Configure the endpoint URL to point to a regional endpoint.

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Set the SKU to a tier that supports regional restrictions.

Question 7

You are building an Azure AI Search solution to index a collection of technical manuals. Users need to find documents by searching for specific terms and also have the ability to filter by document category. Which feature should you configure in the index to support filtering?

Accepted Answer

Set the 'filterable' property to true on the category field. Setting the 'filterable' property to true on the category field enables Azure AI Search to apply OData filter expressions (e.g., `$filter=category eq 'Networking'`) during query execution. This allows users to narrow search results by document category without requiring the field to be full-text searchable, which is essential for efficient filtering in a technical manuals index.

Answer

Set the 'facetable' property to true on the category field

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Set the 'searchable' property to true on the category field

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Set the 'sortable' property to true on the category field

Question 8

You have an Azure AI solution that uses Azure AI Language to perform sentiment analysis. The solution is experiencing high latency. Which action should you take to reduce latency?

Accepted Answer

Scale the service by increasing the number of instances or using a higher pricing tier.. Scaling the Azure AI Language service by increasing the number of instances or moving to a higher pricing tier (e.g., from Standard S0 to a tier with higher throughput) directly addresses high latency by providing more capacity to handle concurrent requests. High latency often results from hitting the service's rate limits or throughput constraints, and scaling alleviates this bottleneck without changing the underlying architecture.

Answer

Move the service to a different Azure region.

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Use the Free tier of the Azure AI Language service.

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Increase the request timeout value.

Question 9

You are using Azure AI Language Service to extract key phrases from customer reviews. You notice that for reviews containing the word 'not good', the service sometimes extracts 'good' as a key phrase. What is the most likely reason?

Accepted Answer

Key phrase extraction does not consider negation. Key phrase extraction in Azure AI Language Service uses a statistical model that identifies significant terms based on frequency and context, but it does not inherently understand negation. When the phrase 'not good' appears, the model may still extract 'good' as a key phrase because it recognizes 'good' as a high-value term, ignoring the negation. This is a known limitation of the feature, as it focuses on noun phrases and important terms rather than sentiment or negated constructs.

Answer

The language detection model misidentified the language

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You need to set a confidence threshold to exclude negative phrases

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The service is not trained on your specific domain

Question 10

You are deploying an agentic solution using Azure AI Agent Service. The agent needs to be invoked from a custom application using REST API calls. Which endpoint should you use to send a message to the agent?

Accepted Answer

POST /threads/{thread_id}/messages. To send a message to an existing conversation thread in Azure AI Agent Service, you must use the POST /threads/{thread_id}/messages endpoint. This adds the user's message to the specified thread, which the agent can then process in a subsequent run. The REST API requires the thread to already exist, and messages are posted directly to that thread's resource.

Answer

POST /threads/{thread_id}/runs

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POST /threads

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GET /agents

Question 11

You are designing a knowledge mining solution for a medical research organization. The solution must extract relationships between drugs, diseases, and genes from scientific articles. The data will be stored in a knowledge graph for querying. Which Azure AI service should you use for the extraction?

Accepted Answer

Azure AI Language healthcare entity recognition and relation extraction. Azure AI Language's healthcare entity recognition and relation extraction is specifically designed to extract medical entities (drugs, diseases, genes) and their relationships from unstructured text, making it ideal for building a knowledge graph. This pre-built model uses deep learning trained on biomedical literature, directly supporting the required extraction without custom training.

Answer

Azure AI Search with semantic ranking

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Azure AI Translator with dictionary lookup

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Azure AI Document Intelligence custom extraction model

Question 12

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 13

You plan to use Azure AI Search to index a large number of text documents stored in Azure Blob Storage. The documents are in English. You want to automatically extract key phrases from the content during indexing. What should you add to the skillset?

Accepted Answer

Key Phrase Extraction skill. The Key Phrase Extraction skill is the correct choice because it is specifically designed to identify and extract important phrases from text content, which aligns with the requirement to automatically extract key phrases during indexing. This skill is part of Azure AI Search's cognitive skillset and operates on English text to produce a list of key phrases per document.

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Sentiment skill

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Language Detection skill

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Entity Recognition skill

Question 14

Which THREE considerations are important when designing a custom skill for Azure AI Search that calls an external API for specialized data extraction?

Accepted Answer

The API endpoint must be reachable from the search service. Option A is correct because a custom skill in Azure AI Search is essentially a Web API skill that the indexer invokes over HTTPS, so the external API endpoint must be network-reachable from the search service (including any required private endpoint or firewall rules) or enrichment will fail. Option D is correct because the Web API skill has a documented maximum request payload size of 16 MB; larger payloads will be rejected, so the skill design must keep the serialized input within that limit. Option E is correct because the Web API skill enforces a maximum execution time of 230 seconds per call, after which the request times out, so long-running extraction logic must be optimized or split. Option B is wrong because a custom skill can accept multiple inputs and produce multiple outputs via the inputs and outputs mappings. Option C is wrong because the skill can be implemented in any language or framework (for example C#, Node.js, or Java) as long as it exposes a compatible HTTP endpoint.

Answer

The skill can only accept one input and produce one output

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The skill must be written in Python

Question 15

A financial services firm uses Azure AI Language to analyze earnings call transcripts. They need to extract key phrases and identify sentiment for each speaker's turn. Which approach should they use?

Accepted Answer

Split the transcript by speaker turns and call key phrase extraction and sentiment analysis on each part. The requirement is to extract key phrases and identify sentiment per speaker turn. The Azure AI Language key phrase extraction and sentiment analysis APIs operate on individual text inputs. By splitting the transcript by speaker turns, each segment can be analyzed independently, providing per-speaker insights. Processing the entire transcript as a single document would aggregate sentiment and key phrases, losing per-speaker granularity.

Answer

Call the prebuilt sentiment analysis API on the entire transcript

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Use QnA Maker to extract Q&A pairs per speaker

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Use Text Analytics for health to extract entities and sentiment

Question 16

You are a solution architect at a legal firm. The firm wants to build a copilot using Microsoft Foundry that answers questions about case law documents stored in Azure Blob Storage. The copilot should use the Retrieval Augmented Generation (RAG) pattern with Azure AI Search as the vector store. The documents are in PDF format and include complex tables and footnotes. The solution must ensure that the answers are grounded in the documents and that the copilot can handle follow-up questions. You need to design the ingestion pipeline. Which approach should you take?

Accepted Answer

Use Azure AI Document Intelligence to extract content, then chunk by headings and paragraphs, generate embeddings using Azure OpenAI, and index in Azure AI Search with vector search. It uses Azure AI Document Intelligence to accurately extract content from PDFs (including complex tables and footnotes), then chunks by headings and paragraphs to preserve document structure, generates embeddings via Azure OpenAI for semantic understanding, and indexes in Azure AI Search with vector search to enable RAG-based, grounded answers with follow-up support.

Answer

Use Azure AI Vision OCR to extract text, split by page, and use Azure AI Search keyword search

Answer

Use Azure AI Document Intelligence prebuilt-read model, chunk by character count, and use Azure AI Search with semantic ranking

Answer

Use Azure AI Language to extract key phrases, create a non-vector index, and use simple search

Question 17

You are building a knowledge mining solution for legal documents using Azure AI Search. The solution must extract entities like dates, organizations, and persons from PDF files and index them. Which built-in skill should you add to the skillset to perform this extraction?

Accepted Answer

Named Entity Recognition skill. The Named Entity Recognition (NER) skill in Azure AI Search is specifically designed to extract entities such as dates, organizations, and persons from text. When added to a skillset, it processes the content extracted from PDF files and outputs structured entity information that can be indexed and queried. This directly matches the requirement to extract and index named entities from legal documents.

Answer

Language Detection skill

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Optical Character Recognition (OCR) skill

Answer

Key Phrase Extraction skill

Question 18

Refer to the exhibit. You have this Azure AI Search indexer configuration. The indexer is failing after processing 6 documents that contain errors. What should you do to ensure the indexer continues processing even if some documents fail?

Accepted Answer

Increase maxFailedItems to a higher value, such as 100. The indexer is failing after processing 6 documents because the `maxFailedItems` threshold has been reached. By increasing `maxFailedItems` to a higher value like 100, the indexer will continue processing even if more documents fail, as long as the total number of failed items stays below the new threshold.

Answer

Decrease the batch size to 5

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Increase batch size to 20

Answer

Remove the schedule to run the indexer on demand

Question 19

A company uses Azure OpenAI to generate product descriptions. They notice that the model occasionally produces descriptions that include false claims about product features. The company needs to reduce the frequency of these inaccuracies without changing the training data. Which parameter adjustment would be most effective?

Accepted Answer

Decrease the temperature parameter. Decreasing the temperature parameter reduces the randomness of the model's output, making it more deterministic and less likely to generate creative but factually incorrect statements. This directly addresses the need to reduce false claims without modifying training data, as lower temperature forces the model to rely on its most probable (and typically more accurate) token predictions.

Answer

Increase the top_p parameter

Answer

Increase the max_tokens parameter

Answer

Increase the frequency_penalty parameter

Question 20

Which THREE components are required to build a custom copilot using Microsoft Copilot Studio that can answer questions from a SharePoint document library?

Accepted Answer

A Microsoft Copilot Studio copilot. A Microsoft Copilot Studio copilot is the core conversational interface that orchestrates the interaction. Without the copilot itself, there is no runtime environment to process user queries, manage conversation state, or invoke configured knowledge sources. It is the mandatory host for all custom copilot functionality.

Answer

A Power Automate flow to trigger the copilot

Answer

An Azure OpenAI Service deployment