AI-102 Practice Test 2 — 25 Questions

Question 1

You are designing a solution that uses Azure AI Language to analyze customer feedback. The solution must detect sentiment, extract key phrases, and identify named entities. Which feature should you use?

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

Accepted Answer

Azure AI Language service. The Azure AI Language service provides pre-built capabilities for sentiment analysis, key phrase extraction, and named entity recognition (NER) as part of its text analytics features. These three tasks are directly supported by the service's Analyze API, making it the correct choice for analyzing customer feedback text.

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Azure AI Speech service

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Azure AI Computer Vision

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Translator API

Question 2

Refer to the exhibit. A developer is configuring a QnA Maker skill for a bot. The skill fails to respond to queries. What is the most likely issue?

Accepted Answer

The modelUrl points to the authoring API instead of the runtime endpoint.. The modelUrl uses the v4.0 API, but the endpoint key and kbId are hardcoded and may be invalid or expired. Additionally, the URL should be for the runtime endpoint, not the authoring API.

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The kbId is not published.

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The skill is not deployed.

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The endpointKey is incorrect.

Question 3

You have deployed a generative AI model using Azure Machine Learning. The model is used for generating financial reports. You need to monitor the model's performance and detect data drift in the input data. What should you use?

Accepted Answer

Azure Machine Learning data drift monitoring. Azure Machine Learning data drift monitoring is the correct choice because it is specifically designed to detect statistical changes in input data over time, which is critical for generative AI models used in financial reporting where data distributions can shift due to market changes or new regulations. It compares the current input data distribution against a baseline dataset using metrics like Wasserstein distance or Population Stability Index, and triggers alerts when drift exceeds a threshold. This ensures the model's outputs remain reliable and compliant, which is a core requirement for generative AI solutions in regulated industries.

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Azure Monitor

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Application Insights

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Azure AI Language

Question 4

A company is building a solution to analyze customer reviews images using Azure AI Vision. They need to extract text from images that may contain both printed and handwritten text. Which feature should they use?

Accepted Answer

Read API. The Read API is the correct choice because it is specifically designed to extract text from images containing both printed and handwritten text, using advanced OCR capabilities that support mixed content. Unlike the OCR API, which is optimized for printed text only, the Read API leverages deep learning models to handle varied handwriting styles and complex layouts, making it ideal for analyzing customer review images.

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Custom Vision

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OCR API (optical character recognition)

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Azure AI Document Intelligence

Question 5

Refer to the exhibit. You are designing a Data Factory pipeline to perform sentiment analysis on a text column. The pipeline fails with a 'BadRequest' error. What is the most likely issue?

Accepted Answer

The input format is incorrect; it should be a JSON array of documents. The Cognitive Services activity in Azure Data Factory that performs sentiment analysis expects input in the form of a JSON array of documents, each containing an 'id' and 'text' field. A 'BadRequest' error typically indicates that the input format is incorrect, such as passing a single string or an improperly structured object instead of the required array. The other options are less likely because the error is specifically related to the request payload format.

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The output variable 'sentimentResult' is not defined

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The activity type should be 'AzureFunction'

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The linked service name is misspelled

Question 6

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?

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 7

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 8

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.

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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 9

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 10

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 11

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 12

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.

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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 13

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.

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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 14

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.

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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 15

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.

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POST /threads/{thread_id}/runs

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POST /threads

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GET /agents

Question 16

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.

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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 17

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.

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4

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2

Answer

1

Question 18

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 19

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.

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The skill can only accept one input and produce one output

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The skill must be written in Python

Question 20

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.

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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