AI-102 Practice Test 9 — 10 Questions

Question 1

Which THREE factors should you consider when selecting a region for an Azure AI Language resource?

Accepted Answer

Data residency requirements.. Data residency requirements (Option C) are critical when selecting an Azure region for an AI Language resource because Azure AI Language processes and stores customer data within the region where the resource is provisioned. Many organizations have compliance obligations (e.g., GDPR, HIPAA, or local data sovereignty laws) that mandate data remain within specific geographic boundaries, so choosing a region that aligns with these requirements is essential.

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Pricing differences.

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Availability of the Free tier SKU.

Question 2

Which THREE components are required to build a custom question-answering solution using Azure AI Language Service?

Accepted Answer

A bot channel registration or web app to interact with users. A bot channel registration or web app is the front-end interface that allows users to submit questions and receive answers from the custom QnA solution. Without this interaction layer, the knowledge base cannot be queried by end users, making it an essential component for a complete solution.

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An Azure AI Search index

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An Azure Functions app for serverless compute

Question 3

You are using Azure OpenAI Service to generate product descriptions. You notice that the model occasionally outputs descriptions that contain factual inaccuracies about product specifications. You want to reduce these hallucinations without changing the model. What should you do?

Accepted Answer

Provide the product specifications in the prompt and use the system message to instruct the model to base answers on them.. Providing the product specifications directly in the prompt and using the system message to instruct the model to base its answers on them grounds the generation in factual data, reducing hallucinations. This technique, known as 'grounding' or 'retrieval-augmented generation' (RAG), does not modify the model itself but constrains its output to the provided context, which is the only way to reduce factual inaccuracies without changing model parameters.

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Increase the frequency_penalty parameter.

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Decrease the temperature parameter.

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Increase the max_tokens parameter.

Question 4

You deployed a custom neural voice in Azure AI Speech. The model generates poor prosody for long sentences. What should you do?

Accepted Answer

Re-record the training audio at 24 kHz sample rate.. A custom neural voice model requires high-quality training data. A 24 kHz sample rate is the standard for Azure AI Speech's neural voices, as it captures the full frequency range needed for natural prosody. Re-recording at this rate ensures the model learns proper intonation and rhythm for long sentences, directly addressing the poor prosody issue.

Answer

Switch to a prebuilt neural voice.

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Increase the number of training scripts.

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Use SSML tags to control prosody at runtime.

Question 5

A company uses Azure AI Search to index customer support transcripts. They want to enable users to find relevant answers by asking natural language questions. Which feature should they enable in the search service?

Accepted Answer

Semantic search. Semantic search improves relevance by understanding natural language queries and providing answer-style results. Synonym maps (B) help with query expansion but not natural language understanding. Cognitive skills (C) are used for enrichment during indexing, not query-time interpretation. Knowledge mining (D) is a broader process that encompasses multiple services, not a specific feature of Azure AI Search.

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Synonym maps

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Cognitive skills

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Knowledge mining

Question 6

A company uses the Face API for identity verification. During testing, they find that the similarity scores between two images of the same person are lower than expected. Which factor is most likely causing this?

Accepted Answer

The images have different lighting conditions (e.g., one is brightly lit, the other is dark).. B is correct because the Face API's similarity scoring is heavily influenced by lighting conditions. Variations in illumination can alter facial features, shadows, and contrast, which reduces the accuracy of face matching algorithms. The API relies on consistent lighting to extract reliable facial landmarks and embeddings, so differing lighting conditions directly lower similarity scores.

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The images are compressed with different quality levels.

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The backgrounds of the images are different.

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The images have different dimensions (e.g., 500x500 vs 1000x1000).

Question 7

A developer is building an agent using the Microsoft Bot Framework SDK in C#. The agent must authenticate users via Microsoft Entra ID and maintain state across conversations. The solution must store user preferences (e.g., language, timezone) in Azure Cosmos DB. Which state management approach should the developer use?

Accepted Answer

Use UserState with Cosmos DB Storage.. The developer needs to persist user preferences across conversations, which requires UserState (not ConversationState, which is scoped to a single conversation). Cosmos DB Storage is the appropriate choice for durable, scalable, and low-latency storage of user-specific data, and it integrates directly with the Bot Framework SDK's `CosmosDbPartitionedStorage` class.

Answer

Use the Bot State Service (deprecated).

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Use UserState with Blob Storage.

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Use ConversationState with Memory Storage.

Question 8

Your organization uses Microsoft Entra ID for identity management. You are building an AI solution that uses Azure AI Vision to analyze images. The solution must use managed identities to authenticate to the Vision resource. Which RBAC role should you assign to the managed identity?

Accepted Answer

Cognitive Services User. The Cognitive Services User role (B) is the correct RBAC role because it grants the minimum required permissions for a managed identity to call Azure AI Vision APIs (e.g., analyze image, OCR) without allowing any write or management operations. This role is specifically designed for accessing Azure Cognitive Services endpoints, and it aligns with the principle of least privilege for authentication via managed identities.

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Owner

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Reader

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Contributor

Question 9

You are designing a solution that uses Azure AI Translator to translate customer support tickets. The solution must handle confidential data and ensure no data leaves the specified Azure region. What should you do?

Accepted Answer

Create a Translator resource with a private endpoint and disable public network access. Using a private endpoint ensures that all traffic to the Translator resource stays within the Azure virtual network and never traverses the public internet, while disabling public network access enforces that no data can leave the specified Azure region. This combination meets the requirement for handling confidential data with regional data residency guarantees.

Answer

Enable customer-managed keys and disable logging

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Use the global Translator endpoint

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Use the S1 pricing tier to ensure regional processing

Question 10

Refer to the exhibit. You deployed a custom model for Language service. Which command should you run to check if the deployment is ready to accept inference requests?

Accepted Answer

az cognitiveservices account deployment show --resource-group myRG --name myLangService --deployment-name myDeployment. The `az cognitiveservices account deployment show` command retrieves the current state of a specific deployment, including its provisioning status (e.g., 'Succeeded'). Only when the status is 'Succeeded' can the deployment accept inference requests. This is the correct command to verify readiness before sending any prediction calls.

Answer

az cognitiveservices account deployment list --resource-group myRG --name myLangService

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az cognitiveservices account deployment delete --resource-group myRG --name myLangService --deployment-name myDeployment

Answer

az cognitiveservices account deployment create --resource-group myRG --name myLangService --deployment-name myDeployment