AI-102 Practice Test 4 — 25 Questions

Question 1

A company is using Azure Cognitive Service for Language to analyze customer support transcripts. They want to identify custom categories (e.g., 'billing', 'technical support') using a custom text classification model. After training and deploying the model, they receive many false positives for the 'billing' category. What is the best first step to improve model accuracy?

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

Accepted Answer

Review the training data for the 'billing' category and correct any mislabeled examples.. False positives for a specific category like 'billing' most often stem from mislabeled or ambiguous training examples in that category. By reviewing and correcting the training data for 'billing', you directly address the root cause of the model's confusion, which is the most effective first step in custom text classification model improvement.

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Add more training data to all categories to improve overall model performance.

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Use a different Azure AI service, such as key phrase extraction, to identify billing-related content.

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Increase the confidence threshold for the 'billing' category to reduce false positives.

Question 2

You are responsible for an Azure AI solution that uses Custom Vision to classify manufacturing defects. The model must achieve high recall to avoid missing defects. The current model has high precision but low recall. Which action should you take?

Accepted Answer

Lower the probability threshold for the defect class. Lowering the probability threshold for the defect class means the model will classify an image as defective even when its confidence score is lower. This increases the number of true positives (defects caught), directly improving recall at the cost of potentially more false positives. In Custom Vision, the default probability threshold is 50%, and adjusting it downward is the standard technique to prioritize recall over precision.

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Add more images of non-defective items to the training set

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Increase the number of training iterations

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Increase the probability threshold for the defect class

Question 3

A company is building a chatbot using Azure AI Language. The chatbot must understand user intents and extract entities like dates and locations. The solution should minimize manual labeling effort. Which feature should the team use?

Accepted Answer

CLU prebuilt entity components. CLU prebuilt entity components provide ready-made entity extraction for common types like dates and locations without requiring manual labeling. This minimizes manual effort while still allowing the chatbot to understand user intents and extract entities, as the prebuilt components are domain-agnostic and cover a wide range of entity types out of the box.

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Conversational Language Understanding (CLU) Orchestration Workflow

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CLU prebuilt intents

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Azure AI QnA Maker

Question 4

Which TWO Azure AI services can be used to detect objects in images?

Accepted Answer

Custom Vision. Custom Vision (Option C) is correct because it allows you to train a custom image classification or object detection model using your own labeled images. You can upload images, tag objects with bounding boxes, and then use the trained model to detect specific objects in new images, making it a valid choice for object detection tasks.

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Video Indexer

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Face API

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Azure AI Document Intelligence

Question 5

Which TWO Azure AI services can be used to build a conversational chatbot that uses generative AI? (Choose two.)

Accepted Answer

Azure AI Bot Service. Azure AI Bot Service (B) provides a dedicated platform for building, deploying, and managing conversational chatbots, integrating with channels like Microsoft Teams and Slack. Azure OpenAI Service (E) enables generative AI capabilities by providing access to large language models (e.g., GPT-4) that can generate human-like responses, making it ideal for powering the conversational intelligence of a chatbot.

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Azure AI Search

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Azure AI Translator

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Azure AI Language

Question 6

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 7

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 8

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 9

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.

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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 10

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 11

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 12

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.

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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 13

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 14

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.

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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 15

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

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

Question 16

You are deploying an Azure OpenAI model for a healthcare application. You need to ensure that the model does not generate medical advice and that all responses include a disclaimer. Which configuration should you use?

Accepted Answer

Configure a system message with instructions and enable content filtering.. Configuring a system message with explicit instructions (e.g., 'Do not provide medical advice; always include a disclaimer') combined with Azure AI Content Safety's content filtering allows you to enforce behavioral guardrails and block harmful outputs at the application layer. The system message sets the model's behavior, while content filtering provides a secondary safety net to catch policy violations, ensuring compliance in a regulated healthcare environment.

Answer

Ground the model with your own medical documents.

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Set max_tokens to 50 to limit response length.

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Use Azure AI Content Safety to filter medical terms.

Question 17

A news organization wants to automatically summarize long articles into short, coherent summaries. The solution must preserve the original meaning and key points. Which Azure AI service should be used?

Accepted Answer

Azure AI Language - Extractive Summarization. Azure AI Language's Extractive Summarization is specifically designed to generate concise summaries by extracting the most important sentences from a document while preserving the original meaning and key points. This service uses natural language processing to rank sentences based on relevance and coherence, making it ideal for summarizing long articles without altering the original content.

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Azure AI Document Intelligence

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Azure AI Language - Key Phrase Extraction

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Azure AI Translator

Question 18

You are deploying a generative AI application that uses Azure OpenAI Service. You need to ensure that the application can handle sudden spikes in traffic without exceeding your quota. Which scaling strategy should you implement?

Accepted Answer

Configure a provisioned throughput deployment with auto-scaling.. Provisioned throughput (PTU) deployments with auto-scaling are designed to handle sudden traffic spikes by automatically adjusting capacity within your configured limits, ensuring consistent performance without exceeding quota. This approach provides reserved throughput that can scale up during demand surges and scale down when traffic subsides, unlike pay-as-you-go which is subject to global rate limits and may throttle during spikes.

Answer

Use a Pay-as-you-go deployment and rely on Azure's automatic scaling.

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Create a deployment with a high base TPM and manually adjust during peak times.

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Deploy multiple instances of the model in different regions.

Question 19

You are responsible for ensuring that an Azure AI solution complies with data residency requirements. The solution processes personal data from users in the European Union. You must ensure that data does not leave the EU region. Which two actions should you take?

Accepted Answer

Configure the Azure AI services to disable cross-region replication.. Disabling cross-region replication ensures that data processed by Azure AI services remains within the specified region and is not automatically replicated to another geographic location for redundancy or disaster recovery. This is a critical control for meeting data residency requirements, as it prevents data from leaving the EU region even during failover scenarios.

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Use the global Azure AI services endpoint for simplicity.

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Enable data encryption at rest and in transit.

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Store data in Azure Blob Storage with hot tier.

Question 20

You deploy a custom vision model using Azure AI Custom Vision. After deployment, you notice the model has high accuracy on training data but low accuracy on new images. What is the most likely cause?

Accepted Answer

The model is overfitted to the training data. High accuracy on training data but low accuracy on new images is the classic symptom of overfitting, where the model has memorized the training examples (including noise and irrelevant patterns) rather than learning generalizable features. In Azure AI Custom Vision, this typically occurs when the training dataset is too small, too homogeneous, or lacks sufficient variation, causing the model to fail on unseen data.

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The training time was too short

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The training dataset has too few images

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The wrong domain was selected during training