AI-102 Plan and manage an Azure AI solution Practice Test 3 — 15 Questions

Question 1

Drag and drop the steps to create a custom Azure AI Translator model into the correct order.

Accepted Answer

Prepare parallel data, Create Custom Translator resource, Upload parallel data, Train model, Deploy model. Start with parallel data, create the resource, upload to custom model, train, then deploy.

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Create Custom Translator resource, Prepare parallel data, Upload parallel data, Train model, Deploy model

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Upload parallel data, Create Custom Translator resource, Train model, Deploy model

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Prepare parallel data, Create Custom Translator resource, Train model, Upload parallel data, Deploy model

Question 2

Drag and drop the steps to troubleshoot a failed Azure AI Search indexer execution into the correct order.

Accepted Answer

Step 1: Check indexer execution history, Step 2: Review error messages, Step 3: Verify data source connectivity, Step 4: Check skillset execution, Step 5: Re-run the indexer. First check history, review errors, verify data source, check skillset, then re-run.

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Step 1: Verify data source connectivity, Step 2: Check indexer execution history, Step 3: Review error messages, Step 4: Check skillset execution, Step 5: Re-run the indexer

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Step 1: Review error messages, Step 2: Check indexer execution history, Step 3: Check skillset execution, Step 4: Verify data source connectivity, Step 5: Re-run the indexer

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Step 1: Check skillset execution, Step 2: Verify data source connectivity, Step 3: Check indexer execution history, Step 4: Review error messages, Step 5: Re-run the indexer

Question 3

Match each Azure AI service to its primary function.

Accepted Answer

Computer Vision: Extracts information from images and videos. The correct matches pair each service with its primary function. Computer Vision analyzes images/videos; Language Service processes text; Speech Service handles audio; Cognitive Search provides AI search. Common confusions include swapping Speech with Computer Vision or Language with Cognitive Search.

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Computer Vision: Converts speech to text and text to speech

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Language Service: AI-powered search over indexed content

Question 4

Match each Azure AI concept to its description.

Accepted Answer

Azure AI Perception: Processes visual and auditory data to understand the world. The correct matches pair each Azure AI concept with its primary function. Common confusions occur between Perception and Content Safety (both deal with content analysis) and between Decision and Communication (both involve interaction but different purposes).

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Azure AI Perception: Detects and monitors offensive or risky content

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Azure AI Decision: Creates conversational agents and speech-based interactions

Question 5

Match each Azure AI scenario to the appropriate service.

Accepted Answer

Building a conversational chatbot: Azure Bot Service. Correct matches: Chatbot -> Bot Service, Image moderation -> Computer Vision, Sentiment -> Text Analytics. Common confusions include associating Bot Service with image tasks or mistaking Cognitive Search for text analytics.

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Building a conversational chatbot: Azure Machine Learning

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Analyzing images for content moderation: Azure Bot Service

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Classifying text by sentiment: Azure Cognitive Search

Question 6

You are deploying an Azure AI Search solution that indexes customer support tickets. The solution must support fuzzy search for misspelled keywords and boost results from premium customers. Which two features should you configure?

Accepted Answer

Scoring profiles. Scoring profiles allow you to boost search results based on specific criteria, such as customer tier, by assigning higher weights to certain fields or adding functions that increase scores for premium customers. This directly supports the requirement to prioritize results from premium customers.

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Synonym maps

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Suggesters

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Custom analyzers

Question 7

Your company uses Azure OpenAI Service to generate product descriptions. You need to ensure that the generated content does not include offensive language and adheres to responsible AI principles. What should you implement?

Accepted Answer

Configure content filters in Azure OpenAI. Content filters in Azure OpenAI allow you to define categories (e.g., hate, violence, self-harm) and severity levels (low, medium, high) to automatically block or flag offensive language in generated outputs. This directly enforces responsible AI principles by preventing harmful content from being surfaced to users, without requiring model retraining or encryption changes.

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Enable customer-managed key encryption

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Fine-tune the model with a curated dataset

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Set usage limits and throttling

Question 8

A team is building a chatbot using Azure Bot Service and Language Understanding (LUIS). The chatbot must handle multiple languages. What should you configure?

Accepted Answer

Create separate LUIS applications for each language. LUIS does not natively support multilingual models within a single app. Each LUIS application is designed for a single language, so to handle multiple languages, you must create separate LUIS apps—one per language—and route user utterances to the appropriate app based on the detected language. This ensures accurate intent and entity recognition tailored to each language's linguistic patterns.

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Configure a single LUIS app with multilingual utterances

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Use Azure AI Search to index translated content

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Use Azure Translator to translate utterances before calling LUIS

Question 9

Refer to the exhibit. You are configuring an Azure AI Foundry agent for customer support. The agent uses Azure AI Search for retrieval and Azure OpenAI for generation. Users report that the agent provides correct answers but sometimes includes inappropriate language. What is the most likely cause?

Accepted Answer

The content safety blocklist is not applied to the chat output. Azure AI Foundry agents rely on Azure OpenAI content filtering to block harmful language in generated responses. If the content safety blocklist is not applied to the chat output, the agent can produce inappropriate language even when the retrieved information is accurate. The blocklist must be explicitly configured to filter the final output of the agent.

Answer

The OpenAI deployment 'gpt-4o' does not support content filtering

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The content safety threshold should be set to 'low' to block more content

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The semantic configuration is not optimized for safety

Question 10

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 11

Your organization wants to implement a document processing pipeline that extracts text from scanned PDFs and identifies named entities. Which two Azure AI services should you use?

Accepted Answer

Azure AI Language. Azure AI Document Intelligence (formerly Form Recognizer) is used to extract text from scanned PDFs via OCR, while Azure AI Language provides pre-built named entity recognition (NER) to identify entities like people, organizations, and locations. Together, they form a complete pipeline: Document Intelligence handles the image-to-text conversion, and Language processes the extracted text for entity extraction.

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Azure AI Custom Vision

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Azure AI Translator

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Azure AI Speech

Question 12

A company uses Azure AI Search to index product catalogs. The search must support multilingual queries and return results in the user's language. What should you configure?

Accepted Answer

Assign appropriate language analyzers to fields. Azure AI Search allows you to assign language-specific analyzers (e.g., Microsoft English, French, Arabic) to individual fields in the index. When a query is submitted, the search engine uses the analyzer associated with the field to tokenize and normalize the text according to the linguistic rules of that language, enabling accurate multilingual search and returning results in the user's language.

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Create synonym maps for each language

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Configure scoring profiles based on language

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Enable semantic search

Question 13

You deploy a Custom Vision object detection model to classify vehicles. The model works well in good lighting but fails in low-light conditions. What is the most appropriate action?

Accepted Answer

Add images with different lighting conditions to the training set. The core issue is a data distribution mismatch: the model was trained primarily on well-lit images and lacks exposure to low-light examples. Adding images with diverse lighting conditions directly addresses this by enriching the training dataset, enabling the model to learn robust features for low-light scenarios. This aligns with the fundamental principle that Custom Vision models are only as good as the training data they receive.

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Increase the probability threshold

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Increase the number of training iterations

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Use a domain-specific model for vehicles

Question 14

Your chatbot uses Azure Bot Service and QnA Maker. Users can ask questions in natural language, and the bot returns answers from a knowledge base. Users report that the bot sometimes returns irrelevant answers. What should you do first?

Accepted Answer

Review and edit the QnA pairs to add alternative phrasings. The core issue is that the bot returns irrelevant answers because the QnA Maker knowledge base lacks sufficient alternative phrasings to match the variety of user questions. By reviewing and editing QnA pairs to add alternative phrasings, you directly improve the synonym and paraphrase coverage, which increases the confidence score for correct matches and reduces irrelevant responses. This is the first and most fundamental troubleshooting step before considering more complex integrations.

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Create multiple QnA Maker knowledge bases for different topics

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Integrate LUIS to detect user intent

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Use Azure AI Search to index the knowledge base

Question 15

Refer to the exhibit. You are using Azure OpenAI to build a trivia bot. The bot responds correctly to simple questions but sometimes gives overly verbose or off-topic answers. Which parameter should you adjust to make responses more focused?

Accepted Answer

Decrease temperature. Decreasing the temperature parameter reduces the randomness of the model's output, making it more deterministic and focused. For a trivia bot that gives overly verbose or off-topic answers, lowering temperature (e.g., from 0.7 to 0.3) forces the model to choose higher-probability tokens, which typically correspond to more concise and relevant responses.

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Increase temperature

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Decrease top_p

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Increase max_tokens

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Decrease max_tokens