AI-102 Practice Test 30 — 15 Questions

Question 1

A company uses Azure AI Vision Image Analysis to generate captions for product photos. The solution must return a caption in English and a confidence score for each image. You call the Image Analysis API with the caption feature. The response does not include a confidence score. What should you do to obtain confidence scores for the captions?

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

Accepted Answer

Use the older Computer Vision v3.2 Analyze Image API with the 'description' visual feature.. The legacy Computer Vision v3.2 Analyze Image API with the description feature returns a caption and a confidence score, which the newer Image Analysis 4.0 caption feature does not. To obtain confidence scores for captions, you must use the older API version. Other options either use features that lack confidence scores or rely on invalid parameters or workarounds that cannot produce a true confidence value.

Answer

Call the Image Analysis API twice and compare the captions to derive a confidence score.

Answer

Use the denseCaptions feature instead of captions.

Answer

Set the language parameter to 'en' and include the 'confidence' query parameter.

Question 2

You are designing an Azure AI Vision solution that must detect and extract text from identity documents such as passports and driver's licenses. The solution must also identify the document type and extract key fields like name and date of birth. You need to choose the appropriate Azure AI service and features. (Choose two.)

Accepted Answer

Azure AI Document Intelligence with the prebuilt-idDocument model. The prebuilt-idDocument model in Azure AI Document Intelligence is specifically designed to analyze identity documents and extract fields like name and date of birth. A custom extraction model in the same service can also be trained to extract custom fields and classify document types when prebuilt models are insufficient. The Read API and Image Analysis read feature only extract text without structured field extraction, and the prebuilt-receipt model is for receipts, not identity documents.

Answer

Azure AI Vision Read API

Answer

Azure AI Vision Image Analysis with the read feature

Answer

Azure AI Document Intelligence with the prebuilt-receipt model

Question 3

A developer is building an Azure AI Vision solution that must detect and locate multiple objects in an image, such as cars, people, and traffic lights. The solution must return bounding boxes for each detected object. Which Azure AI Vision capability should the developer use?

Accepted Answer

Object detection. Object detection is the correct capability because it identifies and localizes multiple objects in an image, returning bounding boxes and labels for each. Image classification only labels the whole image, OCR extracts text, and face detection is limited to faces. For detecting cars, people, and traffic lights with bounding boxes, object detection is the appropriate Azure AI Vision feature.

Answer

Image classification

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Optical character recognition (OCR)

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Face detection

Question 4

You are designing a solution that uses Azure AI Document Intelligence to extract data from invoices. The solution must classify invoices by vendor and extract line items. Which prebuilt model should you use?

Accepted Answer

Prebuilt invoice model. The Prebuilt invoice model (Option A) is specifically designed to extract common fields from invoices, including vendor details and line items, without requiring custom training. This model is optimized for invoice documents and provides out-of-the-box extraction of structured data such as vendor name, invoice date, and line-item descriptions, quantities, and amounts.

Answer

Custom extraction model

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Prebuilt receipt model

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Prebuilt layout model

Question 5

You are implementing an agent with Azure AI Agent Service that must run a multi-step task: retrieve a customer record, then create a support ticket containing that record. You want the agent to complete both steps in a single run and to be able to report intermediate progress. Which two capabilities should you rely on? (Choose two.)

Accepted Answer

Tool calling, so the model can request the retrieval and ticket-creation functions during the run.. Tool calling lets the model request the customer lookup and the ticket creation as part of one run, while run steps give the application visibility into each invocation and result. Together they enable a single multi-step run with observable progress, which is exactly what the scenario requires.

Answer

File search over the support policy documents, so the agent can justify the ticket priority.

Answer

Vector embeddings of the ticket schema, so the model can match the customer record to the correct ticket fields.

Answer

A separate thread per step, so each tool call is isolated from the others.

Question 6

You are building a web app that allows users to upload images and receive a list of content tags, such as 'outdoor', 'tree', and 'person'. You want to use a prebuilt Azure AI service and minimize custom development. Which service should you use?

Accepted Answer

Azure AI Vision, using the Image Analysis API with the Tags visual feature. Azure AI Vision's Image Analysis API includes a Tags feature that returns content tags for an image without any custom training. This prebuilt capability directly satisfies the need for a list of tags such as 'outdoor', 'tree', and 'person'. Other services either require custom training, operate on text, or are document-focused.

Answer

Azure AI Document Intelligence, using the prebuilt-read model

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Azure AI Custom Vision, using a classification model

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Azure AI Language, using key phrase extraction

Question 7

A company wants to use Azure AI Vision to extract text from scanned documents that contain both printed and handwritten text in multiple languages. The documents are large and can take several minutes to process. The solution must return the extracted text asynchronously. Which Azure AI Vision API should they use?

Accepted Answer

Read API. The Read API in Azure AI Vision is designed for asynchronous extraction of printed and handwritten text from large documents and supports multiple languages. It returns results via a polling operation, which suits documents that take minutes to process. The OCR API is synchronous and limited for handwriting, while the Document Intelligence prebuilt-read model, though capable, is not an Azure AI Vision API.

Answer

Image Analysis API with the read feature

Answer

OCR API

Answer

Document Intelligence prebuilt-read model

Question 8

Which TWO Azure AI services can you use to implement a custom question-answering system?

Accepted Answer

Azure OpenAI Service. Azure OpenAI Service (A) is correct because it lets you build a custom question-answering system by grounding a chat/completions model on your own data (e.g., via the On Your Data feature or by supplying retrieved context), enabling natural-language answers over proprietary content. Azure AI Language (D) is correct because it includes the Custom Question Answering feature (formerly QnA Maker), which builds a knowledge base from documents, URLs, and question-answer pairs and exposes it through a REST API for question answering. Azure AI Bot Service (B) is a framework for hosting and connecting conversational bots, not a question-answering knowledge service, so it does not itself implement custom Q&A. Azure AI Translator (C) only performs text translation between languages and has no question-answering capability. Azure AI Search (E) is a search/indexing service that can retrieve relevant documents but does not generate or manage question-answer pairs on its own, so it is not one of the two services for implementing custom question answering.

Answer

Azure AI Bot Service

Answer

Azure AI Translator

Answer

Azure AI Search

Question 9

You are designing an agent in Microsoft Copilot Studio that must answer questions by using a large corpus of internal policy documents. The agent must provide accurate citations and must not invent policy details. You need to configure knowledge sources and grounding behaviour. Which two actions should you take? (Choose two.)

Accepted Answer

Configure the agent to use generative answers with the knowledge source and review the moderation settings.. Grounding the agent in the policy documents and enabling citations ensures answers trace back to authoritative content, while generative answers with reviewed moderation settings produce compliant responses. High creativity and reliance on pre-trained knowledge both increase the chance of fabricated policy details, and deferring accuracy to post-publication feedback does not meet the requirement.

Answer

Disable the knowledge source and rely on the model's pre-trained policy knowledge.

Answer

Publish the agent immediately and let user feedback correct inaccurate policy statements over time.

Answer

Increase the agent's creativity level to high so it can paraphrase policies more naturally.

Question 10

You are building a mobile app that uses Azure AI Vision to generate captions for photos taken by users. The app must work offline in areas with no internet connectivity. Which Azure AI Vision feature should you use?

Accepted Answer

Azure AI Vision Docker container for Image Analysis. The Azure AI Vision Docker container for Image Analysis allows you to run the captioning feature locally, enabling offline operation. This is the only option that provides the required functionality without internet connectivity, making it the correct choice for a mobile app that must work in areas with no network.

Answer

Azure AI Custom Vision

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Azure AI Vision Read API

Answer

Azure AI Vision Image Analysis API

Question 11

You are building an Azure AI Search knowledge mining pipeline that enriches PDF documents with key phrases. The enrichment must be applied after text extraction and before the data is written to the index. You need to ensure the enriched key phrases are available for downstream skills and are mapped to an index field. Which component of the skillset defines the output of the Key Phrase Extraction skill and its mapping to the index?

Accepted Answer

The outputs array of the skill definition. The outputs array within a skill definition is where you declare the name of the output and the enriched document node it targets. This makes the enriched data available to later skills and to the indexer's output field mappings. The indexer's outputFieldMappings then connects that enriched node to a specific index field. The context property scopes the skill's input, but does not define its output.

Answer

The input parameter of the indexer

Answer

The context property of the skill

Answer

The outputFieldMappings of the indexer

Question 12

A company wants to build a solution that automatically generates alt text for images on their website to improve accessibility. The alt text must be a concise, human-readable description of the image content. Which Azure AI Vision feature should they use?

Accepted Answer

Image Analysis with the Caption feature. The Caption feature in Image Analysis is designed to generate a one-sentence description of an image, which is ideal for alt text. It is prebuilt and requires no training. Other features like Tags or Detect Objects provide keywords or bounding boxes but not a coherent description, making them less suitable for accessibility purposes.

Answer

Custom Vision with a classification model trained on descriptive text

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Image Analysis with the Detect Objects feature

Answer

Image Analysis with the Tags feature

Question 13

You have an Azure AI Search indexer that uses a custom skill hosted in an Azure Function to normalize product codes. The function occasionally returns HTTP 429 responses. You need the indexer to retry these calls automatically without failing the entire indexing run. What should you configure?

Accepted Answer

Implement retry logic inside the Azure Function and return a success response after retries.. Azure AI Search does not provide a retryPolicy on custom skills. When a custom skill returns transient errors such as HTTP 429, the correct pattern is to implement retry logic inside the skill implementation, for example using exponential backoff in the Azure Function, so the indexer receives a successful response once the downstream call succeeds.

Answer

Set the batchSize property on the indexer to a smaller value.

Answer

Add a retryPolicy to the custom skill definition in the skillset.

Answer

Configure the indexer's maxFailedItems and maxFailedItemsPerBatch to tolerate failures.

Question 14

You are building an Azure AI Search solution that enriches documents by detecting the language of each document and then routing content to language-specific analyzers. You add a LanguageDetectionSkill to the skillset and want the detected language code to be available to downstream skills and to be stored in the index. The detected language must be mapped to a field named 'languageCode' in the index. What should you do?

Accepted Answer

Add an outputFieldMapping in the indexer that maps the '/document/languageCode' enrichment node to the 'languageCode' index field.. The LanguageDetectionSkill outputs a language code under the enriched document, but that value only reaches the index if the indexer maps it. Output field mappings declare which enrichment nodes become index field values. Configuring analyzers or field attributes changes how data is stored or queried, not whether it is stored. A custom skill is unnecessary because the built-in skill already emits the required value.

Answer

Configure the LanguageDetectionSkill with a 'defaultLanguageCode' parameter and set the index field 'languageCode' to use the 'fr.lucene' analyzer.

Answer

Create a custom skill that calls the Azure AI Language service and writes the detected language directly into the index using the Azure AI Search REST API.

Answer

Set the 'languageCode' field in the index to be retrievable and filterable, and rely on the indexer to automatically populate it from the skill output.

Question 15

You are designing a solution that uses Azure AI Language's conversational language understanding (CLU) to interpret user requests in a banking app. The app must handle utterances like 'Transfer $500 from savings to checking' and extract the amount, source account, and destination account. Which two actions should you perform when creating the CLU project? (Choose two.)

Accepted Answer

Create entities for 'Amount', 'SourceAccount', and 'DestinationAccount'.. To correctly interpret the banking utterances, you must define intents to capture the user's goal and entities to extract the specific data fields. Intents like 'TransferMoney' and entities for 'Amount', 'SourceAccount', and 'DestinationAccount' are fundamental. Prebuilt entities alone lack specificity, and custom training is required.

Answer

Train the model using only the default training set provided by Azure.

Answer

Add a prebuilt entity for 'Number' to automatically extract the amount.

Answer

Configure the project to use multiple languages for utterance interpretation.