AI-102 Practice Test 21 — 15 Questions

Question 1

You need to analyze customer feedback to determine whether the sentiment is positive, negative, or neutral. Which Azure AI service should you use?

Accepted Answer

Azure AI Language - Sentiment Analysis. Azure AI Language's Sentiment Analysis is the correct service because it is specifically designed to evaluate text and return sentiment labels (positive, negative, neutral) along with confidence scores. This directly matches the requirement to determine whether customer feedback sentiment is positive, negative, or neutral.

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Azure AI Language - Key Phrase Extraction

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Azure AI Language - Named Entity Recognition

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Azure AI Language - Language Detection

Question 2

A company is building a chatbot that must handle user queries in multiple languages. The chatbot uses Azure AI Language Service. Which feature should be used to detect the language of incoming messages before routing them to the appropriate language model?

Accepted Answer

Language Detection. Language Detection is the correct feature because it is specifically designed to identify the language of text input, returning a language name and a confidence score. In a multi-language chatbot, this detection step is essential to route the query to the appropriate language-specific model or handler. Azure AI Language Service provides a dedicated pre-built capability for language detection, which can be called via the REST API or SDK.

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Sentiment Analysis

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Key Phrase Extraction

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Entity Recognition

Question 3

Which THREE are valid uses of Azure AI Vision Image Analysis 4.0? (Select three.)

Accepted Answer

Extract printed text from an image using OCR. Option A is correct because Image Analysis 4.0 includes a Read OCR feature that extracts printed and handwritten text from images and documents, returning lines and words. Option C is correct because the object detection capability identifies objects in an image and returns their coordinates as bounding boxes along with confidence scores. Option D is correct because Image Analysis 4.0 can generate captions (and dense captions) describing the content of an image in natural language. Option B is not valid because audio transcription is a speech service capability (e.g., Azure AI Speech), not Image Analysis. Option E is not valid because translating text is performed by Azure AI Translator; Image Analysis only extracts the text via OCR, it does not translate it.

Answer

Transcribe spoken audio from a video file

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Translate text found in an image to another language

Question 4

Which THREE components are part of the Azure AI Bot Service?

Accepted Answer

Azure Bot Service (hosting). Azure Bot Service (hosting) (B) is correct because the Azure AI Bot Service provides the hosting/registration resource that exposes a messaging endpoint and connects bots to channels such as Microsoft Teams, Web Chat, and Direct Line. Bot Framework SDK (D) is correct because it is the core development library (for C#, JavaScript/TypeScript, Python, and Java) used to build bots that handle activities, turns, dialogs, and channel connectors. Bot Framework Composer (E) is correct because it is the visual authoring tool included in the Bot Framework tooling for designing, testing, and publishing conversational bots. Azure AI Search (A) is not part of the Bot Service; it is a separate cognitive search service for indexing and querying content. Azure AI Language (CLU) (C) is also not part of the Bot Service; it is a separate Azure AI Language capability for conversational language understanding that a bot may call but that is not a Bot Service component.

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Azure AI Search

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Azure AI Language (CLU)

Question 5

Which THREE factors should be considered when choosing a region for deploying Azure AI services?

Accepted Answer

Service availability and feature support.. Service availability and feature support (Option B) is a critical factor because not all Azure AI services are available in every region; for example, certain Cognitive Services like Azure OpenAI or Computer Vision OCR may be in preview or fully supported only in specific regions. Choosing a region without the required service or feature would prevent deployment or limit functionality, directly impacting solution design.

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Number of Azure data centers in the region.

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Cost of the services in each region.

Question 6

You are using the Azure AI Language service to process customer reviews. You need to extract the following insights: overall sentiment, key phrases, and entity types (such as product names). Which THREE operations should you call?

Accepted Answer

Key Phrase Extraction. Sentiment Analysis (C) is correct because it returns the overall sentiment of the review (positive, negative, neutral, or mixed) along with confidence scores, which is exactly the first insight required. Key Phrase Extraction (A) is correct because it identifies the main talking points in the review text, satisfying the key phrases requirement. Entity Recognition (E) is correct because it extracts and classifies named entities such as product names, locations, and organizations, which covers the entity types requirement. Language Detection (B) is not needed because the scenario does not ask for identifying the review's language, and PII Detection (D) is not needed because the scenario does not require detecting or redacting personal information.

Answer

Language Detection

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PII Detection

Question 7

A company is building a knowledge mining solution using Azure AI Search. They need to extract key phrases from a large set of documents in multiple languages. Which skill should they 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 the most important phrases from text, which directly supports the requirement to extract key phrases from documents. Azure AI Search's built-in Key Phrase Extraction skill leverages natural language processing to analyze text and return a list of key phrases, making it the appropriate skill for this knowledge mining solution.

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Sentiment Analysis skill

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Language Detection skill

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Entity Recognition skill

Question 8

You are developing a solution that uses Azure AI Language to analyze customer feedback. You need to determine whether the sentiment of a given sentence is positive, negative, or neutral. Which Azure AI Language feature should you use?

Accepted Answer

Sentiment Analysis. Sentiment Analysis is the correct Azure AI Language feature because it is specifically designed to evaluate text and determine whether the sentiment expressed is positive, negative, or neutral. This feature uses machine learning classifiers trained on large datasets to assign a sentiment label and confidence scores at the sentence and document level, directly matching the requirement to analyze customer feedback for sentiment polarity.

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Entity Recognition

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Language Detection

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Key Phrase Extraction

Question 9

Which TWO actions should you take to optimize a custom text classification model in Azure Cognitive Service for Language?

Accepted Answer

Ensure that training examples for different labels do not have overlapping content.. Overlapping content between labels (e.g., the same text appearing in both 'positive' and 'negative' training examples) confuses the custom text classification model, leading to poor decision boundaries. Azure Cognitive Service for Language uses a multi-class or multi-label classifier that learns distinct patterns for each label; overlapping content introduces ambiguity, reducing precision and recall. Ensuring distinct, non-overlapping training examples per label helps the model learn clear, separable features.

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Oversample the minority classes to balance the dataset.

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Remove all stop words from the training data.

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Remove examples with neutral sentiment to focus on positive and negative classes.

Question 10

Your company uses Azure Cognitive Search to index millions of documents. Users report that search results include irrelevant documents. You need to improve search relevance by boosting documents that contain the search term in the title field. Which scoring profile configuration should you use?

Accepted Answer

Create a tagging scoring profile that boosts by the title field with a weight of 10.. A tagging scoring profile boosts documents based on matching tags from a specific field (like title), and the weight parameter controls the boost magnitude. Option B is incorrect because a freshness scoring profile boosts by recency, not by term presence in a field. Option C is incorrect because a distance scoring profile boosts based on geospatial proximity, not text fields. Option D is incorrect because a magnitude scoring profile boosts based on numeric field values, not text fields; a linear boosting function is used for magnitude profiles, but this does not apply to boosting by title.

Answer

Create a freshness scoring profile with a boosting duration of 30 days.

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Create a distance scoring profile with a reference point parameter.

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Create a magnitude scoring profile with a boosting function of 'linear'.

Question 11

A development team is using Azure Cognitive Service for Language to extract key phrases from customer reviews. They notice that some reviews are not being processed, and the API returns a 400 error code. What is the most likely cause?

Accepted Answer

One of the reviews exceeds the maximum character limit for a single document.. The Azure Cognitive Service for Language key phrase extraction API enforces a maximum document size of 5,120 characters per document. When a single review exceeds this limit, the API returns a 400 Bad Request error because the request payload violates the service's input constraints. This is the most common cause of 400 errors in batch text analysis operations.

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The reviews contain characters that are not valid UTF-8.

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The request contains more than 5 documents.

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The reviews are written in a language not supported by the service.

Question 12

You are developing an application that processes images of handwritten forms. The forms contain checkboxes that may be checked or unchecked. Which Azure AI service should you use to detect the state of the checkboxes?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is the correct service because it is specifically designed to extract structured data from documents, including detecting the state of checkboxes (checked or unchecked) in forms. It uses prebuilt models like the 'prebuilt-document' or custom extraction models to analyze form fields and checkbox selections, making it the optimal choice for this task.

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Azure AI Custom Vision

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Azure AI Language

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Azure AI Computer Vision

Question 13

You are designing an enterprise search solution using Azure AI Search. The solution must index data from multiple sources: SQL Database, SharePoint Online, and custom REST APIs. The search index must support faceted navigation and filtering by metadata such as department and document type. You also need to ensure that updates to source data are reflected in the index within 5 minutes. Which approach should you use?

Accepted Answer

Configure indexers for SQL and SharePoint, and use the push API for the REST API. Schedule indexers to run every 5 minutes.. It combines the strengths of indexers (for SQL Database and SharePoint Online, which have native connectors) with the push API for custom REST APIs, which lack a built-in indexer. Scheduling the indexers to run every 5 minutes ensures that updates are reflected within the required latency window, while the push API can be triggered on demand or via a polling mechanism to meet the same 5-minute SLA.

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Use the push API to index all data from a custom application that polls all sources.

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Create a single indexer that reads from all three sources using a data source definition.

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Use only indexers for all sources by creating a custom indexer for the REST API.

Question 14

You are building a question answering solution using Azure AI Language. You have a set of frequently asked questions (FAQs) in a Word document. You need to import the FAQs into a project. Which approach should you use?

Accepted Answer

Create a custom question answering project and import the Word document as a source.. Azure AI Language's custom question answering feature allows you to create a project and import a Word document directly as a source, automatically extracting question-and-answer pairs. This is the intended approach for ingesting FAQ documents into a knowledge base.

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Use Azure AI Document Intelligence to extract QnA pairs.

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Use the prebuilt question answering API to parse the document.

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Use conversational language understanding (CLU) to extract intents and entities.

Question 15

You need to use Azure AI Language to analyze customer feedback. Which THREE analysis types are available in the Text Analytics API?

Accepted Answer

Sentiment analysis. Sentiment analysis (C) is a core Text Analytics API feature that returns sentiment labels and confidence scores (positive, neutral, negative, mixed) for documents or sentences, making it valid for analyzing customer feedback. Entity recognition (D), specifically Named Entity Recognition (NER), is also available and identifies entities such as people, places, organizations, and dates in text. Key phrase extraction (E) is another supported Text Analytics capability that returns the main concepts or topics in a document, which is useful for summarizing customer feedback. Image captioning (A) belongs to Azure AI Vision, not Azure AI Language, and speech-to-text (B) is provided by Azure AI Speech, so neither is part of the Text Analytics API.

Answer

Image captioning

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Speech-to-text