AI-102 Practice Test 50 — 15 Questions

Question 1

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.. Option A is correct because a tagging profile with a weight boosts documents based on matching tags from a specific field. Option B is wrong because a distance profile is for geospatial scoring. Option C is wrong because a magnitude profile is for boosting based on numeric values. Option D is wrong because a freshness profile boosts based on date/time.

Answer

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

Answer

Create a distance scoring profile with a reference point parameter.

Answer

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

Question 2

You are using Azure AI Language to extract information from medical research papers. You need to identify terms like 'dosage', 'side effects', and 'contraindications' specific to the medical domain. Which capability should you use?

Accepted Answer

Custom Named Entity Recognition (NER). Option B is correct because custom Named Entity Recognition allows you to train a model to recognize custom entities like medical terms. Option A is wrong because prebuilt NER only recognizes general entities like person, location, etc. Option C is wrong because entity linking links to external knowledge bases. Option D is wrong because PII detection is for personal information.

Answer

Prebuilt Named Entity Recognition (NER)

Answer

PII detection

Answer

Entity linking

Question 3

You need to enrich documents with key phrases and sentiment before indexing into Azure AI Search. Which type of skill should you use?

Accepted Answer

Cognitive Services skill. Key phrase extraction and sentiment analysis are cognitive skills available in the Azure AI Language service. The Cognitive Services skill references a Cognitive Services resource that provides these capabilities.

Answer

Entity Recognition skill

Answer

Document Extraction skill

Answer

Custom Web API skill

Question 4

Which TWO Azure AI Search features are used to map skill outputs to search index fields? (Select TWO.)

Accepted Answer

outputFieldMappings. outputFieldMappings map outputs from skills to index fields. Field mappings map source data fields to index fields directly. Skillset outputs are intermediate; indexer outputs are final.

Answer

indexer parameters

Answer

skillset outputs

Answer

fieldMappings

Question 5

Refer to the exhibit. You execute a search query on an Azure AI Search index and get these results. The query was 'brown fox'. Why is the first result scored higher than the second?

Accepted Answer

The first document has a higher term frequency and better term proximity for the query terms. Option A is correct. The default scoring algorithm uses TF-IDF, which gives higher scores to documents where the query terms appear more frequently. The first document contains 'brown' and 'fox' exactly, while the second uses 'brown' and 'fox' but also has 'fast' and 'leaps', but the exact phrase match is stronger. Actually, the first document has the exact phrase 'brown fox' while the second has 'brown fox' separated. The first document likely has a higher term frequency or better proximity. Option B is wrong because there is no semantic ranking configured. Option C is wrong because there is no scoring profile defined. Option D is wrong because the index is not using vector search.

Answer

The first document has a higher value in a scoring profile field

Answer

The first document is more similar to the query in vector space

Answer

The first document was boosted by a semantic ranking function

Question 6

Which THREE considerations are important when designing a custom skill for Azure AI Search that calls an external API for specialized data extraction?

Accepted Answer

The API endpoint must be reachable from the search service. Options B, C, and D are correct. Custom skills must have a timeout of 230 seconds (default), the API must be accessible from the search service (public or via private endpoint), and data size limits apply (payload up to 16 MB). Option A is incorrect because custom skills can be written in any language that supports JSON. Option E is incorrect because the skill can have multiple inputs and outputs.

Answer

The skill can only accept one input and produce one output

Answer

The skill must be written in Python

Question 7

You are deploying an Azure AI Search solution that indexes medical research papers. The papers contain sensitive patient data that must be de-identified before indexing. You need to use Azure AI Services to detect and redact personal information. Which combination of skills should you include in a skillset?

Accepted Answer

PII detection skill. Option D is correct because the PII detection skill identifies and redacts personal information from text. The Text Translation skill is not relevant. The Entity Recognition skill does not redact. The Custom Entity Lookup skill requires a predefined list and does not redact.

Answer

Custom Entity Lookup skill and Sentiment skill

Answer

Text Translation skill and Entity Recognition skill

Answer

Entity Recognition skill and Key Phrase Extraction skill

Question 8

You are using Azure AI Document Intelligence to extract data from scanned contracts. The contracts contain tables and handwritten signatures. Which TWO features should you enable?

Accepted Answer

Train a custom neural model to recognize handwritten signatures.. Options A and C are correct. The neural model handles handwriting (A), and the table extraction capability (C) extracts tables. Option B is wrong because the prebuilt-layout model is not custom-trained. Option D is wrong because form recognition is part of Document Intelligence but not specific to contracts. Option E is wrong because OCR is built-in and not a separate feature to enable.

Answer

Enable OCR to read scanned text.

Answer

Use form recognition to capture key-value pairs.

Answer

Use the prebuilt-layout model for all extraction.

Question 9

You are designing a knowledge mining solution using Azure AI Search. The solution must process large volumes of PDFs daily. You need to minimize the cost of cognitive skills execution while ensuring the pipeline can handle transient failures. Which approach should you recommend?

Accepted Answer

Enable incremental enrichment on the indexer. Option A is correct because enabling incremental enrichment caches intermediate results and recovers from failures without re-processing unchanged documents, reducing cost. Option B is incorrect because increasing the number of replicas improves query performance, not indexing. Option C is incorrect because disabling field mappings would break the pipeline. Option D is incorrect because using a free tier is not feasible for large volumes.

Answer

Disable field mappings

Answer

Increase the number of replicas

Answer

Use the free tier for the indexer

Question 10

Your organization is building a knowledge base from technical manuals stored in multiple formats (PDF, Word, HTML). You need to extract text and images from these documents and create a searchable index. The solution must handle tables and preserve their structure. Which approach should you use?

Accepted Answer

Use Azure AI Document Intelligence layout model as a custom skill. Option D is correct because Azure AI Document Intelligence with layout model extracts text, tables, and structure from documents. Option A is incorrect because Azure AI Vision OCR extracts text but not table structure. Option B is incorrect because Azure AI Language extracts entities but not tables. Option C is incorrect because Azure AI Search alone cannot extract content.

Answer

Upload documents directly to Azure AI Search

Answer

Use Azure AI Language custom entity extraction

Answer

Use Azure AI Vision OCR skill in the skillset

Question 11

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. Option A is correct because semantic search improves relevance by understanding natural language queries and providing answer-style results. Synonyms (B) help with query expansion but not natural language understanding. Knowledge mining (C) is a broader process. Cognitive skills (D) are for enrichment, not query-time interpretation.

Answer

Synonym maps

Answer

Cognitive skills

Answer

Knowledge mining

Question 12

Refer to the exhibit. You have this skillset definition for an Azure AI Search enrichment pipeline. You notice that the entity recognition skill is not executing on any document. What is the most likely cause?

Accepted Answer

The input source path in the entity recognition skill should be relative to the context, not absolute. Option C is correct. The context of the EntityRecognitionSkill is '/document/pages/*', but the SplitSkill outputs 'pages' at '/document/pages'. However, the SplitSkill's output is named 'pages' but the context for the split skill is '/document', so the output path is '/document/pages'. The entity recognition skill context '/document/pages/*' would iterate over each element in '/document/pages', but if the split skill's output is not an array, the iteration fails. Actually, the split skill outputs 'textItems' as an array, but the target name is 'pages', so the output is at '/document/pages', which is an array. The context '/document/pages/*' should work. However, the issue is that the split skill's output is 'textItems' but the target name is 'pages', so the actual output node is '/document/pages'. The entity recognition skill inputs source '/document/pages/*' expects each page's content, but the input field name is 'text' and source is correct. Another potential issue is that the split skill's output is not being passed correctly because the entity recognition skill is not referencing the correct output from the split skill. Actually, the correct output should be '/document/pages' which is an array of strings. The entity recognition skill context '/document/pages/*' should iterate over each page. But the entity recognition skill's input source is '/document/pages/*', which is incorrect because that would be the element itself, not the text content. The input source should be '/document/pages/*' to get the text of each page. However, the split skill outputs the text items as strings, so '/document/pages/*' would be the string content. That should work. Wait, the exhibit shows the split skill output target name 'pages', so the output node is '/document/pages' (array). The entity recognition skill input source is '/document/pages/*', which is the individual page string. That seems correct. However, the entity recognition skill expects a 'text' input, and the source is '/document/pages/*' which is the page text. So why would it not execute? Possibly because the language code source '/document/language' is not present in the document. But that would cause an error for the split skill too. Another reason could be that the entity recognition skill requires the language code to be provided, and if it's missing, the skill fails. But the question says 'not executing on any document', implying it never runs. The most likely cause is that the split skill is not producing pages because the maximumPageLength might be too large and the content is short, but that would still produce one page. Actually, the split skill will always produce at least one page. The exhibit shows the skillset, but the entity recognition skill context is '/document/pages/*' which is correct. However, the entity recognition skill might fail if the language code is invalid. But the most common mistake is that the entity recognition skill's context is set to '/document/pages/*', but the input source is '/document/pages/*', which is the same as context, leading to no iteration. Actually, the context defines the iteration, and the input source should be the property of that context, not the context itself. For example, if context is '/document/pages/*', then input source should be relative to that context, like 'text' if the page object had a 'text' property. But here, the input source is '/document/pages/*' which is an absolute path that points to the same node as the context, so it might cause a conflict. In Azure AI Search, the input source should be a path relative to the context or absolute. If the source is absolute and points to the same node as the context, it might not work as expected because the skill expects the input to be a scalar value, but the context is an array element. This is a known issue. Option C states that the input source path should be relative to the context, not absolute. That is the correct answer.

Answer

The entity recognition skill requires a language code that is missing

Answer

The split skill is not producing pages because the content is too short

Answer

The entity recognition skill is not registered in the skillset

Question 13

You are a developer at an e-commerce company. The company wants to build a product search feature that allows customers to search for products using natural language phrases like "red running shoes under $100". The product catalog is stored in Azure Cosmos DB and includes product descriptions, prices, and categories. The solution must use Azure AI Search and must extract entities from product descriptions to enable filtering (e.g., color, size, brand). The search must also support fuzzy matching for misspelled queries. You need to design the indexing pipeline. Which actions should you take?

Accepted Answer

Use Azure AI Language entity extraction as a custom skill, and enable fuzzy search in the index. Option C is correct. Using Azure AI Language for entity extraction extracts attributes like color and brand, and enabling fuzzy search in the index handles misspellings. Option A is incorrect because Azure AI Document Intelligence is for document extraction, not text from a database. Option B is incorrect because OCR is for images. Option D is incorrect because the Language service does not perform vector search.

Answer

Use Azure AI Language key phrase extraction, and enable vector search

Answer

Use Azure AI Document Intelligence to extract entities, and enable semantic ranking

Answer

Use Azure AI Vision OCR to extract text, and enable synonyms

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.. Option A is correct because custom question answering can import FAQ content from a Word document using the 'Import' feature. Option B is wrong because prebuilt question answering does not support document import. Option C is wrong because conversational language understanding is for intent classification, not FAQ import. Option D is wrong because Document Intelligence is for form extraction, not QnA.

Answer

Use Azure AI Document Intelligence to extract QnA pairs.

Answer

Use the prebuilt question answering API to parse the document.

Answer

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

Question 15

You are building a knowledge mining solution for a legal firm that needs to extract key clauses from thousands of scanned contract PDFs. The solution must identify parties, effective dates, and termination conditions. Which Azure AI service should you use as the primary component?

Accepted Answer

Azure AI Document Intelligence. Option B is correct because Azure AI Document Intelligence (formerly Form Recognizer) is designed for extracting structured information from documents using prebuilt and custom models. Option A is incorrect because Azure AI Language is for text analytics and NLP but not optimized for scanned documents. Option C is incorrect because Azure AI Search is for indexing and searching, not extraction. Option D is incorrect because Azure AI Vision is for image analysis, not document extraction.

Answer

Azure AI Vision

Answer

Azure AI Language

Answer

Azure AI Search