AI-102 Implement knowledge mining and information extraction solutions Practice Test 9 — 15 Questions

Question 1

Refer to the exhibit. You have this Azure AI Search indexer configuration. The indexer is failing after processing 6 documents that contain errors. What should you do to ensure the indexer continues processing even if some documents fail?

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

Accepted Answer

Increase maxFailedItems to a higher value, such as 100. The indexer is failing after processing 6 documents because the `maxFailedItems` threshold has been reached. By increasing `maxFailedItems` to a higher value like 100, the indexer will continue processing even if more documents fail, as long as the total number of failed items stays below the new threshold.

Answer

Decrease the batch size to 5

Answer

Increase batch size to 20

Answer

Remove the schedule to run the indexer on demand

Question 2

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. The default scoring algorithm in Azure AI Search uses term frequency (TF) and term proximity. The first document likely has a higher term frequency for 'brown' and 'fox', and the terms are closer together (e.g., appearing as a phrase), resulting in a higher score. Option D correctly identifies this. Option A is incorrect because no scoring profile is mentioned. Option B is incorrect because vector search is not used. Option C is incorrect because no semantic ranking is configured.

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 3

You are building a solution to extract key information from invoices using Azure AI Document Intelligence. The invoices contain fields such as invoice number, date, total amount, and line items. However, the model is not correctly extracting the line items. Which prebuilt model should you use?

Accepted Answer

Prebuilt-invoice model. The prebuilt-invoice model is specifically trained to extract key fields from invoices, including invoice number, date, total amount, and line items. Unlike other prebuilt models, it has dedicated field extraction for line item details such as description, quantity, unit price, and total, making it the correct choice for this scenario.

Answer

Prebuilt-receipt model

Answer

Prebuilt-idDocument model

Answer

Prebuilt-layout model

Question 4

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.

Answer

Create a distance scoring profile with a reference point parameter.

Answer

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

Question 5

You are using Azure AI Language to perform entity recognition on customer feedback. You need to identify the sentiment expressed towards specific entities. Which feature should you use?

Accepted Answer

Sentiment analysis with opinion mining. Sentiment analysis with opinion mining is the correct feature because it not only detects the overall sentiment of a text but also associates specific sentiments with particular entities or aspects mentioned in the text. This allows you to determine, for example, that a customer feels positively about 'product quality' but negatively about 'customer support', which is exactly what the question requires.

Answer

Named Entity Recognition (NER)

Answer

Entity linking

Answer

Key phrase extraction

Question 6

You are using Azure AI Document Intelligence to process a large batch of PDF forms. The forms have varying layouts and handwriting. You need to extract text and key-value pairs. Which custom model type should you train?

Accepted Answer

Custom neural model. Custom neural model. Azure AI Document Intelligence offers custom template models for forms with fixed layouts and custom neural models for forms with varying layouts and handwriting. Neural models use deep learning to handle variability in structure and handwriting, making them ideal for this scenario. Option A (Custom template model) assumes a fixed layout and fails with varying layouts. Option B (Prebuilt-layout model) is a prebuilt model that extracts text, tables, and selection marks but not customized key-value pairs for your forms. Option D (Custom composed model) is a combination of multiple models, but the primary choice for varying layouts is the neural model, not composed. Therefore, C is the best choice.

Answer

Custom template model

Answer

Prebuilt-layout model

Answer

Custom composed model

Question 7

Your company has a large set of PDF documents stored in Azure Blob Storage. You need to index these documents in Azure Cognitive Search so that users can search the text content. What is the first step you should take?

Accepted Answer

Create a data source that connects to Azure Blob Storage.. The correct first step is to create a data source that connects to Azure Blob Storage (option C). In Azure Cognitive Search, the indexing pipeline requires a data source definition to specify the location and type of data. After creating the data source, you can then create an index, and finally an indexer to automate the process. Creating a skillset (option B) or an indexer running daily (option D) would come later. Option A is incorrect because the index fields are defined when you create the index, which is not the first step.

Answer

Create an index with a field for each metadata property.

Answer

Create a skillset to extract text from PDFs.

Answer

Create an indexer that runs daily.

Question 8

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). 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 PII detection is for personal information. Option D is wrong because entity linking links to external knowledge bases.

Answer

Prebuilt Named Entity Recognition (NER)

Answer

PII detection

Answer

Entity linking

Question 9

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.. Custom question answering in Azure AI Language allows importing FAQ content from Word documents as a source directly. Option A is incorrect because Azure AI Document Intelligence is designed for extracting structured data from documents, not specifically for QnA pair import into a question answering project. Option C is incorrect because the prebuilt question answering API is for out-of-the-box QA, not for custom document import. Option D is incorrect because conversational language understanding (CLU) is used for intent and entity extraction, not for importing FAQs.

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 10

You are designing an Azure Cognitive Search solution that indexes customer support tickets. The index must include a field for 'sentiment' that is populated from an AI enrichment pipeline. Which TWO actions are required to achieve this?

Accepted Answer

Add a built-in Sentiment skill to the skillset.. Options A and E are correct. The enrichment pipeline must include a built-in Sentiment skill (A) to analyze sentiment from the support ticket text, and an output field mapping must be configured in the indexer (E) to map the skill's output to the 'sentiment' field in the index. Option B is wrong because a custom skill is not needed as the sentiment skill is built-in. Option C is wrong because the built-in sentiment skill uses Azure AI Language, so a custom skill is unnecessary. Option D is wrong because Collection(Edm.String) is not the correct type for sentiment; a simple string or numeric type would be appropriate.

Answer

Implement a custom skill to normalize sentiment values.

Answer

Create a custom sentiment analysis skill using Azure AI Language.

Answer

Define a 'sentiment' field in the index with type Collection(Edm.String).

Question 11

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 B are correct because a custom neural model can be trained to recognize handwritten signatures (A), and enabling table extraction in the custom model extracts tables from contracts (B). Option C is incorrect because OCR is already enabled by default in Document Intelligence and does not need to be separately enabled. Option D is wrong because form recognition for key-value pairs is not required for this scenario, as the focus is on tables and signatures. Option E is wrong because the prebuilt-layout model does not support custom training for signature recognition and may not handle handwritten elements as accurately as a custom model.

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 12

You are building a knowledge mining solution that uses Azure Cognitive Search and Azure AI Language. The solution must extract key phrases and detect the language of documents. Which THREE components are required?

Accepted Answer

A search index that contains fields for the extracted data.. A search index is the destination where extracted data (key phrases and language) must be stored for querying. Without an index, the extracted information has no structured location to be persisted and made searchable. The index schema must include fields specifically mapped to the output of the skillset's Key Phrase Extraction and Language Detection skills.

Answer

A custom skill to combine key phrases and language.

Answer

An indexer that runs on a schedule.

Question 13

You are a data scientist for Contoso Pharmaceuticals. The company has thousands of research documents in PDF format stored in Azure Blob Storage. You need to build an Azure Cognitive Search solution that enables researchers to search for documents based on chemical compound names, disease mentions, and experimental results. The solution must extract these entities using a custom AI model built in Azure AI Language. Additionally, the solution must support semantic search for natural language queries. The search index must be updated daily with new documents. You have an existing Azure AI Language custom entity extraction model that recognizes chemical compounds and diseases. The model is deployed as an endpoint. You need to configure the enrichment pipeline. What should you do?

Accepted Answer

Create a custom skill in the skillset that calls the custom entity extraction endpoint via HTTP.. To integrate a custom AI model from Azure AI Language into an Azure Cognitive Search enrichment pipeline, you need to create a custom skill in the skillset that calls the custom entity extraction endpoint via HTTP. The built-in Entity Recognition skill only supports prebuilt models and cannot be configured to use a custom model endpoint. Deploying the model to Azure AI Document Intelligence and using a Document Intelligence skill is not appropriate because the model is already deployed in Azure AI Language as a custom entity extraction model. Field mappings in the indexer are for direct field-to-field mappings from the data source to the index, not for calling external AI services. Therefore, Option A is the correct approach.

Answer

Deploy the custom model to Azure AI Document Intelligence and use a Document Intelligence skill.

Answer

Add the custom entity extraction as a field mapping in the indexer.

Answer

Use the built-in Entity Recognition skill and configure it to use your custom model endpoint.

Question 14

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. Azure AI Document Intelligence (formerly Form Recognizer) is the appropriate service for extracting structured data like parties, effective dates, and termination conditions from scanned contract PDFs. It offers prebuilt models for contracts and custom extraction capabilities. Azure AI Language is for text analytics and NLP but lacks the OCR and layout understanding needed for scanned documents. Azure AI Vision provides OCR but not structured extraction. Azure AI Search is for indexing and searching, not extraction. Therefore, Option A is correct.

Answer

Azure AI Vision

Answer

Azure AI Language

Answer

Azure AI Search

Question 15

A company uses Azure AI Search to index customer support tickets. They need to automatically extract key phrases from each ticket to improve search relevance. Which built-in skill should they add to the skillset?

Accepted Answer

Key Phrase Extraction. The Key Phrase Extraction skill extracts key phrases from text, making it the correct choice for automatically extracting key phrases from customer support tickets. Entity Recognition identifies named entities like people, places, and organizations, not key phrases. Sentiment Analysis determines the sentiment (positive, negative, neutral) of text. OCR extracts text from images, not from text-based tickets.

Answer

Entity Recognition

Answer

Sentiment Analysis

Answer

OCR