AI-102 Exam Dumps - Microsoft Azure AI Engineer Associate Questions and Answers

You need to minimize indexing time for 10 GB of scanned docs and images.

Azure Cognitive Search supports parallel indexing to increase throughput by partitioning work.

A text-based indexer or scheduled indexing will not reduce build time.

Field mappings help transform fields but don’t affect indexing speed.

Therefore, the correct way to speed up index building is parallel indexing.

Correct Answer: A

Inclusiveness: AI systems should empower everyone and engage people.

Direct Line Speech is a robust, end-to-end solution for creating a flexible, extensible voice assistant. It is powered by the Bot Framework and its Direct Line Speech channel, that is optimized for voice-in, voice-out interaction with bots.

In Question Answering (Custom question answering in Azure AI Language), you improve coverage for different user phrasings by adding alternative questions to the same answer. For example, adding “How much does cost?” as an alternate question to the existing Q/A about “What is the price of ?” enables the service to match both user queries to the same answer. After adding alternates, you must retrain and republish the project for changes to take effect. This directly addresses the gap and meets the goal.

References

Add and manage alternative questions in custom question answering; retrain/republish for updates.https://learn.microsoft.com/azure/ai-services/language-service/question-answering/how-to/contribute-to-project

Custom question answering concepts and authoring workflow.https://learn.microsoft.com/azure/ai-services/language-service/question-answering/overview

Data Analyst.

When dealing with data roles in an organization, responsibilities are divided among data analysts, data engineers, and data scientists. Let’s review them:

Data Analyst

Focuses on understanding business requirements and ensuring that the right rules and logic are applied to data so it becomes meaningful for decision-making.

They work closely with stakeholders to define business rules such as data validation rules, aggregation logic, and reporting metrics.

This makes them responsible for identifying which business rules must be applied to data.

Data Engineer

Responsible for designing, building, and maintaining the infrastructure and pipelines that move and process data.

They ensure data availability, reliability, and scalability, but do not usually define business rules.

Data Scientist

Focuses on advanced analytics, predictive modeling, and machine learning.

They use data prepared by engineers and analysts but are not typically the ones who define the business rules that govern how raw data should be interpreted.

Therefore, the correct answer is Data Analyst.

Correct Answer: A data analyst

Data roles and responsibilities in Azure

What is a Data Analyst?

Responsibilities of Data Engineers vs Data Scientists vs Data Analysts

Microsoft References

Explanation:

Scenario: When the response confidence score is low, ensure that the chatbot can provide other response options to the customers.

When no good match is found by the ranker, the confidence score of 0.0 or "None" is returned and the default response is "No good match found in the KB". You can override this default response in the bot or application code calling the endpoint. Alternately, you can also set the override response in Azure and this changes the default for all knowledge bases deployed in a particular QnA Maker service.

Choosing Ranker type: By default, QnA Maker searches through questions and answers. If you want to search through questions only, to generate an answer, use the RankerType=QuestionOnly in the POST body of the GenerateAnswer request.

In Custom Question Answering (part of Azure AI Language), if you want to define alternative words or spellings for a specific term (e.g., testing, trials, evaluate), you must use the alterations property.

Each alteration object contains a list of alternative values for the same meaning.

Example:

{

"alterations": [

{

"alterations": [

"testing",

"trials",

"evaluate"

]

}

]

}

phrases is used for pattern matching in question-answer pairs.

synonyms is used for synonyms within LUIS (Language Understanding), not for QnA alterations.

value is used inside synonyms definitions in some contexts but not here.

Correct JSON Payload Completion:The JSON should be:

{

"alterations": [

{

"alterations": [

"testing",

"trials",

"evaluate"

]

}

]

}

Verified Answer:

Left box: "alterations"

Right box: "alterations"

Microsoft References:

Manage alterations in Custom Question Answering (Azure AI Language)

Authoring API reference for alterations

Can search content in different formats, including video

Audio and video insights (multi-channels). When indexing by one channel, partial result for those models will be available.

Keywords extraction: Extracts keywords from speech and visual text.

Named entities extraction: Extracts brands, locations, and people from speech and visual text via natural language processing (NLP).

Topic inference: Makes inference of main topics from transcripts. The 2nd-level IPTC taxonomy is included.

Artifacts: Extracts rich set of "next level of details" artifacts for each of the models.

Sentiment analysis: Identifies positive, negative, and neutral sentiments from speech and visual text.

C: Analyze Receipt - Get Analyze Receipt Result.

Query the status and retrieve the result of an Analyze Receipt operation.

Request URL: https://{endpoint}/formrecognizer/v2.0-preview/prebuilt/receipt/analyzeResults/{resultId}

E: POST {Endpoint}/vision/v3.2/read/analyze

Use this interface to get the result of a Read operation, employing the state-of-the-art Optical Character Recognition (OCR) algorithms optimized for text-heavy documents.

Scenario: Contoso plans to develop a document processing workflow to extract information automatically from PDFs and images of financial documents

The document processing solution must be able to process standardized financial documents that have the following characteristics:

- Contain fewer than 20 pages.

- Be formatted as PDF or JPEG files.

- Have a distinct standard for each office.

*The document processing solution must be able to extract tables and text from the financial documents.

The document processing solution must be able to extract information from receipt images.

Box 1: "categories": ["Locations", "Persons", "Organizations"],

Locations, Persons, Organizations are in the outputs.

Scenario: Contoso plans to develop a searchable knowledgebase of all the intellectual property

Note: The categories parameter is an array of categories that should be extracted. Possible category types: "Person", "Location", "Organization", "Quantity", "Datetime", "URL", "Email". If no category is provided, all types are returned.

Box 2: {"name": " entities"}

The include wikis, so should include entities in the outputs.

Note: entities is an array of complex types that contains rich information about the entities extracted from text, with the following fields

name (the actual entity name. This represents a "normalized" form)

wikipediaId

wikipediaLanguage

wikipediaUrl (a link to Wikipedia page for the entity)

etc.

The requirements state:

All AI solution responses must have a confidence score ≥ 70%.

If the response confidence score is < 70%, the response must be improved by human input.

Members of the Consultant-Bookkeeper group must be able to process financial documents, which includes performing manual review when the AI confidence is below threshold.

Looking at the provided JSON:

"fields": {

"ReceiptType": {

"type": "string",

"valueString": "Itemized",

"confidence": 0.672

},

"MerchantName": {

"type": "string",

"valueString": "Tailwind",

"confidence": 0.913

}

}

ReceiptType.confidence = 0.672 → this is below 0.7.

MerchantName.confidence = 0.913 → this is above 0.7.

Therefore, the correct condition for triggering manual review is:

documentResults.fields..confidence < 0.7

Now, evaluating the options:

A. documentResults.docType == "prebuilt:receipt"

Always true for receipts, does not check confidence. Not correct.

B. documentResults.fields.*.confidence < 0.7

This is the correct general expression: trigger manual review whenever any field confidence is below 0.7.

C. documentResults.fields.ReceiptType.confidence > 0.7

This would bypass manual review when ReceiptType has high confidence. The requirement is to trigger review when confidence < 0.7, so this is the opposite.

D. documentResults.fields.MerchantName.confidence < 0.7

This only checks one field (MerchantName). In the JSON, MerchantName has confidence 0.913 (>0.7), so this condition would not trigger, but ReceiptType clearly needs review. Too narrow.

Correct Answer: B. documentResults.fields.*.confidence < 0.7

Azure AI Document Intelligence – Confidence scores

Human-in-the-loop for Document Intelligence

Microsoft References