Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版) - AI-102日本語 Exam Practice Test

Explanation:
The output will include the following words: our and included. # No
The output will include the following words: Paris, Eiffel, and Tower. # Yes The function will output all the key phrases from the input string to the console. # No Comprehensive Detailed Explanation Let's examine the function:
static void GetKeyWords ( TextAnalyticsClient textAnalyticsClient, string text)
{
var response = textAnalyticsClient.RecognizeEntities(text);
Console.WriteLine( " Key words: " );
foreach (CategorizedEntity entity in response.Value)
{
Console.WriteLine( $ " {entity.Text} " );
}
}
The function uses RecognizeEntities from the Azure Text Analytics SDK.
RecognizeEntities detects and categorizes named entities in text (such as locations, people, organizations, dates, etc.).
It does not extract all words or key phrases.
For extracting key phrases, you would use ExtractKeyPhrases , not RecognizeEntities .
Evaluating the statements:
" The output will include the following words: our and included. "
These are common words, not entities. They will not appear.
The answer: No
" The output will include the following words: Paris, Eiffel, and Tower. " These are named entities (location, landmark). They will be recognized and returned.
The answer: Yes
" The function will output all the key phrases from the input string to the console. " No. The function extracts entities, not key phrases. Key phrases would require ExtractKeyPhrases .
The answer: No

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Explanation:

Box 1: GeographyV2
The prebuilt geographyV2 entity detects places. Because this entity is already trained, you do not need to add example utterances containing GeographyV2 to the application intents.
Box 2: Email
Email prebuilt entity for a LUIS app: Email extraction includes the entire email address from an utterance.
Because this entity is already trained, you do not need to add example utterances containing email to the application intents.
Box 3: Machine learned
The machine-learning entity is the preferred entity for building LUIS applications.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-reference-prebuilt-geographyv2
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-reference-prebuilt-email
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/reference-entity-machine-learned-entity

Explanation:
Box 1: Adaptive card
Box 2: an image
Reference:
https://docs.microsoft.com/en-us/microsoftteams/platform/task-modules-and-cards/cards/cards-reference
https://docs.microsoft.com/en-us/composer/how-to-send-cards?tabs=v1x

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Explanation:

Comprehensive Detailed Explanation
When an indexer runs in Azure AI Search, it processes each document through a fixed series of stages:
Document cracking
The indexer opens the item (file/row) and extracts text, images, and metadata, creating the root of the enriched document (/document). This is always the first stage.
Field mappings
Next, source fields are mapped directly to target index fields before any transformations . Field mappings occur after document cracking but before skillset execution . Microsoft Learn Skillset execution Built-in or custom skills (for example, language and sentiment analysis in your scenario) run and write outputs into the in-memory enriched document. Microsoft Learn Output field mappings Because skill outputs live only in memory, you must define output field mappings to route selected nodes of the enriched document into index fields. This step comes after skillset execution.
Push to index
Finally, the indexer hands off raw and enriched content to the search index (the physical data structures) so it becomes searchable-i.e., the content is "pushed to the index." Microsoft Learn This order- document cracking # field mappings # skillset execution # output field mappings # push to index -ensures raw content is extracted, direct source-to-index mappings are applied, AI enrichment is performed, enriched outputs are projected to fields, and the completed documents are written into the index.
Microsoft Azure AI References
Indexers in Azure AI Search - Stages of indexing (Document cracking; Field mappings; Skillset execution; Output field mappings, with field mappings occurring before transformations). Microsoft Learn Skillset concepts (Output of skillset is routed to the index via output field mappings after skillset execution).
Microsoft Learn
AI enrichment concepts (Field mappings move raw source content to the index; output field mappings move enriched content; indexing ingests raw and enriched content into the physical index). Microsoft Learn Annotations & enriched document tree (Enriched document is created during document cracking at
/document). Microsoft Learn

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Explanation:

The statement is:
" When using the Azure Cosmos DB Gremlin API, the container resource type is projected as a ______. " Options:
graph # Correct. The Gremlin API in Azure Cosmos DB is designed to work with graph data models, supporting vertices (nodes) and edges (relationships). A container is projected as a graph.
table # Used when working with the Table API (key/value store). Not correct here.
partition key # Refers to data distribution and scalability, not the container resource type.
document # Refers to the SQL API or MongoDB API in Cosmos DB, not Gremlin.
Therefore, the correct answer is graph.
The answer: graph
Microsoft References
Azure Cosmos DB Gremlin API
Azure Cosmos DB APIs and data models

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