Microsoft Updated AI-102 Exam Questions and Answers by alfie-james

For a social media messaging app, you need real-time language identification on short text messages. The Azure AI Translator service provides a dedicated Detect API that returns the language and a confidence score for each input text, designed for low-latency scenarios such as chat and in-app communication. This aligns directly with “identify in real time the language used in messages.” Microsoft Azure +2

Why the other options are not the best fit:

Azure AI Speech (A) targets spoken audio (speech-to-text, translation, diarization). It’s not intended for detecting the language of text messages. Azure AI

Azure AI Content Safety (B) focuses on safety/moderation (harmful or unsafe content), not language identification.

Azure AI Language (D) also offers a Language Detection feature, but it’s typically used within broader text analytics workflows. For real-time messaging and potential downstream translation, Translator’s Detect endpoint is the targeted and streamlined choice.

References (Microsoft Learn)

Translator – Detect method (returns detected language + confidence). Microsoft Learn

Translator – Product overview and real-time translation scenarios. Microsoft Azure

Translator – Language support (Auto Language Detection). Microsoft Learn

Azure AI Language – Language Detection (feature overview/how-to) for context.

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A data warehouse is designed for analytical workloads (OLAP) , not transactional workloads (OLTP).

It integrates data from multiple sources and supports complex queries, aggregations, and reporting for decision-making.

A. storage for transactional LOB apps → describes OLTP (e.g., SQL Server, Azure SQL Database), not a data warehouse.

B. transformation services → describes ETL/ELT pipelines (e.g., Data Factory), not the warehouse itself.

C. read-only storage of historical data → partially true but too narrow; the key feature is analytics, not just storage.

D. answers to complex queries across multiple sources → Correct and complete definition.

Correct Answer: D

For a table projection in a knowledge store (skillset definition), the table node must define:

tableName : the table to project into (physical expression).

generatedKeyName : the name of the system-generated key column.

source : an object-shaping expression (projection path) describing what document content flows into the table.

dataSource/dataSourceConnection are service-level objects used by the indexer, not properties of a table projection node.

References

Knowledge store and projections schema: table projections require tableName, generatedKeyName, and source in the skillset JSON.

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.

Correct Answer: graph

Microsoft References

Azure Cosmos DB Gremlin API

Azure Cosmos DB APIs and data models