Helping Hand Questions for AI-102

The exhibit shows incremental increases by quarter leading to a cumulative running total, which is best represented with a waterfall chart.

Ribbon chart shows ranking changes over time, not cumulative totals.

Bar chart shows simple comparisons, not running totals.

Decomposition tree is for drill-down analysis, not running totals.

Correct Answer: A

Scenario Recap:

IoT data from 100 machines × 50 sensors × 1-minute intervals for 1 year = 5,000 time series datasets.

Requirement: Detect unusual values (outliers) to predict failures.

Correct Service:

Azure Anomaly Detector (A) is designed for time-series anomaly detection.

It learns temporal patterns and detects anomalies in sensor/IoT data streams.

Other options:

Cognitive Search = full-text search

Form Recognizer = extract text/fields from documents

Custom Vision = image classification

Answer: A. Anomaly Detector

Comprehensive Detailed Explanation

When an indexer runs in Azure AI Search, it processes each document through a fixed series of stages:

Document crackingThe 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 mappingsNext, 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 executionBuilt-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 mappingsBecause 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 indexFinally, 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

We need to design a Conversational Language Understanding (CLU) model for smart home commands.

The sample utterances:

“Set all the lights to on.” → Intent = change setting, Entity = device (lights)

“Turn off the lights in the living room.” → Intent = change setting, Entities = device (lights) + location (living room)

“What is the current thermostat temperature?” → Intent = query setting, Entity = device (thermostat)

“Lower the temperature of the thermostat by five degrees.” → Intent = change setting, Entity = device (thermostat)

Intents needed:

Change setting intent (D): Covers actions like turning devices on/off, lowering temperature.

Query setting intent (E): Covers questions about current state, like “What is the thermostat temperature?”

Entities needed:

Device entity (F): Identifies devices such as lights and thermostat.

A. a location intent → Location (like “living room”) should be modeled as an entity, not an intent.

B. a change setting entity → Change setting is an action (intent), not an entity.

C. a device intent → Devices are entities, not intents.

Breakdown:Why not the others:

Correct Answer:D. a change setting intent

E. a query setting intent

F. a device entity

Intents and entities in CLU

CLU model design best practices

Microsoft References