Free Microsoft AI-102 Practice Exam with Questions & Answers | Set: 3

Code Recap (Custom Skill JSON Schema)

{

" @odata.type " : " #Microsoft.Skills.Custom.WebApiSkill " ,

" description " : " My custom skill description " ,

" uri " : " https://contoso-webskill.azurewebsites.net/api/process " ,

" context " : " /document/organizations/* " ,

" inputs " : [

{

" name " : " companyName " ,

" source " : " /document/organizations/* "

}

],

" outputs " : [

{

" name " : " companyDescription "

}

]

}

Statement Analysis

CompanyDescription is available for indexing.

Yes.

The skill produces an output field called companyDescription . Outputs of a custom skill become part of the enrichment tree, which can then be projected into the search index or knowledge store.

The definition calls a web API as part of the enrichment process.

Yes.

This is a Web API skill ( @odata.type: #Microsoft.Skills.Custom.WebApiSkill ) with a uri pointing to https://contoso-webskill.azurewebsites.net/api/process . This means an external web API is called to enrich data.

The enrichment step is called only for the first organization under /document/organizations .

No.

The context is set to " /document/organizations/* " , where the * means the skill runs for each element in the collection. It is not limited to the first element.

Final Answer

CompanyDescription is available for indexing → Yes

The definition calls a web API as part of the enrichment process → Yes

The enrichment step is called only for the first organization under /document/organizations → No

Microsoft References

Custom skills in Azure Cognitive Search

Enrichment tree and skill context

Database transactions follow the ACID properties :

Atomicity → Ensures that each transaction is treated as a single “all-or-nothing” unit of work. Either the entire transaction is completed, or none of it is applied. This guarantees that transactions are executed only once and rolled back completely if they fail.

Consistency → Ensures that a transaction brings the database from one valid state to another, preserving integrity constraints.

Isolation → Ensures that concurrent transactions do not interfere with each other.

Durability → Ensures that once a transaction is committed, it remains permanent even in case of system failure.

Thus, the correct property here is Atomicity .

Microsoft Reference: ACID properties in databases

For Azure OpenAI fine-tuning, training data must be provided as a JSON Lines (.jsonl) file. Each line represents one example. Because your data is described as prompt–completion pairs, the required shape is typically:

{ " prompt " : " < your prompt > " , " completion " : " < ideal completion > " }

(Chat model fine-tuning instead uses a messages array per line, but the file format is still JSONL.) JSONL is the only accepted format for upload to the fine-tuning job.

Microsoft References

Azure OpenAI Service – Fine-tuning: data preparation and formats (JSONL; prompt–completion and chat messages ).

Azure OpenAI Service – How to fine-tune models (training files must be .jsonl).

For the category field in Azure Cognitive Search:

searchable → allows users to search by words in the category field.

facetable → enables drill-down filtering (facets).

retrievable → ensures the field appears in search results.

filterable/sortable/key are not required here based on the scenario.

Correct Answer: A

Box 1: Cognitive Service User

Ensure that the members of a group named Management-Accountants can approve the FAQs.

Approve=publish.

Cognitive Service User (read/write/publish): API permissions: All access to Cognitive Services resource except for ability to:

1. Add new members to roles.

2. Create new resources.

Box 2: Cognitive Services QnA Maker Editor

Ensure that the members of a group named Consultant-Accountants can create and amend the FAQs.

QnA Maker Editor: API permissions:

1. Create KB API

2. Update KB API

3. Replace KB API

4. Replace Alterations

5. " Train API " [in new service model v5]

Box 3: Cognitive Services QnA Maker Read

Ensure that the members of a group named the Agent-CustomerServices can browse the FAQs.

QnA Maker Read: API Permissions:

1. Download KB API

2. List KBs for user API

3. Get Knowledge base details

4. Download Alterations

Generate Answer

You are securing an Azure Cognitive Search resource with two specific requirements:

Prevent access to Search1 from the internet

The correct way is to use a private endpoint.

A private endpoint connects the Cognitive Search resource directly into your virtual network (VNet), blocking public internet access.

Configuring an IP firewall can restrict access to specific IP ranges but does not fully prevent internet access.

Azure roles and authentication do not address network-level restrictions.

Limit the access of each app to specific queries

This is done via Azure role-based access control (RBAC).

You can assign different Azure roles or API keys with query rights to control what each app can do.

Key authentication alone only provides coarse-grained access (admin/query keys) but does not allow fine-grained per-app restrictions.

RBAC ensures principle of least privilege is enforced.

Correct Answer:

Prevent access from internet → Create a private endpoint

Limit access to queries → Use Azure roles

Microsoft References

Secure access to Azure Cognitive Search using private endpoints

Role-based access control in Azure Cognitive Search

The given text:

" Our tour guide took us up the Space Needle during our trip to Seattle last week. "

The Language service output shows:

Text

Category

ConfidenceScore

Tour guide

PersonType

0.45

Space Needle

Location

0.38

Trip

Event

0.78

Seattle

Location

0.78

Last week

DateTime

0.80

Why the correct answer is Named Entity Recognition (NER):

NER identifies named entities (people, places, dates, events, organizations, etc.) in text and classifies them into categories with confidence scores.

In the table, we see categories such as PersonType, Location, Event, DateTime → these are classic outputs of NER.

Why not the others:

A. Entity Linking → Identifies entities and links them to a knowledge base (like Wikipedia). Output would include links/IDs rather than just categories.

C. Key Phrase Extraction → Returns important phrases (“tour guide”, “Space Needle”, “trip”, “Seattle”, “last week”) but does not assign categories like PersonType, Location, DateTime.

D. Sentiment Analysis → Returns sentiment scores (positive/neutral/negative), not entity categories.

Correct Answer:

B. Named Entity Recognition

Microsoft References

Named Entity Recognition in Azure AI Language

Entity Linking vs Named Entity Recognition

Comprehensive Detailed Explanation

In the provided LUDown file snippet:

Intents represent the purpose or action the user wants to perform.

Examples: ## Confirm , ## ExtractName , ## Logout , ## SelectItem , ## SelectNone .

These are all intent definitions.

So, SelectItem is an intent.

Utterances are the example phrases that users might say to invoke a particular intent.

Under ## SelectItem , one example utterance is:

choose { @DirectionalReference =top right}

This is an example phrase that maps to the SelectItem intent and contains the DirectionalReference entity.

Therefore, Choose {@DirectionalReference=top right} is an utterance.

Why not the other options?

Domain: Predefined category grouping multiple intents (not applicable here).

Entity: Extracted data within utterances (e.g., @DirectionalReference=top right or @personName ).

The entire phrase " choose {@DirectionalReference=top right} " is not an entity but an utterance containing an entity.

Correct Answers

SelectItem → an intent

Choose {@DirectionalReference=top right} → an utterance

Microsoft References

LUDown file format for LUIS

LUIS concepts: intents, utterances, and entities