Newly Released Microsoft AI-102 Exam PDF

Box 1: Yes

You create property accessors using the CreateProperty method that provides a handle to the BotState object. Each state property accessor allows you to get or set the value of the associated state property.

Box 2: Yes

Box 3: No

Before you exit the turn handler, you use the state management objects' SaveChangesAsync() method to write all state changes back to storage.

Comprehensive Detailed ExplanationThe question focuses on monitoring a public-facing sales system that processes video and text, ensuring that the system provides equitable results regardless of user location or background.

Two Microsoft Responsible AI principles apply directly here:

Fairness

AI systems should treat all people fairly and avoid bias.

Monitoring ensures that outcomes do not disadvantage people based on location, background, or demographics.

This directly addresses the requirement for equitable results.

Inclusiveness

AI systems should empower everyone and engage people of all backgrounds.

Monitoring for inclusiveness ensures that the system works well across different regions, languages, and cultures, making the system accessible and beneficial for all users.

A. Transparency

Important for explaining how AI systems work, but does not directly address equitable results across demographics.

D. Reliability and safety

Focuses on consistent and safe system operation, not equity across users.

E. Privacy and security

Critical for data protection, but not related to equitable treatment of users.

Why not the other options?

Correct Answer:

B. fairness

C. inclusiveness

Microsoft Responsible AI Principles

Fairness in AI systems

Inclusiveness in AI systems

Microsoft References

Explanation:

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

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.

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.

Why the correct answer is Named Entity Recognition (NER):Why not the others:

Correct Answer:B. Named Entity Recognition

Named Entity Recognition in Azure AI Language

Entity Linking vs Named Entity Recognition

Microsoft References

The scenario involves storing text files, videos, audio streams, and virtual disk images, along with metadata and a unique ID. This exactly matches Object storage characteristics:

Stores large amounts of unstructured data (blobs).

Each object contains the data itself, metadata, and a unique identifier.

Azure Blob Storage is Microsoft’s object storage solution.

Other options:

Columnar → Used for analytics workloads, optimized for reading columns (e.g., data warehouses).

Key/Value → Good for simple lookups but not optimized for storing large multimedia files.

Document → Stores semi-structured JSON documents, not large binary objects.

Thus, the correct answer is Object storage.

Microsoft Reference: Azure Blob Storage (Object storage) overview