Microsoft AI-200 : Developing AI Cloud Solutions on Azure

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Microsoft AI-200 Exam Syllabus Topics:

Microsoft Developing AI Cloud Solutions on Azure Sample Questions:

1. You are choosing an embedding strategy for a RAG solution. Documents range from 2 to 200 pages. You need to preserve semantic coherence while staying within embedding model token limits. What should you do?

A) Embed each entire document as a single vector
B) Chunk documents into smaller overlapping segments before embedding
C) Use only document titles for embedding
D) Convert documents to images and use image embeddings

2. Hotspot Question
You have an Azure Service Bus namespace that contains a topic named Topic1.
You plan to create a subscription named Sub1 to Topic1. In Sub1, you plan to filter messages from Topic1 based on their system properties and apply an action that will annotate each filtered message.
You need to configure the filtering.
How should you configure the filtering? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

3. Hotspot Question
You are implementing semantic retrieval in Redis.
The solution must support low-latency, approximate nearest neighbor (ANN) vector similarity search for large-scale AI retrieval workloads.
You need to select the appropriate vector schema settings.
Which configuration values should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

4. You need to add a custom voice that matches your company's brand for a text-to-speech feature, distinct from any standard neural voice. What should you use?

A) Azure AI Speech prebuilt neural voices
B) Azure AI Speech Custom Neural Voice
C) Azure AI Translator
D) Azure AI Language sentiment analysis

5. Your application stores conversation history so a multi-turn chatbot can reference earlier turns, but the context window is limited. You need to keep the most relevant history within token limits.
What should you implement?

A) Increase max_tokens for output only
B) Truncate all history after 2 turns
C) Summarize older turns and retain recent turns verbatim
D) Store history only in the client and never send it to the model

Solutions: