AI-102 Plan and manage an Azure AI solution Practice Question
Which TWO are valid techniques for reducing latency in an Azure AI solution that processes real-time chat messages?
⚠ Common exam trap
Many candidates confuse model accuracy with performance, assuming a larger model will be faster, when in reality it increases latency due to higher compute demands, and they may overlook batch processing as a valid latency-reduction technique because they associate it only with throughput, not real-time responsiveness.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Deploy Azure AI services in the same region as the application.
Deploying Azure AI services in the same region as the application minimizes network latency by reducing the physical distance data must travel. This ensures lower round-trip times (RTT) for real-time chat message processing, which is critical for maintaining responsiveness in interactive applications.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a larger, more accurate model for better performance.
Why it's wrong here
Larger models often have higher latency.
- ✗
Move the application to a different region to use a different pricing tier.
Why it's wrong here
Does not guarantee lower latency.
- ✓
Deploy Azure AI services in the same region as the application.
Why this is correct
Minimizes network round-trip time.
- ✓
Use batch processing to combine multiple messages into one API call.
Why this is correct
Reduces the number of HTTP requests.
- ✗
Increase the client-side timeout to allow for slower responses.
Why it's wrong here
Does not reduce latency.
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