The answer is to deploy the Cognitive Services resource in multiple regions and use a load balancer, because the Text Analytics API within Azure Cognitive Services for Language natively handles sentiment analysis, key phrase extraction, and language detection in a single API call, meeting all three functional requirements without separate services. The critical technical concept here is that scaling to thousands of requests per second requires horizontal scaling—distributing traffic across multiple regional instances via a load balancer—since a single endpoint will throttle under high concurrency. On the Microsoft Azure AI-102 exam, this tests your understanding of both the unified Text Analytics API capabilities and the architecture for high-throughput workloads; a common trap is to think you need separate resources for each feature or that scaling is automatic. Remember the memory tip: “One API, many regions—balance the load for millions of sessions.”
AI-102 Plan and manage an Azure AI solution Practice Question
This AI-102 practice question tests your understanding of plan and manage an azure ai solution. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company is designing a solution that uses Azure Cognitive Services for text analytics. The solution must meet the following requirements:
- Detect sentiment in customer feedback.
- Extract key phrases from the feedback.
- Identify the language of the feedback automatically.
- Ensure that the solution can scale to handle thousands of requests per second.
Which TWO actions should the company take?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use the Text Analytics API for sentiment analysis and key phrase extraction.
Option B is correct because the Text Analytics API (part of Azure Cognitive Services for Language) provides built-in capabilities for sentiment analysis, key phrase extraction, and language detection within a single API call. This eliminates the need for separate services and directly satisfies the requirements for detecting sentiment, extracting key phrases, and identifying language automatically.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Deploy a separate Language Detection service for language identification.
Why it's wrong here
Text Analytics API includes language detection.
✓
Use the Text Analytics API for sentiment analysis and key phrase extraction.
Why this is correct
Text Analytics API provides these features.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use the Free pricing tier to reduce costs.
Why it's wrong here
Free tier has low throughput limits.
✓
Deploy the Cognitive Services resource in multiple regions and use a load balancer.
Why this is correct
Multi-region deployment with load balancing supports high throughput.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Azure Functions to aggregate results from multiple API calls.
Why it's wrong here
Not required; the API handles results per request.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think language detection requires a separate dedicated service (Option A) or that the Free tier can scale (Option C), when in fact the Text Analytics API consolidates all required features and the Free tier is severely throttled for high-throughput workloads.
Detailed technical explanation
How to think about this question
The Text Analytics API uses pre-trained machine learning models hosted in Azure, supporting batch processing and real-time endpoints. Under the hood, language detection leverages the ISO 639-1 code standard, and sentiment analysis returns a confidence score between 0 and 1 for each document. For high-scale scenarios, deploying Cognitive Services resources in multiple regions with Azure Traffic Manager or Azure Front Door enables geo-distributed load balancing, ensuring sub-second response times even under thousands of concurrent requests.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Plan and manage an Azure AI solution — This question tests Plan and manage an Azure AI solution — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use the Text Analytics API for sentiment analysis and key phrase extraction. — Option B is correct because the Text Analytics API (part of Azure Cognitive Services for Language) provides built-in capabilities for sentiment analysis, key phrase extraction, and language detection within a single API call. This eliminates the need for separate services and directly satisfies the requirements for detecting sentiment, extracting key phrases, and identifying language automatically.
What should I do if I get this AI-102 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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