Quick Answer
The correct action to reduce high latency in Azure AI Language is to scale the service by increasing the number of instances or moving to a higher pricing tier. This works because high latency typically stems from hitting the service’s throughput limits or rate constraints—when too many concurrent requests exceed the capacity of your current tier, the service queues or throttles them, causing delays. Scaling up to a tier like Standard S0 or higher, or adding more instances, directly alleviates this bottleneck by providing more processing power and higher request-per-second allowances. On the AI-102 exam, this scenario tests your understanding of Azure AI Language’s performance scaling options versus architectural changes—a common trap is to suggest optimizing code or changing endpoints, but the fastest fix is simply increasing capacity. Remember the memory tip: “When latency’s high, scale to the sky”—think of scaling as the immediate lever for throughput issues, not redesign.
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.
You have an Azure AI solution that uses Azure AI Language to perform sentiment analysis. The solution is experiencing high latency. Which action should you take to reduce latency?
Move the service to a different Azure region.
Why wrong: Latency may not be region-related.
Use the Free tier of the Azure AI Language service.
Why wrong: Free tier has lower limits and may increase latency.
Increase the request timeout value.
Why wrong: Timeout does not affect processing speed.
Scale the service by increasing the number of instances or using a higher pricing tier.
More capacity reduces queuing and latency.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Scale the service by increasing the number of instances or using a higher pricing tier.
Option D is correct because scaling the Azure AI Language service by increasing the number of instances or moving to a higher pricing tier (e.g., from Standard S0 to a tier with higher throughput) directly addresses high latency by providing more capacity to handle concurrent requests. High latency often results from hitting the service's rate limits or throughput constraints, and scaling alleviates this bottleneck without changing the underlying architecture.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Move the service to a different Azure region.
Why it's wrong here
Latency may not be region-related.
Use the Free tier of the Azure AI Language service.
Why it's wrong here
Free tier has lower limits and may increase latency.
Increase the request timeout value.
Why it's wrong here
Timeout does not affect processing speed.
Scale the service by increasing the number of instances or using a higher pricing tier.
Why this is correct
More capacity reduces queuing and latency.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often confuse network latency (solved by region proximity) with service throughput latency (solved by scaling), leading them to incorrectly choose Option A when the real bottleneck is capacity, not geography.
Detailed technical explanation
Azure AI Language services enforce rate limits based on the pricing tier (e.g., Standard S0 allows up to 1,000 requests per minute per region). When requests exceed this limit, the service returns HTTP 429 (Too Many Requests) or queues requests, causing perceived latency. Scaling by adding instances (via Azure AI Services multi-region deployment or using a higher tier) increases the allowed transactions per second (TPS) and reduces queuing. In a real-world scenario, a retail chatbot performing real-time sentiment analysis on customer feedback during a flash sale would see latency spikes due to throttling, which scaling resolves by providing dedicated capacity.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
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..
The correct answer is: Scale the service by increasing the number of instances or using a higher pricing tier. — Option D is correct because scaling the Azure AI Language service by increasing the number of instances or moving to a higher pricing tier (e.g., from Standard S0 to a tier with higher throughput) directly addresses high latency by providing more capacity to handle concurrent requests. High latency often results from hitting the service's rate limits or throughput constraints, and scaling alleviates this bottleneck without changing the underlying architecture.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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