- A
Enable adaptive sampling in the Application Insights SDK and set a target events-per-second limit
Adaptive sampling runs in the SDK. It monitors the outgoing telemetry rate and automatically raises or lowers the sample percentage to stay near the target rate. All operation types are sampled proportionally, so statistical trends remain meaningful even at 20 percent of raw volume. Data is reduced before transmission, lowering both network and ingestion costs.
- B
Configure ingestion sampling in the Azure portal to retain 20 percent of incoming telemetry
Why wrong: Ingestion sampling discards 80 percent of events after they arrive at the Application Insights endpoint. The data is still transmitted from the application, so network bandwidth is unchanged. It reduces storage and query costs but not the cost of transmitting data from the app.
- C
Increase the TelemetryClient flush interval from 30 seconds to 5 minutes to batch events
Why wrong: Flush interval controls when buffered events are sent, not how many are sent. Flushing less frequently does not reduce the number of events — it only changes the timing. Total event volume and ingestion cost remain the same.
- D
Filter all events with HTTP status code 200 from the telemetry pipeline before sending
Why wrong: Filtering out 200-OK requests removes all evidence of successful operations. Metrics like response time percentiles, throughput, and dependency success rates become meaningless. Sampling preserves proportional representation; hard filtering destroys observability for the majority of traffic.
Quick Answer
The correct answer is to enable adaptive sampling in the Application Insights SDK and set a target events-per-second limit. This works because adaptive sampling automatically adjusts the telemetry volume sent from your high-traffic API, intelligently dropping a consistent percentage of events across all request types to meet your specified target rate—such as reducing from 80,000 events per second down to roughly 16,000, achieving the desired 80% cost reduction while preserving representative samples. On the AZ-204 exam, this scenario tests your understanding of cost optimization through telemetry sampling, often appearing as a distractor against fixed-rate sampling or ingestion-side sampling; the key trap is assuming you must manually define sampling rules, when adaptive sampling handles it dynamically. Remember the memory tip: “Adaptive = Automatic Adjustment,” meaning the SDK self-tunes to your target rate without requiring custom logic for each request type.
AZ-204 Practice Question: Application Insights adaptive sampling to reduce…
This AZ-204 practice question tests your understanding of monitor, troubleshoot, and optimize azure solutions. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. A key principle to apply: application Insights adaptive sampling. 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 high-traffic API sends approximately 80,000 telemetry events per second to Application Insights. Monthly ingestion costs are too high. The team wants to reduce data volume by roughly 80 percent while still seeing representative samples of all request types. What should the developer configure?
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
Enable adaptive sampling in the Application Insights SDK and set a target events-per-second limit
Adaptive sampling in the Application Insights SDK automatically adjusts the volume of telemetry sent to the service, targeting a specified rate of events per second. By setting a target that reduces the original 80,000 events/sec to roughly 20%, the SDK will intelligently sample all request types proportionally, preserving representative data while cutting costs.
Key principle: Application Insights adaptive sampling
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable adaptive sampling in the Application Insights SDK and set a target events-per-second limit
Why this is correct
Adaptive sampling runs in the SDK. It monitors the outgoing telemetry rate and automatically raises or lowers the sample percentage to stay near the target rate. All operation types are sampled proportionally, so statistical trends remain meaningful even at 20 percent of raw volume. Data is reduced before transmission, lowering both network and ingestion costs.
Related concept
Application Insights adaptive sampling
- ✗
Configure ingestion sampling in the Azure portal to retain 20 percent of incoming telemetry
Why it's wrong here
Ingestion sampling discards 80 percent of events after they arrive at the Application Insights endpoint. The data is still transmitted from the application, so network bandwidth is unchanged. It reduces storage and query costs but not the cost of transmitting data from the app.
- ✗
Increase the TelemetryClient flush interval from 30 seconds to 5 minutes to batch events
Why it's wrong here
Flush interval controls when buffered events are sent, not how many are sent. Flushing less frequently does not reduce the number of events — it only changes the timing. Total event volume and ingestion cost remain the same.
- ✗
Filter all events with HTTP status code 200 from the telemetry pipeline before sending
Why it's wrong here
Filtering out 200-OK requests removes all evidence of successful operations. Metrics like response time percentiles, throughput, and dependency success rates become meaningless. Sampling preserves proportional representation; hard filtering destroys observability for the majority of traffic.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse ingestion sampling (a portal-level fixed filter) with adaptive sampling (an SDK-level dynamic filter), assuming any sampling in the portal will suffice, but only adaptive sampling can meet the dual goals of volume reduction and representativeness across all request types.
Detailed technical explanation
How to think about this question
Adaptive sampling uses a dynamic algorithm that monitors the current ingestion rate and adjusts the sampling percentage per telemetry type (e.g., requests, dependencies, exceptions) to stay near the configured target. Under the hood, the SDK maintains a per-second rate counter and applies a sampling decision early in the pipeline, before any expensive serialization or transmission, which minimizes CPU and network overhead. In high-traffic scenarios, this ensures that even during sudden bursts, the system gracefully throttles without overwhelming the backend or exceeding budget.
KKey Concepts to Remember
- Application Insights adaptive sampling
- ingestion cost
- representative telemetry
- SDK-side sampling
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
Application Insights adaptive sampling
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
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FAQ
Questions learners often ask
What does this AZ-204 question test?
Monitor, troubleshoot, and optimize Azure solutions — This question tests Monitor, troubleshoot, and optimize Azure solutions — Application Insights adaptive sampling.
What is the correct answer to this question?
The correct answer is: Enable adaptive sampling in the Application Insights SDK and set a target events-per-second limit — Adaptive sampling in the Application Insights SDK automatically adjusts the volume of telemetry sent to the service, targeting a specified rate of events per second. By setting a target that reduces the original 80,000 events/sec to roughly 20%, the SDK will intelligently sample all request types proportionally, preserving representative data while cutting costs.
What should I do if I get this AZ-204 question wrong?
Review application Insights adaptive sampling, then practise related AZ-204 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Application Insights adaptive sampling
About these practice questions
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Same concept, more angles
2 more ways this is tested on AZ-204
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Your e-commerce application sends telemetry to Application Insights. You need to reduce ingestion costs while preserving the ability to detect trends in performance metrics. Which sampling type should you configure?
medium- A.Fixed-rate sampling
- ✓ B.Adaptive sampling
- C.Ingestion sampling
- D.Head-based sampling
Why B: Adaptive sampling is the correct choice because it automatically adjusts the volume of telemetry data collected based on the application's activity level, ensuring that performance trends are preserved while reducing ingestion costs. Unlike fixed-rate sampling, adaptive sampling dynamically increases or decreases the sampling rate to maintain a target volume, making it ideal for e-commerce applications with variable traffic patterns.
Variation 2. An e-commerce application emits a high volume of telemetry data to Azure Application Insights. You need to reduce the cost of data ingestion while preserving statistical accuracy for performance metrics. Which sampling technique should you use?
medium- ✓ A.Adaptive sampling
- B.Fixed-rate sampling with a 1% rate
- C.Ingestion sampling
- D.Head-based sampling
Why A: Adaptive sampling is the correct choice because it automatically adjusts the sampling rate based on the volume of telemetry data, ensuring that during low-traffic periods all data is retained for statistical accuracy, while during high-traffic periods it reduces the rate to control costs. This technique is specifically designed for high-volume scenarios like e-commerce telemetry, where preserving statistical accuracy for performance metrics (e.g., request durations, failure rates) is critical, and it avoids the manual tuning required by fixed-rate sampling.
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Last reviewed: Jun 11, 2026
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