Debugging scenarioHard~12 minReliability / 21 of 32

Interview question

After a deployment, database CPU spikes and one API endpoint is suspected. How would you investigate?

Debug high database CPU after an API change

Explains high database CPU after an API change with practical .NET data-access examples and production trade-offs.

1Say itAnswer out loud first

2CompareCompare with Core answer

3RefineRefine with Stronger and Advanced

TL;DR

I would first confirm the timing: when CPU increased, which deployment happened, and which endpoints or queries changed. Then I would identify the most expensive queries using database monitoring, application logs, or traces. I would compare generated SQL before and after if possible. I would also check query count, execution plans, row counts, and missing indexes.

Full answer below

Practice focus

Understanding of high database CPU after an API change, EF Core/SQL behavior, API boundaries, production risks, and practical debugging or design judgment.

Answer first

Answer path

Core answer first, then checkpoints toward a stronger answer.

Start
Core answer
Start here. This is the direct spoken answer to practice first.
Why this question matters
Explain high database CPU after an API change with practical .NET data-access examples and production trade-offs. It matters in real backend work because query shape, indexes, transactions, locking, and data volume can turn harmless-looking code into a production bottleneck. The practical angle is high database CPU after an API change, EF Core/SQL behavior, API boundaries, production risks, and practical debugging or design judgment, tied to a concrete production decision.
I would first confirm the timing: when CPU increased, which deployment happened, and which endpoints or queries changed. Then I would identify the most expensive queries using database monitoring, application logs, or traces. I would compare generated SQL before and after if possible. I would also check query count, execution plans, row counts, and missing indexes.
Checkpoint
Stronger answer
Optional upgrade with examples, trade-offs, and sharper phrasing.
A common cause is a new filter, include, sort, or projection that changes the SQL shape. Adding a sort by an unindexed column on a large table can spike CPU. I would decide whether to mitigate by rolling back, disabling the feature, reducing traffic, adding an index, or changing the query. The trade-off is that adding an index in production may also lock or slow writes, so it needs careful rollout.
Finish
Advanced answer
Optional deeper angle for production judgment and nuance.
I treat high database CPU as an incident with both mitigation and root-cause work. I look at query plans, wait stats, CPU-consuming queries, connection pool behavior, and API traffic patterns. I avoid guessing from C# code alone because EF Core can generate different SQL than expected. After fixing, I add regression coverage: query logging, representative performance tests, dashboards, and code-review checks for risky query changes.

Coding practice

Practice the problem like a real interview: restate, reason, implement, and test.

ExampleCandidatePractical exampleWalk through sample input and output.Open details

Details

A new sort on an unindexed column may scan a large table on every request. Query plans and deployment timing reveal it.

Review signals

Use these to catch weak phrasing, shallow reasoning, and risky claims.

Common mistakesCandidateCommon mistakesMistakes that weaken the answer or signal shallow understanding.Open details

Details

Treating high database CPU after an API change as a syntax detail instead of a production behavior.
Not checking generated SQL, query plans, or actual data volume.
Testing only with tiny local data and assuming production will behave the same.
Fixing symptoms with caching, timeouts, or retries before understanding the data-access cause.

Red flagsCandidateSigns the approach is fragileSignals that an interviewer may treat as risky.Open details

Details

Cannot explain how high database CPU after an API change affects API behavior.
Gives only a definition with no practical example.
Ignores production failure modes.
Does not know how to verify the behavior.

Follow-up prompts

Quick interviewer prompts inside this drill.

Follow-up questionsCandidateFollow-up questionsLikely next questions after the main answer.Open details

Details

Treat these as the next interviewer turn. Answer one out loud, then save the gap in your practice notes.

How would you identify the expensive query?
When would you rollback instead of indexing?
How can a new sort cause high CPU?
What metrics prove the fix worked?

Continue the section

Next in Reliability: Connection Pool Exhaustion

Step 22 of 32

Debug connection pool exhaustion in a .NET API

Opening drill

Debugging scenarioHard~12 minReliability / 21 of 32

Interview question

After a deployment, database CPU spikes and one API endpoint is suspected. How would you investigate?

Debug high database CPU after an API change

Explains high database CPU after an API change with practical .NET data-access examples and production trade-offs.

1Say itAnswer out loud first

2CompareCompare with Core answer

3RefineRefine with Stronger and Advanced

TL;DR

Full answer below

Practice focus

Understanding of high database CPU after an API change, EF Core/SQL behavior, API boundaries, production risks, and practical debugging or design judgment.

Answer first

Answer path

Core answer first, then checkpoints toward a stronger answer.

Start
Core answer
Start here. This is the direct spoken answer to practice first.
Why this question matters
Explain high database CPU after an API change with practical .NET data-access examples and production trade-offs. It matters in real backend work because query shape, indexes, transactions, locking, and data volume can turn harmless-looking code into a production bottleneck. The practical angle is high database CPU after an API change, EF Core/SQL behavior, API boundaries, production risks, and practical debugging or design judgment, tied to a concrete production decision.
I would first confirm the timing: when CPU increased, which deployment happened, and which endpoints or queries changed. Then I would identify the most expensive queries using database monitoring, application logs, or traces. I would compare generated SQL before and after if possible. I would also check query count, execution plans, row counts, and missing indexes.
Checkpoint
Stronger answer
Optional upgrade with examples, trade-offs, and sharper phrasing.
A common cause is a new filter, include, sort, or projection that changes the SQL shape. Adding a sort by an unindexed column on a large table can spike CPU. I would decide whether to mitigate by rolling back, disabling the feature, reducing traffic, adding an index, or changing the query. The trade-off is that adding an index in production may also lock or slow writes, so it needs careful rollout.
Finish
Advanced answer
Optional deeper angle for production judgment and nuance.
I treat high database CPU as an incident with both mitigation and root-cause work. I look at query plans, wait stats, CPU-consuming queries, connection pool behavior, and API traffic patterns. I avoid guessing from C# code alone because EF Core can generate different SQL than expected. After fixing, I add regression coverage: query logging, representative performance tests, dashboards, and code-review checks for risky query changes.

Coding practice

Practice the problem like a real interview: restate, reason, implement, and test.

ExampleCandidatePractical exampleWalk through sample input and output.Open details

Details

A new sort on an unindexed column may scan a large table on every request. Query plans and deployment timing reveal it.

Review signals

Use these to catch weak phrasing, shallow reasoning, and risky claims.

Common mistakesCandidateCommon mistakesMistakes that weaken the answer or signal shallow understanding.Open details

Details

Treating high database CPU after an API change as a syntax detail instead of a production behavior.
Not checking generated SQL, query plans, or actual data volume.
Testing only with tiny local data and assuming production will behave the same.
Fixing symptoms with caching, timeouts, or retries before understanding the data-access cause.

Red flagsCandidateSigns the approach is fragileSignals that an interviewer may treat as risky.Open details

Details

Cannot explain how high database CPU after an API change affects API behavior.
Gives only a definition with no practical example.
Ignores production failure modes.
Does not know how to verify the behavior.

Follow-up prompts

Quick interviewer prompts inside this drill.

Follow-up questionsCandidateFollow-up questionsLikely next questions after the main answer.Open details

Details

Treat these as the next interviewer turn. Answer one out loud, then save the gap in your practice notes.

How would you identify the expensive query?
When would you rollback instead of indexing?
How can a new sort cause high CPU?
What metrics prove the fix worked?

Continue the section

Next in Reliability: Connection Pool Exhaustion

Step 22 of 32

Debug connection pool exhaustion in a .NET API

After a deployment, database CPU spikes and one API endpoint is suspected. How would you investigate?

Practice focus

Answer path

Why this question matters

Coding practice

Review signals

Follow-up prompts

Continue the section

After a deployment, database CPU spikes and one API endpoint is suspected. How would you investigate?

Practice focus

Answer path

Why this question matters

Coding practice

Review signals

Follow-up prompts

Continue the section