354 .NET Developer Interview Questions & Answers

The usual order is: exception handling first, then routing, then CORS, then authentication, then authorization, and endpoints last. That order matters because later middleware depends on routing and identity data, and exception handling must wrap the rest of the pipeline.

This question asks for the correct order of the steps that handle one incoming web request in an ASP.NET Core app. One step should catch problems first. Another step should find the right page or action. Another step should check browser access. Then the app should confirm who the user is and whether that user is allowed to continue. The final step should run the page, controller, or endpoint. If these steps are moved around, the app may hide errors, block valid requests, or let checks run too late.

1. Are you using minimal APIs, controllers, or both?
2. Do you want the classic UseRouting and UseEndpoints pattern or the newer Map style?

The safe general order in ASP.NET Core is:

1. Exception handling first — UseExceptionHandler or UseDeveloperExceptionPage should be near the top so it can catch errors from the rest of the pipeline.
2. Routing next — UseRouting decides which endpoint matches the request.
3. CORS after routing — UseCors often needs endpoint metadata or route context to choose the right policy.
4. Authentication next — UseAuthentication builds the user identity.
5. Authorization after authentication — UseAuthorization checks whether that user can access the endpoint.
6. Endpoints last — MapControllers, MapRazorPages, MapGet, or UseEndpoints executes the selected endpoint.

A common ordering looks like this:

csharp app.UseExceptionHandler("/Error"); app.UseRouting(); app.UseCors("DefaultPolicy"); app.UseAuthentication(); app.UseAuthorization(); app.MapControllers();

The reason order matters is simple: each step depends on the previous one. If you move authorization before authentication, the app may not know who the user is. If you place CORS in the wrong spot, the browser may block the request. If exception handling is too low in the pipeline, some failures will escape the handler. If routing happens too late, endpoint-based middleware cannot make the right decision.

In an interview, I would say the rule is: wrap first, route early, secure before execute, and map last. That shows both the correct sequence and the reason behind it.

1. Put exception handling at the top.
2. Enable routing.
3. Apply CORS after routing.
4. Authenticate the request.
5. Authorize the user.
6. Map and run endpoints last.
7. Verify by testing an unauthenticated request, a blocked CORS request, and a thrown exception.

Time cost is small because these are per-request checks already needed by the web app. Memory cost is low. The real cost is maintenance: the wrong order creates hard-to-debug failures, so the pipeline must stay consistent and well documented.

Placing authentication before routing, which can break endpoint-aware policy decisions. Putting authorization before authentication, which leaves no user identity to check. Running CORS after the endpoint, which can cause browser failures. Putting exception handling too late, which lets errors escape unhandled. Forgetting that the exact pattern differs slightly between UseEndpoints and minimal APIs, but the dependency order is still the same.

State the order first, then give one sentence for why each middleware depends on the one before it.

IOptions is a stable app-wide snapshot, IOptionsSnapshot refreshes once per request scope, and IOptionsMonitor is for long-lived services that need updated values. In debugging, the wrong choice shows up as stale configuration or a lifetime mismatch error.

Imagine an app that reads settings from one place. This question asks how three helpers behave when those settings change. One keeps the same copy for the whole app. One gives a fresh copy each time a request starts. One watches for updates while the app is still running. The interviewer also wants to know which helper is safe in long-lived code and how a bad choice shows up as old values or an error when the app starts or handles a request in the wrong place.

1. Do these settings change while the app is running?
2. Is this code inside a short-lived request or a long-lived service?

Start with the practical rule:

• Use IOptions<T> when the values are effectively fixed for the app lifetime.
• Use IOptionsSnapshot<T> when you want a fresh value per request scope.
• Use IOptionsMonitor<T> when a singleton or background service needs to see updates.

The key lifetime difference is this:

• IOptions<T> is a simple options object you can inject anywhere, but it does not re-read changes automatically.
• IOptionsSnapshot<T> is scoped, so it is recreated for each request scope. That is why it refreshes per request, but it cannot be injected into a singleton.
• IOptionsMonitor<T> is singleton-friendly and can react to reloads. It is the best choice when a long-lived service should use the latest configuration.

How bad lifetime choices appear during debugging:

• If a singleton holds IOptionsSnapshot<T>, you usually get a dependency injection lifetime error, because a scoped service is being used from a longer-lived object.
• If code uses IOptions<T> or the wrong abstraction for changing settings, the app keeps using old values, so the bug looks like stale configuration.
• If IOptionsMonitor<T> is used but the underlying configuration source does not reload, you may still see old values, so you should check the reload settings and the change source.

A good interview answer is: IOptions is for stable values, IOptionsSnapshot is for per-request values, and IOptionsMonitor is for live updates and singleton use. In debugging, I first check the service lifetime, then whether the configuration source reloads, and finally whether the app is reading the current value from the correct abstraction.

1. Decide whether the value is stable or can change at runtime.
2. Use IOptions<T> for stable, app-wide settings.
3. Use IOptionsSnapshot<T> in controllers or request handlers that should see updated settings on each request.
4. Use IOptionsMonitor<T> in singleton background workers, hosted services, caches, or long-running processors that must react to configuration updates.
5. When debugging, confirm the DI lifetime, then confirm config reload behavior, then verify the value being read at runtime.

Time cost is small because reading options is cheap. Memory cost is also small, but Snapshot creates a fresh copy per request scope. The main maintenance cost is choosing the right lifetime. The wrong choice can cause stale values, DI errors, or confusing bugs during runtime.

A common mistake is injecting IOptionsSnapshot<T> into a singleton and then getting a lifetime error. Another mistake is expecting IOptions<T> to refresh automatically. A third mistake is using IOptionsSnapshot<T> outside a request scope and wondering why the value does not behave like a live update. Another is forgetting that IOptionsMonitor<T> still depends on the configuration source being set up to reload.

Say the lifetime rule first, then the debugging clue. That shows both API knowledge and practical judgment.

A reverse proxy can hide the original scheme, host, and client IP from the app. In ASP.NET Core, you usually need forwarded headers processed early so redirects, authentication, logging, and link generation use the real request context instead of the proxy’s internal one.

This question asks what changes when another server sits in front of your app. The app may not see the visitor's real web address, secure connection, or home address directly. Instead, it may only see the values from the middle server unless those original details are passed along. That can make a test on your laptop look correct while the live site acts differently. The interviewer wants to know whether you can spot that hidden change, find where it happens, and explain why it affects page jumps, sign-in steps, and links.

1. Is HTTPS terminated at the proxy before the request reaches the app?
2. Are forwarded headers and trusted proxy settings already configured?

The practical difference is that the app no longer receives the client request exactly as it left the browser. The reverse proxy may terminate TLS, rewrite the host, change the scheme to HTTP internally, and pass the real values in forwarded headers such as X-Forwarded-For, X-Forwarded-Proto, and X-Forwarded-Host.

In ASP.NET Core, I would process forwarded headers early in the pipeline, before HTTPS redirection, authentication, authorization, or link generation. That lets the app rebuild the original request context and avoid bad redirects, wrong callback URLs, incorrect cookies, or logs that show the proxy instead of the real client.

For debugging, I would compare local direct requests with production-like proxy requests, inspect the incoming headers, confirm KnownProxies or KnownNetworks, and check the observed Scheme, Host, and RemoteIpAddress. The root issue is often not the controller code. It is the request metadata that changed on the way in.

1. Reproduce the problem with and without the proxy.
2. Inspect the observed scheme, host, path base, and client IP.
3. Verify forwarded headers are enabled and run first.
4. Confirm trusted proxy settings.
5. Check redirects, auth callbacks, and absolute links.
6. Add regression tests for proxy and non-proxy paths.

The runtime cost is usually small. The main cost is configuration and testing. The hard part is not CPU or memory. It is making sure the app sees the same request information in every environment and that the middleware order is correct.

Common mistakes are reading Scheme or Host before forwarded headers run, trusting raw proxy headers from any client, forgetting KnownProxies or KnownNetworks, placing HTTPS redirection before the forwarded headers middleware, and assuming local direct requests behave like production.

Lead with one clear rule: restore the original request context first, then let the rest of the pipeline use it.

The API likely ran one query for the product list and then hidden follow-up queries from lazy loading or a per-row lookup. That is a classic N+1 pattern. I would trace the request, find the repeated SQL, and verify the fix by rerunning the same endpoint.

The question is asking why one action that should return 200 items still caused one extra hidden trip to where the app keeps its data. The likely reason is that the program loaded the main list and then asked for more details item by item instead of getting everything together. In an interview, you should explain how to find that hidden call, why it points to the common one-by-one lookup problem, and how to prove the fix by checking the request again after the change.

1. Is Entity Framework Core using lazy loading here?
2. Are any related properties accessed inside a loop?
3. Should the endpoint return related data or only product fields?

I would first reproduce the endpoint and confirm the query count with SQL Profiler, EF Core logging, or a command interceptor. Then I would look for one query that loads the products and many similar queries that repeat for each product or each related entity. That is the typical N+1 pattern.

The most likely cause is lazy loading or code that touches a navigation property inside a loop. For example, the API may load 200 products and then issue one query per product for related data such as category, supplier, or reviews. In your case, 201 total queries usually means one main query plus 200 follow-up queries, not just one harmless extra call.

To verify the root cause, I would correlate the SQL text with the request path, check the call stack or logs, and identify exactly where the repeated query starts. Then I would fix it by projecting the needed fields in one query, using Include only when appropriate, or disabling lazy loading if it is causing hidden database access. After the fix, I would rerun the same request and confirm that the query count drops, the response data stays correct, and the latency improves. I would also add a regression test or performance check so the N+1 pattern does not return.

1. Reproduce the endpoint and confirm the query count with SQL Profiler or EF Core SQL logs.
2. Identify the repeated SQL statement and the point in the request where it appears.
3. Check for lazy loading, navigation-property access, or a database call inside a loop.
4. Map the repeated query back to the exact code path, mapper, or serializer.
5. Fix the access pattern by projecting the needed data in one query or using the right eager-loading shape.
6. Rerun the request and verify the query count, response correctness, and latency.
7. Add a regression test or performance guard so the same pattern is caught later.

The cost is not about CPU only. It is mostly extra database round trips, which slow the API and increase database load. The hidden queries also make debugging harder and can create maintenance risk because the slowdown may appear only with real data. After the fix, the API usually becomes faster and more predictable.

A common mistake is to count only the main query and ignore extra queries caused by lazy loading, mapping, or serialization. Another mistake is to assume the extra query is harmless because the response still works. A third mistake is to fix the symptom without checking whether the query shape still returns the same data.

Say the evidence first, then the root cause, then the fix, and end with how you verified it. Interviewers like a diagnosis that is based on logs and traces, not a guess.

You injected a scoped, non-thread-safe DbContext into a singleton service. That creates a lifetime violation, so the same context can be reused after disposal or concurrently, causing random ObjectDisposedException, second-operation, and shared-state bugs. Resolve DbContext per work item through a scope or a factory.

A long-running app has one always-on worker that keeps using a database helper meant to be short-lived. That helper is shared longer than intended, so it can be used after it has been cleaned up or by two jobs at the same time. Because the timing changes from run to run, the app sometimes seems fine and sometimes fails. The question is asking you to spot that the helper is being kept in the wrong place, explain why that makes the errors come and go, and describe the safer way to get a fresh helper for each job.

1. Is the background service doing one job at a time, or can jobs overlap?
2. Should the fix use IServiceScopeFactory or IDbContextFactory<TContext>?

Start with the practical rule: a singleton background service must not hold a DbContext field. DbContext is scoped, which means it is created for one unit of work and then discarded. A singleton lives for the whole process, so it can outlive the context and also share it across threads. That causes intermittent failures such as ObjectDisposedException, InvalidOperationException about a second operation starting, stale tracked entities, or cross-thread access problems.

The root cause is the lifetime violation, not the database itself. The hosting model makes it intermittent because the Generic Host creates the background service once, but the service may run many iterations over time. Sometimes the old context still appears to work, sometimes it has already been disposed, and sometimes two overlapping operations touch it at the same time. Timing changes the symptom.

The correct fix is to resolve DbContext inside a scope for each iteration, or use IDbContextFactory<TContext> when that fits the design. I would also verify the fix by checking that every unit of work gets a new context instance and by watching for the random errors to disappear under load.

1. Reproduce the failure with logging and stack traces.
2. Confirm the service lifetime and the DbContext lifetime.
3. Check whether the DbContext is stored in a field on the singleton.
4. Create a scope per background operation, or switch to IDbContextFactory<TContext>.
5. Verify that no DbContext instance is reused across iterations or threads.
6. Add a regression test or load test to confirm the random errors are gone.

A common mistake is injecting DbContext directly into the singleton and keeping it in a field. Another mistake is creating one scope in the constructor and reusing it forever. A third mistake is blaming the database when the real bug is service lifetime and concurrency.

Say the lifetime rule first: singleton service plus scoped DbContext is invalid. Then name the symptoms, explain why they are intermittent, and finish with the fix: create a scope per work item or use a DbContext factory.

This usually means EF Core found a LINQ part it cannot convert to SQL, often a custom method or unsupported expression. I would reproduce the query, isolate the exact failing call or predicate, rewrite it to a SQL-friendly form, or move only the non-translatable part after materialization, then verify with logging and tests.

This question asks about a search in your program that looks normal, but fails because one part cannot be turned into something the database can use. The app starts the search, reaches that part, and stops with an error. In an interview, I would say I first find the smallest version that still breaks, read the error carefully, and then change the code so the database can handle it or move only the hard part after the data is loaded.

1. Which database provider and EF Core version are you using?
2. Can I see the exact LINQ query and full exception text?

This is usually a runtime translation failure, not a compile error. EF Core builds an expression tree from the LINQ query and tries to convert each part into SQL. One unsupported method call, custom helper, comparer, or predicate can break the whole query.

My first step is to reproduce the failure with the smallest possible input. Then I inspect the full exception, the stack trace, and the generated SQL using ToQueryString() or EF Core query logging. I remove one method call or predicate at a time until the failing piece is obvious.

The root-cause fix is to rewrite that piece into SQL-friendly operations, map a database function when appropriate, or move only the truly client-only part after AsEnumerable() or ToList() so filtering still happens in the database first. I do not switch to client evaluation too early, because that can load far too much data.

If production needs a temporary workaround, I narrow the query or precompute the value outside the query, but I treat that as containment, not the final fix. After the change, I rerun the query, add a regression test, and confirm paging, filtering, and sorting still work.

1. Reproduce the failure with the smallest possible input.
2. Read the full exception and stack trace.
3. Identify the exact method call, predicate, or projection that fails to translate.
4. Inspect the generated SQL with ToQueryString() or query logging.
5. Rewrite the failing part to use SQL-friendly expressions.
6. If needed, move only the non-translatable part after AsEnumerable() or ToList().
7. Verify the fix with tests and regression coverage.

The query itself usually does not get harder to run, but debugging takes time. The main cost is reading logs, shrinking the query, and testing the fix. If you move too much work to memory, you may add extra data transfer, slower response time, and more memory use.

A common mistake is assuming the database is broken when the LINQ expression is the real problem. Another is using AsEnumerable() too early and pulling too much data into memory. People also forget to check the exact failing call, or they replace the query with a broad client-side load that hurts performance.

Say the exact failing expression first, then explain how you would narrow it down and fix only the non-translatable part.

I would stop further damage first, then verify the live schema, migration history, and last known-good deployment. After that, I would repair the mismatch with a controlled rollback, restore, or corrective migration, validate on a clone, and only then bring production back with monitoring.

This question asks how you would handle a live system when the record of past changes no longer matches what is actually running. You need to show that you would not guess, rush, or make random edits. Instead, you would first find the last version that worked, check what changed, protect users from more damage, and then fix the mismatch in a careful, controlled way. The interviewer wants to hear that you verify before changing anything, pick the safest repair, and confirm the system works again afterward.

1. Is the live schema wrong, or only the migration history table out of sync?
2. Can I take the system read-only or schedule a short maintenance window?
3. Do we need to preserve new production data, or is a restore acceptable?

I would start by treating it as a production incident. My first step is to stop further writes if needed and collect evidence: the deployed app version, the migration history table, the current schema, logs, and any recent deployment metadata. Then I would verify the last good deployment by matching the release tag or commit SHA with the migration set and checking when the schema and app version were still aligned.

Next, I would decide whether the problem is only the history table or the actual schema. If the schema is correct but the history is wrong, I would reconcile it carefully, usually with a controlled script after validating on a clone. If the schema is damaged or data is unsafe, I would prefer restore or point-in-time recovery, then apply a forward fix. I would avoid rewriting shipped migrations in production. Finally, I would validate in a restored copy, redeploy, monitor, and add a prevention step such as migration checks in CI, release gates, and backup verification.

Freeze the blast radius and stop new damage.,Collect evidence from the deployed app, migration records, schema, logs, and release metadata.,Verify the last good deployment by matching the release tag or commit to the last known working schema.,Decide whether only the history is wrong or the live schema is wrong.,Choose the safest repair: restore, point-in-time recovery, or a controlled corrective change.,Test the fix on a clone first.,Apply the approved fix in production.,Validate behavior, then add CI and release checks to prevent repeat drift.

The work is not about code speed. The main cost is operational time: finding the last good release, checking the database, testing a fix, and restoring safely if needed. Larger databases make backup or restore slower. Memory use is usually small, but the maintenance cost is high because you need careful coordination and strong rollback planning.

A common mistake is editing already-shipped migration files and pretending the history is enough. Another mistake is deleting rows from the migration history table without checking the real schema first. People also skip backups, apply fixes directly in production without a clone, or forget that data may have changed after the bad migration. Another error is not verifying the exact release that last matched the database.

Lead with safety, evidence, and verification. Say what you would check first, how you would identify the last good release, and how you would choose between restore, corrective migration, or history reconciliation.

I compare the live database to the EF Core model and migration history, find the mismatch, and then repair it with a new migration or a controlled baseline. I avoid editing already-applied migrations and I verify the fix on a copy before deployment.

Sometimes a person changes the live data setup by hand instead of using the normal team process. That can make the running system and the saved history disagree. The question asks how you would spot that mismatch, decide which version should be kept, and fix things so future changes can still be delivered safely. The important part is to protect the existing data, avoid breaking the live app, and leave behind a clear, repeatable path for later updates. In practice, you would compare what is there now, what the team wrote down, and the plan, then choose the safest repair.

1. Was the manual change done only in production, or in other environments too?
2. Is the manual change the intended final state, or should it be reversed?
3. Is there any data that must be preserved exactly as-is?

I would treat this as schema drift: the database on disk no longer matches the schema EF Core thinks it manages. The first step is reproduction on a safe copy of the database. Then I compare three things: the live schema, the EF Core migration files, and the migration history table. That tells me whether the drift came from a direct SQL change, a missing migration, or a migration that was edited after it was deployed.

My main rule is not to rewrite an already-applied migration. If the manual change is the correct end state, I create a new corrective migration so the source-controlled history catches up to reality. If the live change was only an emergency hotfix, I keep it as a forward migration and apply it consistently across environments. If the live database should not have changed, I create a safe, data-preserving rollback or fix-up migration.

After that, I generate an idempotent script, test it on a clone, and run the app against the updated schema. The goal is a deployable path where future releases can apply cleanly again.

This approach works because it separates evidence, containment, and repair. The evidence is the live schema and migration history. The containment is stopping more drift. The repair is a new, versioned migration or a controlled baseline, never a silent manual fix.

If the manual change includes data movement, I keep the data script inside the migration or in a separate one-time fix, and I test it on restored data first. That way I protect data and keep the deployment repeatable.

1. Restore or clone the live database so you can investigate safely.
2. Compare the live schema with the EF Core model snapshot, migration files, and __EFMigrationsHistory.
3. Identify the exact object-level drift: tables, columns, indexes, constraints, defaults, seed data, or data changes.
4. Decide which state is correct: the live schema or the source-controlled migration path.
5. If the live change is intended, create a new corrective migration that captures it.
6. If the live change is accidental, create a safe fix-up or rollback migration that preserves data.
7. Avoid editing migrations that have already been applied in shared environments.
8. Generate an idempotent SQL script and test it on a copy.
9. Verify application startup, queries, and rollback/upgrade paths.
10. Lock in prevention: require migrations in deployment and block direct schema changes.

The comparison work is usually quick, but the safe repair takes time because you must test carefully. The memory cost is low. The main cost is operational risk: a wrong fix can break deployments or lose data. Testing on a copy adds time, but it lowers that risk a lot.

Editing an already-applied migration, deleting rows from the migration history table without a plan, trusting only the EF snapshot, ignoring indexes or constraints, forgetting data changes, testing only on a developer database, and using a destructive drop-and-recreate fix when the data must be preserved.

Say that you compare evidence first, preserve data second, and restore repeatable deployments last. That shows you understand both debugging and release safety.

With a normal relational SaveChanges call, EF Core treats the batch as one unit. If the third entity fails, the whole save fails and the other four are not committed. The earlier successful changes are rolled back, so you do not get a partial database update.

The question asks whether a group of changes stays together or gets partly saved when one item breaks. Imagine updating five records at once. If the third one fails, the interviewer wants to know whether the other four stay saved or are removed too. The expected answer is about one save acting like one package, and what that means for recovery after the failure.

1. Is this one SaveChanges call against a relational database?
2. Do you want all five changes to succeed together, or can some succeed independently?

With EF Core on a relational database, SaveChanges normally runs the pending changes in a transaction, which means the batch is atomic: it either all succeeds or all fails. If the third entity fails, the earlier two are rolled back and the later two are not committed. So the database does not end up with a partial update.

I would also mention one important nuance: the database rollback is not the same as the in-memory change tracker. After a failure, the DbContext may still hold tracked state that you need to inspect or clear before retrying. If partial success is acceptable, you must split the work into separate SaveChanges calls or design an explicit transaction boundary around only the parts that must stay together. In debugging, I would first confirm the exception, check the transaction boundary, and then verify the final database state.

1. Confirm that all five changes are part of one SaveChanges call.
2. Verify that the provider is a relational database with transaction support.
3. Reproduce the failure and capture the exception and stack trace.
4. Check whether the transaction rolled back in the database.
5. Inspect DbContext state separately from database state.
6. Decide whether the fix is better error handling, a smaller batch, or an explicit transaction boundary.

The normal cost is one database save plus one rollback if something fails. Memory use is small because EF Core tracks the entities in the DbContext. The bigger cost is debugging the failure and deciding whether to retry, split the work, or change the transaction design.

A common mistake is thinking the first successful updates stay saved even though one later change failed. Another mistake is assuming the DbContext automatically resets itself after the exception. People also forget that partial success requires separate save boundaries or explicit transaction design.

Start with the one-sentence rule: one SaveChanges call usually means one transaction. Then add the rollback detail and the note that in-memory tracking is separate from database commit.

I would reproduce the slow request, measure each part separately, and compare server time, database time, serialization, and network time. Then I would fix the slowest verified part first, re-test, and keep the same measurements so I know the improvement is real.

This question asks how you would find out why one web call feels slow. You should not guess. First repeat the same call and measure the time for each main part separately. Check how long the program spends doing its own work, how long it waits for stored data, how long it takes to turn the result into text, and how long the reply takes to travel back. The goal is to prove the slow part with evidence before changing anything.

1. Is the 800ms measured on the server, the client, or both?
2. Is this slow for one request type or for all traffic?
3. Did the slowdown start after a recent change?
4. Do we have logs, traces, or database timings already?

I would start with reproduction and scope. I would confirm whether the 800ms is end-to-end latency or only server time. Then I would break the request into parts: application work, database access, serialization, and network transfer.

For the application side, I would add timing around the main code path, use structured logs, and inspect traces if needed. For database time, I would measure the exact query, look at query plans, and check round trips, indexing, and rows returned. For serialization, I would compare object size and response shape, because large payloads can be expensive to convert to JSON. For network time, I would compare server timing with client timing, check payload size, compression, and any proxy or TLS overhead.

I would not optimize until I know which part is actually slow. If database time is the issue, I would fix the query or indexes. If serialization is the issue, I would reduce the payload or simplify the response. If network time is the issue, I would reduce response size or add compression. After each change, I would re-measure the same request so I can prove the improvement.

The main tradeoff is speed of diagnosis versus depth of measurement. A quick first pass gives direction, but a deeper trace gives proof. In production, I would prefer low-risk measurements first, then use detailed tracing only if the simple timings do not isolate the bottleneck.

1. Reproduce the slow request with the same inputs.
2. Decide whether 800ms is server time or end-to-end time.
3. Measure app work, database time, serialization time, and network time separately.
4. Use logs and traces to find the slow segment.
5. Inspect the slow segment in detail: query plan, payload size, CPU work, or client/network path.
6. Fix the proven bottleneck only.
7. Re-run the same test and compare before/after numbers.
8. Add regression checks so the issue does not return.

The main cost is investigation time, not CPU or memory. Good measurement tools add a little overhead, but that is usually worth it. The bigger cost is if you optimize the wrong part and make the system more complex without real benefit.

A common mistake is guessing that the database is slow without measuring it. Another mistake is looking only at total request time and missing serialization or network cost. People also often test with different inputs, which hides the real issue. A final mistake is fixing code before proving the bottleneck.

Say that you measure first, split the request into parts, and only optimize the proven slow step. That shows discipline, not guesswork.