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Exam Code: DP-800

Exam Name: Developing AI-Enabled Database Solutions

Certification Provider: Microsoft

Corresponding Certification: Microsoft Certified: SQL AI Developer Associate

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Microsoft DP-800 Practice Test 44

Question 430

A SQL Server transaction log grows continuously after a failed nightly process. Which result distinguishes a successful implementation from a cosmetic change?

  1. Failing requests recover within limits or surface errors without a retry amplification storm.
  2. The offending retention cause is resolved and log growth returns to expected levels.
  3. The spill disappears or decreases and the query meets latency targets without new regressions.
  4. The unexpected object or configuration is reported before any destructive deployment.

Explanation

Correct Answer

B:

Log capacity symptoms require diagnosis of transactions and truncation; blindly shrinking can recur. The observation “The offending retention cause is resolved and log growth returns to expected levels” directly confirms the chosen action worked for this situation: A SQL Server transaction log grows continuously after a failed nightly process.

Incorrect Answers

A:

Retry logic must be finite and targeted; uncontrolled retries worsen resource contention. This observation would not establish that the required result occurred for A SQL Server transaction log grows continuously after a failed nightly process. The confirming test is: The offending retention cause is resolved and log growth returns to expected levels.

C:

Tempdb spills indicate memory/workload mismatch and should be diagnosed from actual plans. This observation would not establish that the required result occurred for A SQL Server transaction log grows continuously after a failed nightly process. The confirming test is: The offending retention cause is resolved and log growth returns to expected levels.

D:

Schema drift occurs when deployed objects differ from version-controlled declarative models. This observation would not establish that the required result occurred for A SQL Server transaction log grows continuously after a failed nightly process. The confirming test is: The offending retention cause is resolved and log growth returns to expected levels.

Question 431

An Azure SQL workload exhibits persistent log write pressure and delayed truncation. Which solution is the most appropriate for the scenario?

  1. Inspect active transactions, log usage, and recovery/configuration conditions before resizing.
  2. Project only required columns and measure a covering index or columnstore fit.
  3. Configure Azure Monitor metrics, diagnostic logs, and appropriate alerts with actionable thresholds.
  4. Instrument the app with Application Insights and SQL diagnostics with shared correlation context.

Explanation

Correct Answer

A:

Log capacity symptoms require diagnosis of transactions and truncation; blindly shrinking can recur. For the described situation—An Azure SQL workload exhibits persistent log write pressure and delayed truncation—the targeted fix is Inspect active transactions, log usage, and recovery/configuration conditions before resizing. A successful test should show: The offending retention cause is resolved and log growth returns to expected levels.

Incorrect Answers

B:

Overfetching wastes IO and network capacity; targeted projection can allow lean index access. But that technique does not solve the stated problem—An Azure SQL workload exhibits persistent log write pressure and delayed truncation. The needed intervention is Inspect active transactions, log usage, and recovery/configuration conditions before resizing.

C:

Monitoring becomes operationally useful when routed to timely alerts rather than passive dashboards. But that technique does not solve the stated problem—An Azure SQL workload exhibits persistent log write pressure and delayed truncation. The needed intervention is Inspect active transactions, log usage, and recovery/configuration conditions before resizing.

D:

Application Insights complements database-level metrics by connecting application requests to dependencies. But that technique does not solve the stated problem—An Azure SQL workload exhibits persistent log write pressure and delayed truncation. The needed intervention is Inspect active transactions, log usage, and recovery/configuration conditions before resizing.

Question 432

An Azure SQL workload exhibits persistent log write pressure and delayed truncation. What should a technical reviewer verify before approving completion?

  1. Representative parameter values meet latency targets without breaking atypical cases.
  2. The plan identifies the high-cost operator and a tested change reduces duration and logical reads.
  3. Job execution order matches the declared gates and failed jobs prevent downstream release.
  4. The offending retention cause is resolved and log growth returns to expected levels.

Explanation

Correct Answer

D:

Log capacity symptoms require diagnosis of transactions and truncation; blindly shrinking can recur. The observation “The offending retention cause is resolved and log growth returns to expected levels” directly confirms the chosen action worked for this situation: An Azure SQL workload exhibits persistent log write pressure and delayed truncation.

Incorrect Answers

A:

Query Store keeps plan/runtime history and can help isolate parameter-sensitive plan regressions. This observation would not establish that the required result occurred for An Azure SQL workload exhibits persistent log write pressure and delayed truncation. The confirming test is: The offending retention cause is resolved and log growth returns to expected levels.

B:

Execution plans show access paths and join strategies; blind indexing is not reliable diagnosis. This observation would not establish that the required result occurred for An Azure SQL workload exhibits persistent log write pressure and delayed truncation. The confirming test is: The offending retention cause is resolved and log growth returns to expected levels.

C:

CI/CD stage dependencies guarantee required validation precedes a deployment action. This observation would not establish that the required result occurred for An Azure SQL workload exhibits persistent log write pressure and delayed truncation. The confirming test is: The offending retention cause is resolved and log growth returns to expected levels.

Question 433

A key range query is slow despite a new index because the WHERE predicate wraps its indexed column in a function. What should the SQL developer implement after verifying the conditions?

  1. Inspect active transactions, log usage, and recovery/configuration conditions before resizing.
  2. Inspect execution-plan estimates and update relevant statistics before proposing disruptive hints.
  3. Rewrite predicates to sargable range conditions where semantics are preserved.
  4. Create a dedicated minimal database role with only required schema and object grants.

Explanation

Correct Answer

C:

Applying functions to indexed columns often prevents efficient predicate seek pushdown. For the described situation—A key range query is slow despite a new index because the WHERE predicate wraps its indexed column in a function—the targeted fix is Rewrite predicates to sargable range conditions where semantics are preserved. A successful test should show: A query plan uses the intended index range seek and returns identical date-filtered rows.

Incorrect Answers

A:

Log capacity symptoms require diagnosis of transactions and truncation; blindly shrinking can recur. But that technique does not solve the stated problem—A key range query is slow despite a new index because the WHERE predicate wraps its indexed column in a function. The needed intervention is Rewrite predicates to sargable range conditions where semantics are preserved.

B:

Statistics guide cardinality estimation; stale distributions can cause inefficient joins and memory grants. But that technique does not solve the stated problem—A key range query is slow despite a new index because the WHERE predicate wraps its indexed column in a function. The needed intervention is Rewrite predicates to sargable range conditions where semantics are preserved.

D:

Least privilege limits damage from compromised application identities and unintended queries. But that technique does not solve the stated problem—A key range query is slow despite a new index because the WHERE predicate wraps its indexed column in a function. The needed intervention is Rewrite predicates to sargable range conditions where semantics are preserved.

Question 434

A key range query is slow despite a new index because the WHERE predicate wraps its indexed column in a function. Which verification finding is most specific to this scenario?

  1. Blocked request durations fall while consistency requirements still hold.
  2. Failing requests recover within limits or surface errors without a retry amplification storm.
  3. A query plan uses the intended index range seek and returns identical date-filtered rows.
  4. A headless CI build validates the project and emits the expected deployable DACPAC.

Explanation

Correct Answer

C:

Applying functions to indexed columns often prevents efficient predicate seek pushdown. The observation “A query plan uses the intended index range seek and returns identical date-filtered rows” directly confirms the chosen action worked for this situation: A key range query is slow despite a new index because the WHERE predicate wraps its indexed column in a function.

Incorrect Answers

A:

Long transactions prolong lock retention, and extra indexes alone cannot resolve idle open transactions. This observation would not establish that the required result occurred for A key range query is slow despite a new index because the WHERE predicate wraps its indexed column in a function. The confirming test is: A query plan uses the intended index range seek and returns identical date-filtered rows.

B:

Retry logic must be finite and targeted; uncontrolled retries worsen resource contention. This observation would not establish that the required result occurred for A key range query is slow despite a new index because the WHERE predicate wraps its indexed column in a function. The confirming test is: A query plan uses the intended index range seek and returns identical date-filtered rows.

D:

SDK-style SQL projects support standardized build workflows and target-platform validation. This observation would not establish that the required result occurred for A key range query is slow despite a new index because the WHERE predicate wraps its indexed column in a function. The confirming test is: A query plan uses the intended index range seek and returns identical date-filtered rows.

Question 435

A service filters on YEAR(OrderDate) instead of a bounded date range and scans records. Which approach achieves the intended result without bypassing the stated controls?

  1. Rewrite predicates to sargable range conditions where semantics are preserved.
  2. Inspect spills/memory grants and adjust query/indexing or capacity based on measured plans.
  3. Identify the cycle and standardize access order, shorten transactions, and add bounded retry on deadlock victims.
  4. Expose procedures only when necessary and enforce per-role execute permissions and parameter validation.

Explanation

Correct Answer

A:

Applying functions to indexed columns often prevents efficient predicate seek pushdown. For the described situation—A service filters on YEAR(OrderDate) instead of a bounded date range and scans records—the targeted fix is Rewrite predicates to sargable range conditions where semantics are preserved. A successful test should show: A query plan uses the intended index range seek and returns identical date-filtered rows.

Incorrect Answers

B:

Tempdb spills indicate memory/workload mismatch and should be diagnosed from actual plans. But that technique does not solve the stated problem—A service filters on YEAR(OrderDate) instead of a bounded date range and scans records. The needed intervention is Rewrite predicates to sargable range conditions where semantics are preserved.

C:

Deadlocks form cycles of lock dependencies; a victim rollback must be handled and root lock patterns improved. But that technique does not solve the stated problem—A service filters on YEAR(OrderDate) instead of a bounded date range and scans records. The needed intervention is Rewrite predicates to sargable range conditions where semantics are preserved.

D:

API schema exposure does not supersede stored-procedure privileges or input governance. But that technique does not solve the stated problem—A service filters on YEAR(OrderDate) instead of a bounded date range and scans records. The needed intervention is Rewrite predicates to sargable range conditions where semantics are preserved.

Question 436

A service filters on YEAR(OrderDate) instead of a bounded date range and scans records. How should the team validate the selected design?

  1. A controlled test triggers the intended alert with enough context for response.
  2. Load tests show reduced latch waits and consistent insert correctness.
  3. Oversized queries are denied or paged while ordinary client requests meet latency goals.
  4. A query plan uses the intended index range seek and returns identical date-filtered rows.

Explanation

Correct Answer

D:

Applying functions to indexed columns often prevents efficient predicate seek pushdown. The observation “A query plan uses the intended index range seek and returns identical date-filtered rows” directly confirms the chosen action worked for this situation: A service filters on YEAR(OrderDate) instead of a bounded date range and scans records.

Incorrect Answers

A:

Monitoring becomes operationally useful when routed to timely alerts rather than passive dashboards. This observation would not establish that the required result occurred for A service filters on YEAR(OrderDate) instead of a bounded date range and scans records. The confirming test is: A query plan uses the intended index range seek and returns identical date-filtered rows.

B:

Page-level hot spots can constrain concurrency beyond disk capacity; fixes require evidence of actual latch waits. This observation would not establish that the required result occurred for A service filters on YEAR(OrderDate) instead of a bounded date range and scans records. The confirming test is: A query plan uses the intended index range seek and returns identical date-filtered rows.

C:

GraphQL and REST query surfaces require limits to prevent accidental overfetch and resource exhaustion. This observation would not establish that the required result occurred for A service filters on YEAR(OrderDate) instead of a bounded date range and scans records. The confirming test is: A query plan uses the intended index range seek and returns identical date-filtered rows.

Question 437

An Azure SQL service hits its resource ceiling during peak connections and batch jobs. What intervention should the responsible engineer choose?

  1. Implement optimistic concurrency using a rowversion or equivalent checked update token.
  2. Inspect actual execution plans and relevant Query Store runtime statistics before changing indexes.
  3. Use monitoring and workload profiling to choose suitable capacity/configuration changes.
  4. Constrain network access using supported private endpoints and firewall or network policy.

Explanation

Correct Answer

C:

Capacity selection should reflect measured demand, not only the number of users. For the described situation—An Azure SQL service hits its resource ceiling during peak connections and batch jobs—the targeted fix is Use monitoring and workload profiling to choose suitable capacity/configuration changes. A successful test should show: Peak load tests meet resource, latency, and cost limits after the chosen adjustment.

Incorrect Answers

A:

Optimistic concurrency compares version tokens to prevent lost updates without holding long locks. But that technique does not solve the stated problem—An Azure SQL service hits its resource ceiling during peak connections and batch jobs. The needed intervention is Use monitoring and workload profiling to choose suitable capacity/configuration changes.

B:

Execution plans show access paths and join strategies; blind indexing is not reliable diagnosis. But that technique does not solve the stated problem—An Azure SQL service hits its resource ceiling during peak connections and batch jobs. The needed intervention is Use monitoring and workload profiling to choose suitable capacity/configuration changes.

D:

Network isolation limits reachability but complements rather than replaces SQL authentication and grants. But that technique does not solve the stated problem—An Azure SQL service hits its resource ceiling during peak connections and batch jobs. The needed intervention is Use monitoring and workload profiling to choose suitable capacity/configuration changes.

Question 438

An Azure SQL service hits its resource ceiling during peak connections and batch jobs. What observable outcome proves the recommendation worked?

  1. Peak load tests meet resource, latency, and cost limits after the chosen adjustment.
  2. Load tests show reduced latch waits and consistent insert correctness.
  3. A controlled test triggers the intended alert with enough context for response.
  4. The workload connects without a stored SQL secret and denied operations remain blocked.

Explanation

Correct Answer

A:

Capacity selection should reflect measured demand, not only the number of users. The observation “Peak load tests meet resource, latency, and cost limits after the chosen adjustment” directly confirms the chosen action worked for this situation: An Azure SQL service hits its resource ceiling during peak connections and batch jobs.

Incorrect Answers

B:

Page-level hot spots can constrain concurrency beyond disk capacity; fixes require evidence of actual latch waits. This observation would not establish that the required result occurred for An Azure SQL service hits its resource ceiling during peak connections and batch jobs. The confirming test is: Peak load tests meet resource, latency, and cost limits after the chosen adjustment.

C:

Monitoring becomes operationally useful when routed to timely alerts rather than passive dashboards. This observation would not establish that the required result occurred for An Azure SQL service hits its resource ceiling during peak connections and batch jobs. The confirming test is: Peak load tests meet resource, latency, and cost limits after the chosen adjustment.

D:

Managed identity enables token-based passwordless workload authentication, subject to identity and SQL grants. This observation would not establish that the required result occurred for An Azure SQL service hits its resource ceiling during peak connections and batch jobs. The confirming test is: Peak load tests meet resource, latency, and cost limits after the chosen adjustment.

Question 439

A database has stable plans but waits show CPU and IO saturation at the current service tier. Which design choice BEST addresses the root issue?

  1. Configure Azure Monitor metrics, diagnostic logs, and appropriate alerts with actionable thresholds.
  2. Use monitoring and workload profiling to choose suitable capacity/configuration changes.
  3. Rewrite predicates to sargable range conditions where semantics are preserved.
  4. Constrain network access using supported private endpoints and firewall or network policy.

Explanation

Correct Answer

B:

Capacity selection should reflect measured demand, not only the number of users. For the described situation—A database has stable plans but waits show CPU and IO saturation at the current service tier—the targeted fix is Use monitoring and workload profiling to choose suitable capacity/configuration changes. A successful test should show: Peak load tests meet resource, latency, and cost limits after the chosen adjustment.

Incorrect Answers

A:

Monitoring becomes operationally useful when routed to timely alerts rather than passive dashboards. But that technique does not solve the stated problem—A database has stable plans but waits show CPU and IO saturation at the current service tier. The needed intervention is Use monitoring and workload profiling to choose suitable capacity/configuration changes.

C:

Applying functions to indexed columns often prevents efficient predicate seek pushdown. But that technique does not solve the stated problem—A database has stable plans but waits show CPU and IO saturation at the current service tier. The needed intervention is Use monitoring and workload profiling to choose suitable capacity/configuration changes.

D:

Network isolation limits reachability but complements rather than replaces SQL authentication and grants. But that technique does not solve the stated problem—A database has stable plans but waits show CPU and IO saturation at the current service tier. The needed intervention is Use monitoring and workload profiling to choose suitable capacity/configuration changes.

Question 440

A database has stable plans but waits show CPU and IO saturation at the current service tier. Which result would provide the strongest acceptance evidence?

  1. Peak load tests meet resource, latency, and cost limits after the chosen adjustment.
  2. A stale write is detected and retried or rejected rather than silently overwriting new data.
  3. Concurrent reports avoid dirty data while meeting tested read/write throughput goals.
  4. Only authorized auditors can view or alter audit records and retention meets policy.

Explanation

Correct Answer

A:

Capacity selection should reflect measured demand, not only the number of users. The observation “Peak load tests meet resource, latency, and cost limits after the chosen adjustment” directly confirms the chosen action worked for this situation: A database has stable plans but waits show CPU and IO saturation at the current service tier.

Incorrect Answers

B:

Optimistic concurrency compares version tokens to prevent lost updates without holding long locks. This observation would not establish that the required result occurred for A database has stable plans but waits show CPU and IO saturation at the current service tier. The confirming test is: Peak load tests meet resource, latency, and cost limits after the chosen adjustment.

C:

READ UNCOMMITTED or NOLOCK may read uncommitted changes; row versioning can improve concurrency without dirty reads. This observation would not establish that the required result occurred for A database has stable plans but waits show CPU and IO saturation at the current service tier. The confirming test is: Peak load tests meet resource, latency, and cost limits after the chosen adjustment.

D:

Audit credibility depends on destination protection and retention, not just enabling the event stream. This observation would not establish that the required result occurred for A database has stable plans but waits show CPU and IO saturation at the current service tier. The confirming test is: Peak load tests meet resource, latency, and cost limits after the chosen adjustment.

Developing AI-Enabled Database Solutions (DP-800) Practice Tests