McAfee Secure

Exam Code: DP-800

Exam Name: Developing AI-Enabled Database Solutions

Certification Provider: Microsoft

Corresponding Certification: Microsoft Certified: SQL AI Developer Associate

Microsoft DP-800 Questions & Answers

Study with Up-To-Date REAL Exam Questions and Answers from the ACTUAL Test

101 Questions & Answers with Testing Engine

"Developing AI-Enabled Database Solutions Exam", also known as DP-800 exam, is a Microsoft certification exam.

Pass your tests with the always up-to-date DP-800 Exam Engine. Your DP-800 training materials keep you at the head of the pack!

Satisfaction Guaranteed

Money Back Guarantee

Testking provides hassle-free money back guarantee with our products. That is because we have 100% trust in the abilities of our professional and experience product team, and our record is a proof of that.

99.6% PASS RATE
Was: $137.49
Now: $124.99

Microsoft DP-800 Practice Test 72

Question 710

Two deployment environments use different currency lookup codes, causing inconsistent reports. Which ongoing control is MOST relevant after the issue is fixed?

  1. Rotate exposed secrets and enable precommit scanning.
  2. Review seed-data changes like schema changes.
  3. Define drift policy for emergency fixes.
  4. Reconcile sanctioned hotfixes into source control promptly.

Explanation

Correct Answer

B:

Review seed-data changes like schema changes. This guards against recurrence because untracked static data can drift even when table definitions match. The direct remediation is: Version reference data alongside schema and apply idempotent migration scripts.

Incorrect Answers

A:

Rotate exposed secrets and enable precommit scanning. The current task instead requires this action: review seed-data changes like schema changes.

C:

Define drift policy for emergency fixes. The current task instead requires this action: review seed-data changes like schema changes.

D:

Reconcile sanctioned hotfixes into source control promptly. The current task instead requires this action: review seed-data changes like schema changes.

Question 711

A payment platform stores exact currency amounts and currently uses FLOAT, producing rounding differences. What is the MOST appropriate immediate corrective approach?

  1. Select BIGINT after modeling value range, foreign keys and migration effects.
  2. Evaluate supported JSON index or indexed computed-column path for that SQL platform and version.
  3. Replace FLOAT money arithmetic with appropriately sized DECIMAL precision and scale.
  4. Use a shared SQL SEQUENCE with NEXT VALUE FOR in supported database environments.

Explanation

Correct Answer

C:

Replace FLOAT money arithmetic with appropriately sized DECIMAL precision and scale. This targets the primary issue because binary floating-point approximations cannot represent every decimal currency value exactly. Verification: Reconciled payment totals match control calculations to the cent. Prevent recurrence: Keep precision/scale decisions in schema review and financial regression tests.

Incorrect Answers

A:

Selecting BIGINT after modeling value range, foreign keys and migration effects would not correct a payment platform stores exact currency amounts and currently uses FLOAT, producing rounding differences. Replace FLOAT money arithmetic with appropriately sized DECIMAL precision and scale.

B:

Evaluate supported JSON index or indexed computed-column path for that SQL platform and version. The relevant technical condition instead is that replace FLOAT money arithmetic with appropriately sized DECIMAL precision and scale.

D:

A sequence can allocate numbers independently of one table identity property.

Question 712

A partition scheme maps every new monthly data slice into the final partition unexpectedly. What observed evidence would BEST confirm successful remediation?

  1. A tested switch moves the intended range without orphaning rows.
  2. Verification detects a controlled tampering attempt against the ledger history.
  3. Sample records validate JSON paths while required keys remain enforced.
  4. New monthly rows land in the intended partition after the split.

Explanation

Correct Answer

D:

New monthly rows land in the intended partition after the split. This is the decisive observable evidence after implementing manage the partition function with properly planned SPLIT RANGE boundaries. It tests the underlying requirement: Missing future boundaries force incoming values into the existing terminal partition.

Incorrect Answers

A:

A tested switch moves the intended range without orphaning rows. However, that result would not establish the required outcome: new monthly rows land in the intended partition after the split.

B:

Verification detects a controlled tampering attempt against the ledger history. However, that result would not establish the required outcome: new monthly rows land in the intended partition after the split.

C:

Sample records validate JSON paths while required keys remain enforced. However, that result would not establish the required outcome: new monthly rows land in the intended partition after the split.

Question 713

A finance bot must add exact invoice totals rather than compose explanatory prose. What is the MOST relevant technical distinction in this situation?

  1. Numeric reconciliation is a structured calculation, not a reason to hallucinate an answer from documents.
  2. RAG is appropriate only if the retriever enforces contractual permissions.
  3. An authorization error occurs before model inference can produce valid content.
  4. Context-window constraints and cost require bounded prompt construction.

Explanation

Correct Answer

A:

Numeric reconciliation is a structured calculation, not a reason to hallucinate an answer from documents. This explains why the corrective action is to use deterministic SQL aggregation for arithmetic and add RAG only for explanatory source text if needed. Confirm the diagnosis when the computed amount matches audited ledger SQL exactly.

Incorrect Answers

B:

RAG is appropriate only if the retriever enforces contractual permissions. The actual technical constraint is that numeric reconciliation is a structured calculation, not a reason to hallucinate an answer from documents.

C:

An authorization error occurs before model inference can produce valid content. The actual technical constraint is that numeric reconciliation is a structured calculation, not a reason to hallucinate an answer from documents.

D:

Context-window constraints and cost require bounded prompt construction. The actual technical constraint is that numeric reconciliation is a structured calculation, not a reason to hallucinate an answer from documents.

Question 714

A catalog embeds a numeric inventory count into every product description and regenerates all vectors on each sale. What maintenance or governance control BEST reduces recurrence of this problem?

  1. Apply column allowlists and pre-inference DLP checks.
  2. Define embedding input contract and refresh triggers per field.
  3. Remove deleted chunks promptly and verify index consistency.
  4. Keep section IDs and overlap rules versioned.

Explanation

Correct Answer

B:

Define embedding input contract and refresh triggers per field. This guards against recurrence because frequent numeric fields add update cost and can weaken semantic relevance. The direct remediation is: Keep rapidly changing structured quantities as filterable columns outside the descriptive embedding text.

Incorrect Answers

A:

Apply column allowlists and pre-inference DLP checks. The current task instead requires this action: define embedding input contract and refresh triggers per field.

C:

Remove deleted chunks promptly and verify index consistency. The current task instead requires this action: define embedding input contract and refresh triggers per field.

D:

Keep section IDs and overlap rules versioned. The current task instead requires this action: define embedding input contract and refresh triggers per field.

Question 715

A query uses NOT IN to find suppliers with no invoices but invoice SupplierId can contain NULL. Which implementation decision BEST addresses this requirement?

  1. Use a correlated aggregate subquery or window function scoped by category.
  2. Use JSON_OBJECT with explicit key/value expressions where supported.
  3. Prefer a properly correlated NOT EXISTS with matching key conditions.
  4. Use OPENJSON on the reviewer array with a typed WITH clause.

Explanation

Correct Answer

C:

Prefer a properly correlated NOT EXISTS with matching key conditions. This targets the primary issue because NULL in a NOT IN subquery can yield UNKNOWN and unexpectedly filter all rows. Verification: Suppliers with no invoice are returned correctly with null-containing test data. Prevent recurrence: Keep foreign-key columns and nullable policies explicit.

Incorrect Answers

A:

An outer reference lets the inner aggregate vary by each row's category.

B:

JSON_OBJECT constructs a JSON object from typed expressions instead of fragile string concatenation.

D:

OPENJSON expands array elements into tabular rows for joins and validation.

Question 716

A vector corpus uses cosine similarity but index and query metrics are inconsistent. Which measurable outcome BEST verifies that the requirement is satisfied?

  1. Recall improves on a holdout set while latency remains acceptable.
  2. The quoted phrase test excludes documents without that phrase.
  3. Questions correctly distinguish indexed approximate search from exact KNN behavior.
  4. The same labeled nearest-neighbor fixtures rank consistently.

Explanation

Correct Answer

D:

The same labeled nearest-neighbor fixtures rank consistently. This is the decisive observable evidence after implementing align metric and model embedding guidance across index creation and query evaluation. It tests the underlying requirement: Distance semantics affect neighbor ordering and recall.

Incorrect Answers

A:

Recall improves on a holdout set while latency remains acceptable. However, that result would not establish the required outcome: the same labeled nearest-neighbor fixtures rank consistently.

B:

The quoted phrase test excludes documents without that phrase. However, that result would not establish the required outcome: the same labeled nearest-neighbor fixtures rank consistently.

C:

Questions correctly distinguish indexed approximate search from exact KNN behavior. However, that result would not establish the required outcome: the same labeled nearest-neighbor fixtures rank consistently.

Question 717

A DAB service is deployed to Azure Container Apps without private access to its Azure SQL backend. Which technical reason BEST explains the proposed correction?

  1. Publishing the API does not grant the host network or SQL access automatically.
  2. Autoscaling the API does not eliminate downstream database exhaustion or unauthorized access.
  3. REST endpoint mapping supplies predictable URLs and HTTP operations.
  4. Configuration must resolve the correct database per environment without leaking secrets.

Explanation

Correct Answer

A:

Publishing the API does not grant the host network or SQL access automatically. This explains why the corrective action is to configure supported network access, managed identity and database permissions. Confirm the diagnosis when container-based smoke tests connect securely to the intended Azure SQL database.

Incorrect Answers

B:

Autoscaling the API does not eliminate downstream database exhaustion or unauthorized access. The actual technical constraint is that publishing the API does not grant the host network or SQL access automatically.

C:

REST endpoint mapping supplies predictable URLs and HTTP operations. The actual technical constraint is that publishing the API does not grant the host network or SQL access automatically.

D:

Configuration must resolve the correct database per environment without leaking secrets. The actual technical constraint is that publishing the API does not grant the host network or SQL access automatically.

Question 718

An employee assistant must answer questions from policies that change weekly without retraining a base model. What maintenance or governance control BEST reduces recurrence of this problem?

  1. Keep business arithmetic outside uncontrolled model generation.
  2. Test corpus freshness and unauthorized access.
  3. Exclude sensitive data and validate schema before external transmission.
  4. Cap retries and alert on repeated model service failures.

Explanation

Correct Answer

B:

Test corpus freshness and unauthorized access. This guards against recurrence because RAG can inject up-to-date source evidence at answer time rather than encoding it in weights. The direct remediation is: Use retrieval-augmented generation over current authorized policy documents.

Incorrect Answers

A:

Keep business arithmetic outside uncontrolled model generation. The current task instead requires this action: test corpus freshness and unauthorized access.

C:

Exclude sensitive data and validate schema before external transmission. The current task instead requires this action: test corpus freshness and unauthorized access.

D:

Cap retries and alert on repeated model service failures. The current task instead requires this action: test corpus freshness and unauthorized access.

Question 719

A contract is embedded as one 400-page block and retrieval often returns irrelevant sections. Which implementation decision BEST addresses this requirement?

  1. Use heading-aware and language-aware chunk boundaries with source metadata.
  2. Include semantically meaningful title and content fields, with identifiers retained as metadata.
  3. Split content by semantically meaningful sections within model token limits.
  4. Select language-appropriate content and model capabilities while retaining locale as metadata.

Explanation

Correct Answer

C:

Split content by semantically meaningful sections within model token limits. This targets the primary issue because oversized chunks mix unrelated topics and degrade passage-level relevance. Verification: Clause queries retrieve the specific correct paragraph rather than the entire contract. Prevent recurrence: Keep section IDs and overlap rules versioned.

Incorrect Answers

A:

Using heading-aware and language-aware chunk boundaries with source metadata would not correct a contract is embedded as one 400-page block and retrieval often returns irrelevant sections. Split content by semantically meaningful sections within model token limits.

B:

IDs alone lack the natural-language signal needed for semantic retrieval.

D:

Embedding the wrong language source can degrade multilingual retrieval quality.

Question 720

A team stores model API keys directly in a version-controlled SQL Database Project. Which measurable outcome BEST verifies that the requirement is satisfied?

  1. Model calls and retrieval calls each succeed only with required scopes.
  2. The verified pipeline deploys to staging but fails from an untrusted branch.
  3. A test UPDATE appears in protected audit storage with correct identity.
  4. Secret scans pass and deployments resolve approved credentials securely.

Explanation

Correct Answer

D:

Secret scans pass and deployments resolve approved credentials securely. This is the decisive observable evidence after implementing remove keys and use deployment-time secret references or supported managed identity. It tests the underlying requirement: Repository history retains secrets even after a visible commit is removed.

Incorrect Answers

A:

Model calls and retrieval calls each succeed only with required scopes. However, that result would not establish the required outcome: secret scans pass and deployments resolve approved credentials securely.

B:

The verified pipeline deploys to staging but fails from an untrusted branch. However, that result would not establish the required outcome: secret scans pass and deployments resolve approved credentials securely.

C:

A test UPDATE appears in protected audit storage with correct identity. However, that result would not establish the required outcome: secret scans pass and deployments resolve approved credentials securely.

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