Microsoft DP-800 Practice Test 58
Question 570
Briarfield Foods is responsible for an Azure SQL or Microsoft SQL workload. A B2B app exposes SQL data to signed-in users. Operational evidence: Anonymous API access bypasses identity checks. What operational condition would provide the earliest useful regression warning?
- Monitor token validation failures and auth configuration
- Monitor GraphQL query cost and relationship failures
- Monitor procedure errors and API write attempts
- Monitor config validation and service startup errors
Explanation
Correct Answer
A:
Monitor bearer-token validation failures and changes to issuer, audience, signature-key, and expiry checks for the SQL-facing API. Approved users should obtain authorized results, while invalid or unauthenticated tokens fail closed; a successful HTTP response without identity validation would recreate the reported exposure.
Incorrect Answers
B:
“Monitor GraphQL query cost and relationship failures” concerns GraphQL schema lacks relationship mapping despite SQL foreign key; the reported problem instead is Anonymous API access bypasses identity checks. Monitor bearer-token validation failures and changes to issuer, audience, signature-key, and expiry checks for the SQL-facing API. Approved users should obtain authorized results, while invalid or unauthenticated tokens fail closed; a successful HTTP response without identity validation would recreate the reported exposure.
C:
“Monitor procedure errors and API write attempts” concerns Direct table insert bypasses business constraints; the reported problem instead is Anonymous API access bypasses identity checks. Monitor bearer-token validation failures and changes to issuer, audience, signature-key, and expiry checks for the SQL-facing API. Approved users should obtain authorized results, while invalid or unauthenticated tokens fail closed; a successful HTTP response without identity validation would recreate the reported exposure.
D:
“Monitor config validation and service startup errors” concerns No configuration maps entities to SQL objects or runtime connections; the reported problem instead is Anonymous API access bypasses identity checks. Monitor bearer-token validation failures and changes to issuer, audience, signature-key, and expiry checks for the SQL-facing API. Approved users should obtain authorized results, while invalid or unauthenticated tokens fail closed; a successful HTTP response without identity validation would recreate the reported exposure.
Question 571
At Northstar Analytics, an Azure SQL service intermittently times out. The observed constraint is: Application logs alone cannot distinguish database waits from network errors. What initial check best narrows the technical cause without altering business data?
- Inspect authentication provider and role mapping
- Inspect Application Insights dependency instrumentation
- Inspect DAB relationships and entity naming
- Inspect Azure Monitor metrics and diagnostic settings
Explanation
Correct Answer
D:
Application logs alone cannot distinguish database waits from network errors. Inspect Azure Monitor metrics and diagnostic settings.
Incorrect Answers
A:
“Inspect authentication provider and role mapping” concerns Anonymous API access bypasses identity checks; the reported problem instead is Application logs alone cannot distinguish database waits from network errors. Inspect Azure Monitor metrics and diagnostic settings.
B:
“Inspect Application Insights dependency instrumentation” concerns HTTP response latency is available but SQL dependency duration is missing; the reported problem instead is Application logs alone cannot distinguish database waits from network errors. Inspect Azure Monitor metrics and diagnostic settings.
C:
“Inspect DAB relationships and entity naming” concerns GraphQL schema lacks relationship mapping despite SQL foreign key; the reported problem instead is Application logs alone cannot distinguish database waits from network errors. Inspect Azure Monitor metrics and diagnostic settings.
Question 572
A database engineer at Maple Harbor Research is investigating this case: An Azure SQL service intermittently times out. The relevant finding is: Application logs alone cannot distinguish database waits from network errors. Which proposed change is the most suitable response to the observed constraint?
- Expose only approved fields and restrict role privileges
- Route relevant SQL metrics/logs into approved Log Analytics workspace
- Create Data API builder config with approved SQL source/entity mapping
- Use an approved SQL change feed connected to Logic Apps
Explanation
Correct Answer
B:
Application logs alone cannot distinguish database waits from network errors. Route relevant SQL metrics/logs into approved Log Analytics workspace.
Incorrect Answers
A:
“Expose only approved fields and restrict role privileges” concerns Role mapping permits clients to select every column; the reported problem instead is Application logs alone cannot distinguish database waits from network errors. Route relevant SQL metrics/logs into approved Log Analytics workspace.
C:
“Create Data API builder config with approved SQL source/entity mapping” concerns No configuration maps entities to SQL objects or runtime connections; the reported problem instead is Application logs alone cannot distinguish database waits from network errors. Route relevant SQL metrics/logs into approved Log Analytics workspace.
D:
“Use an approved SQL change feed connected to Logic Apps” concerns One-off SQL triggers cannot safely call external systems; the reported problem instead is Application logs alone cannot distinguish database waits from network errors. Route relevant SQL metrics/logs into approved Log Analytics workspace.
Question 573
Juniper Commerce is responsible for an Azure SQL or Microsoft SQL workload. An Azure SQL service intermittently times out. Operational evidence: Application logs alone cannot distinguish database waits from network errors. What isolated pilot would provide the strongest evidence before rollout?
- Generate synthetic slow query and inspect correlated telemetry
- Query synthetic order with multiple line items through GraphQL
- Run mock API request invoking deliberate slow SQL
- Request successive pages from a synthetic large dataset
Explanation
Correct Answer
A:
Application logs alone cannot distinguish database waits from network errors. Generate synthetic slow query and inspect correlated telemetry.
Incorrect Answers
B:
“Query synthetic order with multiple line items through GraphQL” concerns GraphQL schema lacks relationship mapping despite SQL foreign key; the reported problem instead is Application logs alone cannot distinguish database waits from network errors. Generate synthetic slow query and inspect correlated telemetry.
C:
“Run mock API request invoking deliberate slow SQL” concerns HTTP response latency is available but SQL dependency duration is missing; the reported problem instead is Application logs alone cannot distinguish database waits from network errors. Generate synthetic slow query and inspect correlated telemetry.
D:
“Request successive pages from a synthetic large dataset” concerns Unbounded pages risk resource exhaustion; the reported problem instead is Application logs alone cannot distinguish database waits from network errors. Generate synthetic slow query and inspect correlated telemetry.
Question 574
At Summit Logistics, an Azure SQL service intermittently times out. The observed constraint is: Application logs alone cannot distinguish database waits from network errors. Which acceptance result most directly demonstrates the requirement has been satisfied?
- Client receives changed keys and refreshes correct rows
- Workflow receives correct state without blocking OLTP transaction
- Alerts capture query slowdown and source context
- One intended event is processed with idempotency controls
Explanation
Correct Answer
C:
Application logs alone cannot distinguish database waits from network errors. Alerts capture query slowdown and source context.
Incorrect Answers
A:
“Client receives changed keys and refreshes correct rows” concerns Full row-history logging is unnecessary; the reported problem instead is Application logs alone cannot distinguish database waits from network errors. Alerts capture query slowdown and source context.
B:
“Workflow receives correct state without blocking OLTP transaction” concerns One-off SQL triggers cannot safely call external systems; the reported problem instead is Application logs alone cannot distinguish database waits from network errors. Alerts capture query slowdown and source context.
D:
“One intended event is processed with idempotency controls” concerns Polling entire table every minute is wasteful; the reported problem instead is Application logs alone cannot distinguish database waits from network errors. Alerts capture query slowdown and source context.
Question 575
A database engineer at BrightPath Studios is investigating this case: An Azure SQL service intermittently times out. The relevant finding is: Application logs alone cannot distinguish database waits from network errors. What operational condition would provide the earliest useful regression warning?
- Monitor retries, dead-letter states and authorization
- Monitor large page requests and API latency
- Monitor latency, failed log ingestion and alert noise
- Monitor trigger lag and duplicate processing
Explanation
Correct Answer
C:
Application logs alone cannot distinguish database waits from network errors. Monitor latency, failed log ingestion and alert noise.
Incorrect Answers
A:
“Monitor retries, dead-letter states and authorization” concerns One-off SQL triggers cannot safely call external systems; the reported problem instead is Application logs alone cannot distinguish database waits from network errors. Monitor latency, failed log ingestion and alert noise.
B:
“Monitor large page requests and API latency” concerns Unbounded pages risk resource exhaustion; the reported problem instead is Application logs alone cannot distinguish database waits from network errors. Monitor latency, failed log ingestion and alert noise.
D:
“Monitor trigger lag and duplicate processing” concerns Polling entire table every minute is wasteful; the reported problem instead is Application logs alone cannot distinguish database waits from network errors. Monitor latency, failed log ingestion and alert noise.
Question 576
Lakeview Services is responsible for an Azure SQL or Microsoft SQL workload. An app team cannot tell which API call causes slow SQL. Operational evidence: HTTP response latency is available but SQL dependency duration is missing. What initial check best narrows the technical cause without altering business data?
- Inspect Application Insights dependency instrumentation
- Inspect supported SQL trigger binding and underlying change tracking
- Inspect DAB relationships and entity naming
- Inspect whether client needs keys versus complete before/after images
Explanation
Correct Answer
A:
HTTP response latency is available but SQL dependency duration is missing. Inspect Application Insights dependency instrumentation.
Incorrect Answers
B:
“Inspect supported SQL trigger binding and underlying change tracking” concerns Polling entire table every minute is wasteful; the reported problem instead is HTTP response latency is available but SQL dependency duration is missing. Inspect Application Insights dependency instrumentation.
C:
“Inspect DAB relationships and entity naming” concerns GraphQL schema lacks relationship mapping despite SQL foreign key; the reported problem instead is HTTP response latency is available but SQL dependency duration is missing. Inspect Application Insights dependency instrumentation.
D:
“Inspect whether client needs keys versus complete before/after images” concerns Full row-history logging is unnecessary; the reported problem instead is HTTP response latency is available but SQL dependency duration is missing. Inspect Application Insights dependency instrumentation.
Question 577
At Crownridge Travel, an app team cannot tell which API call causes slow SQL. The observed constraint is: HTTP response latency is available but SQL dependency duration is missing. Which proposed change is the most suitable response to the observed constraint?
- Use an approved SQL change feed connected to Logic Apps
- Configure supported Azure Functions SQL trigger binding with least privilege
- Configure supported one-to-many GraphQL relationship between entities
- Instrument supported SQL dependencies and correlation identifiers
Explanation
Correct Answer
D:
HTTP response latency is available but SQL dependency duration is missing. Instrument supported SQL dependencies and correlation identifiers.
Incorrect Answers
A:
“Use an approved SQL change feed connected to Logic Apps” concerns One-off SQL triggers cannot safely call external systems; the reported problem instead is HTTP response latency is available but SQL dependency duration is missing. Instrument supported SQL dependencies and correlation identifiers.
B:
“Configure supported Azure Functions SQL trigger binding with least privilege” concerns Polling entire table every minute is wasteful; the reported problem instead is HTTP response latency is available but SQL dependency duration is missing. Instrument supported SQL dependencies and correlation identifiers.
C:
“Configure supported one-to-many GraphQL relationship between entities” concerns GraphQL schema lacks relationship mapping despite SQL foreign key; the reported problem instead is HTTP response latency is available but SQL dependency duration is missing. Instrument supported SQL dependencies and correlation identifiers.
Question 578
A database engineer at Oakridge Advisors is investigating this case: An app team cannot tell which API call causes slow SQL. The relevant finding is: HTTP response latency is available but SQL dependency duration is missing. What isolated pilot would provide the strongest evidence before rollout?
- Modify a test catalog and request changes since known version
- Try valid, expired and missing tokens against mock endpoint
- Run mock API request invoking deliberate slow SQL
- Generate synthetic slow query and inspect correlated telemetry
Explanation
Correct Answer
C:
HTTP response latency is available but SQL dependency duration is missing. Run mock API request invoking deliberate slow SQL.
Incorrect Answers
A:
“Modify a test catalog and request changes since known version” concerns Full row-history logging is unnecessary; the reported problem instead is HTTP response latency is available but SQL dependency duration is missing. Run mock API request invoking deliberate slow SQL.
B:
“Try valid, expired and missing tokens against mock endpoint” concerns Anonymous API access bypasses identity checks; the reported problem instead is HTTP response latency is available but SQL dependency duration is missing. Run mock API request invoking deliberate slow SQL.
D:
“Generate synthetic slow query and inspect correlated telemetry” concerns Application logs alone cannot distinguish database waits from network errors; the reported problem instead is HTTP response latency is available but SQL dependency duration is missing. Run mock API request invoking deliberate slow SQL.
Question 579
Horizon Communications is responsible for an Azure SQL or Microsoft SQL workload. An app team cannot tell which API call causes slow SQL. Operational evidence: HTTP response latency is available but SQL dependency duration is missing. Which acceptance result most directly demonstrates the requirement has been satisfied?
- Client receives changed keys and refreshes correct rows
- Workflow receives correct state without blocking OLTP transaction
- Trace spans connect HTTP call with SQL dependency duration
- One intended event is processed with idempotency controls
Explanation
Correct Answer
C:
HTTP response latency is available but SQL dependency duration is missing. Trace spans connect HTTP call with SQL dependency duration.
Incorrect Answers
A:
“Client receives changed keys and refreshes correct rows” concerns Full row-history logging is unnecessary; the reported problem instead is HTTP response latency is available but SQL dependency duration is missing. Trace spans connect HTTP call with SQL dependency duration.
B:
“Workflow receives correct state without blocking OLTP transaction” concerns One-off SQL triggers cannot safely call external systems; the reported problem instead is HTTP response latency is available but SQL dependency duration is missing. Trace spans connect HTTP call with SQL dependency duration.
D:
“One intended event is processed with idempotency controls” concerns Polling entire table every minute is wasteful; the reported problem instead is HTTP response latency is available but SQL dependency duration is missing. Trace spans connect HTTP call with SQL dependency duration.
Question 580
At Brookfield Labs, an app team cannot tell which API call causes slow SQL. The observed constraint is: HTTP response latency is available but SQL dependency duration is missing. What operational condition would provide the earliest useful regression warning?
- Monitor trigger lag and duplicate processing
- Monitor procedure errors and API write attempts
- Monitor missing correlation and sampling errors
- Monitor config validation and service startup errors
Explanation
Correct Answer
C:
HTTP response latency is available but SQL dependency duration is missing. Monitor missing correlation and sampling errors.
Incorrect Answers
A:
“Monitor trigger lag and duplicate processing” concerns Polling entire table every minute is wasteful; the reported problem instead is HTTP response latency is available but SQL dependency duration is missing. Monitor missing correlation and sampling errors.
B:
“Monitor procedure errors and API write attempts” concerns Direct table insert bypasses business constraints; the reported problem instead is HTTP response latency is available but SQL dependency duration is missing. Monitor missing correlation and sampling errors.
D:
“Monitor config validation and service startup errors” concerns No configuration maps entities to SQL objects or runtime connections; the reported problem instead is HTTP response latency is available but SQL dependency duration is missing. Monitor missing correlation and sampling errors.