Microsoft DP-900 Practice Test 16
Question 150
An employee changes department and a report must show their old allocation accurately. Which choice BEST meets this requirement?
- Design intentional historical snapshots or slowly changing dimensions for analytics.
- Evaluate latency concurrency query style and update volume before choosing a store.
- Extract and load source records then perform controlled transformations within target compute.
- Apply the newest customer address to every historic transaction and call it history-preserving.
Explanation
Correct Answer
A:Analytical history and mutable operational attributes have different retention semantics.
Incorrect Answers
B:Evaluate latency concurrency query style and update volume before choosing a store. That action primarily handles Data platform read and write profiles; the given requirement is Historical dimensions versus current operational truth. Analytical history and mutable operational attributes have different retention semantics.
C:Extract and load source records then perform controlled transformations within target compute. That action primarily handles ELT load then transform in engine; the given requirement is Historical dimensions versus current operational truth. Analytical history and mutable operational attributes have different retention semantics.
D:Apply the newest customer address to every historic transaction and call it history-preserving. It does not implement Historical dimensions versus current operational truth. Replacing old dimension values can rewrite historical business interpretation.
Question 151
A report must filter transaction amounts by customer region and month. If the relevant Star schema report model safeguard is skipped, which failure is MOST directly associated with that omission?
- Using a warehouse as a replacement for every OLTP payment transaction may be inappropriate.
- Joining dimensions directly to many unrelated fact tables can cause ambiguous paths.
- Hard-coded manually copied totals become stale when filters change.
- Expecting a notebook file alone to execute a cluster without compute is incorrect.
Explanation
Correct Answer
B:Joining dimensions directly to many unrelated fact tables can cause ambiguous paths. Star schemas simplify controlled dimension filtering for analytics. A suitable check is to inspect model relationships and test known totals.
Incorrect Answers
A:Using a warehouse as a replacement for every OLTP payment transaction may be inappropriate. This failure is associated with Fabric Warehouse relational reporting; it is not the primary failure in the stated Star schema report model scenario. Star schemas simplify controlled dimension filtering for analytics.
C:Hard-coded manually copied totals become stale when filters change. This failure is associated with Power BI semantic model measures; it is not the primary failure in the stated Star schema report model scenario. Star schemas simplify controlled dimension filtering for analytics.
D:Expecting a notebook file alone to execute a cluster without compute is incorrect. This failure is associated with Azure Databricks Apache Spark processing; it is not the primary failure in the stated Star schema report model scenario. Star schemas simplify controlled dimension filtering for analytics.
Question 152
A report uses a map with ambiguous city names shared by several countries. What is the MOST appropriate next validation or correction?
- Check that segments are mutually exclusive and total appropriately.
- Check selected locations against known coordinates and category labels.
- Assume every text field named Location always geocodes accurately.
- Reconstruct sample fact lineage across each pipeline step.
Explanation
Correct Answer
B:Check selected locations against known coordinates and category labels. This tests the intended result: locations resolve to intended regions and values match actual places.
Incorrect Answers
A:Check that segments are mutually exclusive and total appropriately. It tests Pie donut chart proportion caution rather than the critical Map visual geographic measures behavior. Geographic visuals require accurate location encoding and appropriate map granularity.
C:Assume every text field named Location always geocodes accurately. This unsafe shortcut leaves the Map visual geographic measures problem unresolved. Missing country context can place a city on the wrong continent.
D:Reconstruct sample fact lineage across each pipeline step. It tests Data governance and lineage for analytics rather than the critical Map visual geographic measures behavior. Geographic visuals require accurate location encoding and appropriate map granularity.
Question 153
A finance number changes but nobody knows which source transformed it. After selecting the proper Data lineage from source to report approach, which observation gives the clearest evidence of success?
- The model supports intended joins or nested attribute reads without excessive conversions.
- Reviewers trace a reported measure back to approved inputs and rules.
- Sample accents and amounts survive round-trip without becoming different values.
- Recommendations respect costs limits and forecast assumptions.
Explanation
Correct Answer
B:Reviewers trace a reported measure back to approved inputs and rules. This is the directly observable result for Data lineage from source to report; lineage makes analytical values accountable across processing stages.
Incorrect Answers
A:The model supports intended joins or nested attribute reads without excessive conversions. This supports Relational rows versus document attributes, not confirmation of Data lineage from source to report. The relevant evidence is that reviewers trace a reported measure back to approved inputs and rules.
C:Sample accents and amounts survive round-trip without becoming different values. This supports Text encoding and numeric parsing, not confirmation of Data lineage from source to report. The relevant evidence is that reviewers trace a reported measure back to approved inputs and rules.
D:Recommendations respect costs limits and forecast assumptions. This supports Prescriptive decision optimization, not confirmation of Data lineage from source to report. The relevant evidence is that reviewers trace a reported measure back to approved inputs and rules.
Question 154
A user assumes its query language means Azure SQL server tables and joins. During testing, the team observes this specific failure: Expecting cross-table foreign keys in the NoSQL API can break designs. Which corrective check should be performed FIRST?
- Assume every service supporting SELECT enforces SQL Server foreign keys.
- Test nested JSON access versus relational join constraints.
- Enable suitable blob soft delete retention and test restore.
- Measure and budget provisioned or consumed request units for actual Cosmos operations.
Explanation
Correct Answer
B:Test nested JSON access versus relational join constraints. A successful correction should demonstrate that document queries behave under partition and JSON item semantics.
Incorrect Answers
A:Assume every service supporting SELECT enforces SQL Server foreign keys. This reinforces the error rather than addressing the Cosmos DB API for NoSQL versus SQL text failure. Similar keyword syntax does not make NoSQL document storage a SQL Server database.
C:Enable suitable blob soft delete retention and test restore. It serves Blob soft delete recovery, not the diagnosed Cosmos DB API for NoSQL versus SQL text problem. Expecting cross-table foreign keys in the NoSQL API can break designs.
D:Measure and budget provisioned or consumed request units for actual Cosmos operations. It serves Cosmos DB Request Unit RU accounting, not the diagnosed Cosmos DB API for NoSQL versus SQL text problem. Expecting cross-table foreign keys in the NoSQL API can break designs.
Question 155
Global website events arrive with local wall-clock timestamps from many regions. After selecting the proper Timestamp and timezone representation approach, which observation gives the clearest evidence of success?
- Tests confirm analysts can SELECT approved data but cannot DROP tables.
- Confirmed metrics distinguish real business change from ingest artifacts.
- Ordered events represent the correct absolute instant across regions.
- Nested lookups return the right address and amount without misreading sibling values.
Explanation
Correct Answer
C:Ordered events represent the correct absolute instant across regions. This is the directly observable result for Timestamp and timezone representation; timestamp interpretation needs explicit timezone conventions for cross-region analysis.
Incorrect Answers
A:Tests confirm analysts can SELECT approved data but cannot DROP tables. This supports DBA least privilege account roles, not confirmation of Timestamp and timezone representation. The relevant evidence is that ordered events represent the correct absolute instant across regions.
B:Confirmed metrics distinguish real business change from ingest artifacts. This supports Data analyst anomaly interpretation, not confirmation of Timestamp and timezone representation. The relevant evidence is that ordered events represent the correct absolute instant across regions.
D:Nested lookups return the right address and amount without misreading sibling values. This supports JSON arrays and nested objects, not confirmation of Timestamp and timezone representation. The relevant evidence is that ordered events represent the correct absolute instant across regions.
Question 156
Analysts want to preserve raw extracts for new future transformations. Select THREE statements that correctly describe the ELT load then transform in engine implementation and how it can be verified.
- Extract and load source records then perform controlled transformations within target compute.
- Apply supported change data capture to transmit tracked source modifications.
- Delete raw source files before checking all curated transformation outputs.
- Parse CSV with configured delimiter quote escaping and column type rules.
- Curated tables derive reproducibly from retained raw landing data.
- ELT shifts transformation to the destination processing platform.
Explanation
Correct Answers
A:Extract and load source records then perform controlled transformations within target compute. This is an appropriate action for the ELT load then transform in engine case. ELT shifts transformation to the destination processing platform.
E:Curated tables derive reproducibly from retained raw landing data. This is directly observable success evidence for ELT load then transform in engine.
F:ELT shifts transformation to the destination processing platform. This supports why the action and evidence together address ELT load then transform in engine.
Incorrect Answers
B:Apply supported change data capture to transmit tracked source modifications. This implements CDC incremental change propagation instead of the required ELT load then transform in engine function. ELT shifts transformation to the destination processing platform.
C:Delete raw source files before checking all curated transformation outputs. This is not a safe or appropriate ELT load then transform in engine implementation. Running transformations only in a dashboard can leave incompatible raw measurements.
D:Parse CSV with configured delimiter quote escaping and column type rules. This implements CSV delimited text schema instead of the required ELT load then transform in engine function. ELT shifts transformation to the destination processing platform.
Question 157
An existing PostgreSQL application uses PostgreSQL-native SQL and extensions. After selecting the proper Azure Database for PostgreSQL managed service approach, which observation gives the clearest evidence of success?
- Rewritten or indexed queries improve measured response safely.
- Analytics engines list intended folders and permissions govern directory access.
- Observed quality metrics identify incomplete or duplicate data before publishing.
- The PostgreSQL client and application use supported PostgreSQL syntax and configured features.
Explanation
Correct Answer
D:The PostgreSQL client and application use supported PostgreSQL syntax and configured features. This is the directly observable result for Azure Database for PostgreSQL managed service; postgreSQL-managed Azure service preserves PostgreSQL engine semantics rather than T-SQL engine identity.
Incorrect Answers
A:Rewritten or indexed queries improve measured response safely. This supports DBA performance tuning and indexing, not confirmation of Azure Database for PostgreSQL managed service. The relevant evidence is that the PostgreSQL client and application use supported PostgreSQL syntax and configured features.
B:Analytics engines list intended folders and permissions govern directory access. This supports Azure Data Lake Storage Gen2 analytics landing, not confirmation of Azure Database for PostgreSQL managed service. The relevant evidence is that the PostgreSQL client and application use supported PostgreSQL syntax and configured features.
C:Observed quality metrics identify incomplete or duplicate data before publishing. This supports Data quality completeness and accuracy, not confirmation of Azure Database for PostgreSQL managed service. The relevant evidence is that the PostgreSQL client and application use supported PostgreSQL syntax and configured features.
Question 158
Slow SQL queries cause a billing application to time out. After selecting the proper DBA performance tuning and indexing approach, which observation gives the clearest evidence of success?
- Ordered events represent the correct absolute instant across regions.
- Database is recoverable pipelines are reliable and reports use curated facts.
- Rewritten or indexed queries improve measured response safely.
- Recommendations respect costs limits and forecast assumptions.
Explanation
Correct Answer
C:Rewritten or indexed queries improve measured response safely. This is the directly observable result for DBA performance tuning and indexing; dBAs tune database performance as well as maintaining access and reliability.
Incorrect Answers
A:Ordered events represent the correct absolute instant across regions. This supports Timestamp and timezone representation, not confirmation of DBA performance tuning and indexing. The relevant evidence is that rewritten or indexed queries improve measured response safely.
B:Database is recoverable pipelines are reliable and reports use curated facts. This supports Separation of data roles in a migration, not confirmation of DBA performance tuning and indexing. The relevant evidence is that rewritten or indexed queries improve measured response safely.
D:Recommendations respect costs limits and forecast assumptions. This supports Prescriptive decision optimization, not confirmation of DBA performance tuning and indexing. The relevant evidence is that rewritten or indexed queries improve measured response safely.
Question 159
A team must aggregate HTTP status categories from heterogeneous log messages. Which principle BEST explains the correct Log text extraction versus structured event fields design decision?
- ADLS Gen2 adds data-lake semantics on Azure Blob storage.
- Unstructured logs can become analyzable after parsing but remain unstructured at source.
- Object metadata supports structured discovery of unstructured content.
- Cosmos DB supports distributed data models but partition and consistency choices matter.
Explanation
Correct Answer
B:Unstructured logs can become analyzable after parsing but remain unstructured at source. In this case the applicable decision is to extract defined fields into structured events before reliable aggregation.
Incorrect Answers
A:ADLS Gen2 adds data-lake semantics on Azure Blob storage. That concept explains Azure Data Lake Storage Gen2 analytics landing; the situation instead concerns Log text extraction versus structured event fields. Simple substring matching may confuse status text with message content.
C:Object metadata supports structured discovery of unstructured content. That concept explains Binary object metadata separation; the situation instead concerns Log text extraction versus structured event fields. Simple substring matching may confuse status text with message content.
D:Cosmos DB supports distributed data models but partition and consistency choices matter. That concept explains Cosmos DB for distributed JSON apps; the situation instead concerns Log text extraction versus structured event fields. Simple substring matching may confuse status text with message content.
Question 160
A user assumes its query language means Azure SQL server tables and joins. Which principle BEST explains the correct Cosmos DB API for NoSQL versus SQL text design decision?
- Table Storage identifies entities by partition and row keys.
- Indexed document properties support queries while policy affects write/read cost.
- Similar keyword syntax does not make NoSQL document storage a SQL Server database.
- Blob Storage provides object-based access with containers and blobs.
Explanation
Correct Answer
C:Similar keyword syntax does not make NoSQL document storage a SQL Server database. In this case the applicable decision is to distinguish Cosmos DB for NoSQL query language from relational T-SQL.
Incorrect Answers
A:Table Storage identifies entities by partition and row keys. That concept explains Table Storage PartitionKey and RowKey; the situation instead concerns Cosmos DB API for NoSQL versus SQL text. Expecting cross-table foreign keys in the NoSQL API can break designs.
B:Indexed document properties support queries while policy affects write/read cost. That concept explains Cosmos DB NoSQL indexing and queries; the situation instead concerns Cosmos DB API for NoSQL versus SQL text. Expecting cross-table foreign keys in the NoSQL API can break designs.
D:Blob Storage provides object-based access with containers and blobs. That concept explains Blob storage object and container hierarchy; the situation instead concerns Cosmos DB API for NoSQL versus SQL text. Expecting cross-table foreign keys in the NoSQL API can break designs.
