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Microsoft DP-700 Practice Test 17

Question 160

A dashboard wants a 15-minute rolling count of incoming events. Which transformation should be prepared?

  1. Use an explicit supported sliding time-window aggregation with a five-minute hop if overlapping updates are needed.
  2. Capture raw payload plus schema/version metadata and validate into a curated stream.
  3. Use a durable checkpoint with a compatible transactional sink or idempotent write logic.
  4. Use a continuously running supported stream-to-Delta ingestion flow with durable progress state.

Explanation

Correct Answer

A:

Sliding windows evaluate overlapping event-time intervals and differ from disjoint tumbling windows. Compare the expected number of windows for one test event.

Incorrect Answers

B:

Raw preservation avoids silent data loss when evolving fields do not fit a single rigid schema.

C:

Recovery may replay input; correctness depends on durable offsets, compatible state, and safe target commits.

D:

Streaming sinks can commit ongoing events to Delta while checkpoints support recovery.

Question 161

A Spark team needs open-format Delta tables for machine learning, file ingestion, and notebook transformations. Which Fabric storage item fits?

  1. Land raw JSON in a lakehouse and transform curated outputs for warehouse consumption.
  2. Create a lakehouse with Delta tables in OneLake.
  3. Use Eventhouse and real-time ingestion for telemetry analytics.
  4. Create a Fabric data warehouse for SQL-first curated analytics.

Explanation

Correct Answer

B:

Lakehouse supports file and table data with Spark-centric ingestion and open storage patterns. Run a notebook read and write against a sample Delta table.

Incorrect Answers

A:

Separating flexible raw landing from relational curation avoids forcing semi-structured files into a fixed star schema immediately.

C:

A real-time analytics engine better suits time-series arrival and near-real-time exploration than batch-only warehouse refresh.

D:

A warehouse exposes relational schema and SQL processing for governed analytical tables and transformations.

Question 162

Business analysts primarily use relational SQL schemas and T-SQL transformations over curated data. Which item aligns with that workload?

  1. Ingest into a KQL database within an Eventhouse.
  2. Create a Fabric data warehouse for SQL-first curated analytics.
  3. Use Eventhouse and real-time ingestion for telemetry analytics.
  4. Create a lakehouse with Delta tables in OneLake.

Explanation

Correct Answer

B:

A warehouse exposes relational schema and SQL processing for governed analytical tables and transformations. Run the expected T-SQL statements on a test model.

Incorrect Answers

A:

Eventhouse targets event and time-series workloads and KQL queries instead of a generic transactional table store.

C:

A real-time analytics engine better suits time-series arrival and near-real-time exploration than batch-only warehouse refresh.

D:

Lakehouse supports file and table data with Spark-centric ingestion and open storage patterns.

Question 163

A security team queries high-volume time-stamped logs with KQL and requires fast exploratory analytics. Which data store best fits?

  1. Ingest into a KQL database within an Eventhouse.
  2. Query through the lakehouse SQL analytics endpoint for supported read operations.
  3. Create a Fabric data warehouse for SQL-first curated analytics.
  4. Use a Fabric warehouse with supported T-SQL DML for the modeled tables.

Explanation

Correct Answer

A:

Eventhouse targets event and time-series workloads and KQL queries instead of a generic transactional table store. Test a time-filtered KQL query on representative events.

Incorrect Answers

B:

The lakehouse SQL analytics endpoint exposes relational reads over Delta tables; writes should use a supported ingestion or Spark path.

C:

A warehouse exposes relational schema and SQL processing for governed analytical tables and transformations.

D:

Warehouse SQL supports curated relational transformations rather than relying on the lakehouse read-only SQL endpoint.

Question 164

A data scientist must retain arbitrary binary files next to structured Delta tables. Where should files be placed in the lakehouse?

  1. Create a Fabric data warehouse for SQL-first curated analytics.
  2. Land raw JSON in a lakehouse and transform curated outputs for warehouse consumption.
  3. Use Eventhouse and real-time ingestion for telemetry analytics.
  4. Store source files under the lakehouse Files area and curated Delta tables under Tables.

Explanation

Correct Answer

D:

Files holds varied formats while Tables supports managed Delta table discovery and querying. Verify the expected file and table paths separately.

Incorrect Answers

A:

A warehouse exposes relational schema and SQL processing for governed analytical tables and transformations.

B:

Separating flexible raw landing from relational curation avoids forcing semi-structured files into a fixed star schema immediately.

C:

A real-time analytics engine better suits time-series arrival and near-real-time exploration than batch-only warehouse refresh.

Question 165

An existing warehouse serves dimensional reporting, while raw clickstream JSON needs early landing. Which landing design supports both?

  1. Ingest into a KQL database within an Eventhouse.
  2. Land raw JSON in a lakehouse and transform curated outputs for warehouse consumption.
  3. Create a lakehouse with Delta tables in OneLake.
  4. Query through the lakehouse SQL analytics endpoint for supported read operations.

Explanation

Correct Answer

B:

Separating flexible raw landing from relational curation avoids forcing semi-structured files into a fixed star schema immediately. Confirm warehouse results trace back to preserved raw files.

Incorrect Answers

A:

Eventhouse targets event and time-series workloads and KQL queries instead of a generic transactional table store.

C:

Lakehouse supports file and table data with Spark-centric ingestion and open storage patterns.

D:

The lakehouse SQL analytics endpoint exposes relational reads over Delta tables; writes should use a supported ingestion or Spark path.

Question 166

Operations must analyze fast-changing telemetry without constant copying into nightly warehouse batches. Which Fabric component should be evaluated?

  1. Create a lakehouse with Delta tables in OneLake.
  2. Use Eventhouse and real-time ingestion for telemetry analytics.
  3. Use a Fabric warehouse with supported T-SQL DML for the modeled tables.
  4. Store source files under the lakehouse Files area and curated Delta tables under Tables.

Explanation

Correct Answer

B:

A real-time analytics engine better suits time-series arrival and near-real-time exploration than batch-only warehouse refresh. Measure end-to-end event visibility latency.

Incorrect Answers

A:

Lakehouse supports file and table data with Spark-centric ingestion and open storage patterns.

C:

Warehouse SQL supports curated relational transformations rather than relying on the lakehouse read-only SQL endpoint.

D:

Files holds varied formats while Tables supports managed Delta table discovery and querying.

Question 167

A project needs a SQL analytics interface over existing lakehouse Delta tables without authoring writes through it. Which surface is applicable?

  1. Store source files under the lakehouse Files area and curated Delta tables under Tables.
  2. Use Eventhouse and real-time ingestion for telemetry analytics.
  3. Ingest into a KQL database within an Eventhouse.
  4. Query through the lakehouse SQL analytics endpoint for supported read operations.

Explanation

Correct Answer

D:

The lakehouse SQL analytics endpoint exposes relational reads over Delta tables; writes should use a supported ingestion or Spark path. Read a created table through the endpoint and verify writes use the correct engine.

Incorrect Answers

A:

Files holds varied formats while Tables supports managed Delta table discovery and querying.

B:

A real-time analytics engine better suits time-series arrival and near-real-time exploration than batch-only warehouse refresh.

C:

Eventhouse targets event and time-series workloads and KQL queries instead of a generic transactional table store.

Question 168

A business model contains facts and dimensions that require warehouse SQL DML operations. Which choice should the engineer prioritize?

  1. Ingest into a KQL database within an Eventhouse.
  2. Land raw JSON in a lakehouse and transform curated outputs for warehouse consumption.
  3. Create a lakehouse with Delta tables in OneLake.
  4. Use a Fabric warehouse with supported T-SQL DML for the modeled tables.

Explanation

Correct Answer

D:

Warehouse SQL supports curated relational transformations rather than relying on the lakehouse read-only SQL endpoint. Test a controlled insert or merge in the warehouse.

Incorrect Answers

A:

Eventhouse targets event and time-series workloads and KQL queries instead of a generic transactional table store.

B:

Separating flexible raw landing from relational curation avoids forcing semi-structured files into a fixed star schema immediately.

C:

Lakehouse supports file and table data with Spark-centric ingestion and open storage patterns.

Question 169

A data steward needs to merge monthly Excel extracts with a mapping table in a graphical experience. Which transformation tool fits?

  1. Check join keys and relationship cardinality in the Merge step.
  2. Choose and validate a supported append update method for the configured data destination.
  3. Use Dataflow Gen2 Power Query merges and load the resulting table to an approved destination.
  4. Filter null and blank account identifiers before loading the curated output.

Explanation

Correct Answer

C:

Power Query handles visual join and shaping operations without writing distributed Spark code. Preview the merged key coverage before refresh.

Incorrect Answers

A:

A many-to-many or repeated-key join can multiply rows; removing duplicates afterward can conceal the modeling error.

B:

Destination write behavior determines whether previous records remain; query transformations alone do not select append semantics.

D:

A deterministic filter at transformation time prevents invalid business keys from reaching aggregates.

Question 170

A source column contains dates formatted as text, causing downstream schema mismatches. What should the dataflow do?

  1. Apply an explicit Power Query type-conversion step with suitable locale handling.
  2. Use a column split by a validated delimiter or parsing rule.
  3. Check join keys and relationship cardinality in the Merge step.
  4. Add the currency join and calculation in a curated Dataflow Gen2 query output.

Explanation

Correct Answer

A:

Explicit typing prevents text dates from being written to a destination with incompatible semantics. Test both ambiguous date strings and normal values.

Incorrect Answers

B:

An explicit split can normalize a compound field while preserving the original transformation definition for future refreshes.

C:

A many-to-many or repeated-key join can multiply rows; removing duplicates afterward can conceal the modeling error.

D:

Curated query steps express transformation without rewriting the raw source data.

Implementing Data Engineering Solutions Using Microsoft Fabric (DP-700) Practice Tests