cert
cert-1
cert-2

Pass Microsoft DP-700 Exam in First Attempt Guaranteed!

Get 100% Latest Exam Questions, Accurate & Verified Answers to Pass the Actual Exam!
30 Days Free Updates, Instant Download!

cert-5
cert-6
DP-700 Exam - Verified By Experts
DP-700 Premium Bundle
$14.99

DP-700 Premium Bundle

$74.99
$89.98
  • Premium File 160 Questions & Answers. Last update: Nov 10, 2026
  • Training Course 160 Video Lectures
 
$89.98
$74.99
accept 159 downloads in last 7 days
block-screenshots
Purchase Microsoft DP-700 Exam Training Products Individually
Microsoft DP-700 Premium File
Premium File 160 Q&A
$76.99$69.99
Microsoft DP-700 Video Training Course
Training Course 160 Lectures
$21.99 $19.99
Microsoft DP-700 Practice Test 16

Question 150

Transactions arrive before their customer dimension record exists. What technique preserves fact ingestion without inventing a valid customer?

  1. Create one conformed date dimension containing agreed calendar and fiscal attributes.
  2. Apply a Type 1 overwrite to the descriptive dimension attribute.
  3. Use a controlled unknown or inferred-member dimension key and reconcile it later.
  4. Maintain Type 2 dimension versions with effective dates and durable surrogate keys.

Explanation

Correct Answer

C:

An unknown member prevents orphaned foreign keys while preserving the pending fact for subsequent conformance. Load a late customer and check the related facts are updated.

Incorrect Answers

A:

Shared date semantics keep financial reporting consistent across facts; per-report date parsing risks mismatched periods.

B:

Type 1 correction updates current descriptive values without retaining alternative historical spelling versions.

D:

Type 2 history associates old facts with the dimension attributes valid at transaction time.

Question 151

Several dimension systems use overlapping integer customer IDs. How should keys be prepared for a combined model?

  1. Apply a Type 1 overwrite to the descriptive dimension attribute.
  2. Declare the intended fact grain and aggregate line measures to the correct order key.
  3. Create stable warehouse surrogate keys with source-system-aware business-key mapping.
  4. Separate conformed customer attributes into a customer dimension and retain fact foreign keys.

Explanation

Correct Answer

C:

A surrogate mapping prevents accidental joins across separate source identities that share numeric IDs. Test matching source IDs from two systems.

Incorrect Answers

A:

Type 1 correction updates current descriptive values without retaining alternative historical spelling versions.

B:

Fact grain determines which measures can be summed without duplication; an implicit join may multiply amounts.

D:

Dimension separation reduces repeated descriptive attributes in high-volume facts and supports consistent slicing.

Question 152

A date dimension is needed for fiscal-quarter grouping across multiple fact tables. What design best supports reuse?

  1. Use a controlled unknown or inferred-member dimension key and reconcile it later.
  2. Create one conformed date dimension containing agreed calendar and fiscal attributes.
  3. Declare the intended fact grain and aggregate line measures to the correct order key.
  4. Create stable warehouse surrogate keys with source-system-aware business-key mapping.

Explanation

Correct Answer

B:

Shared date semantics keep financial reporting consistent across facts; per-report date parsing risks mismatched periods. Compare quarter grouping across two related facts.

Incorrect Answers

A:

An unknown member prevents orphaned foreign keys while preserving the pending fact for subsequent conformance.

C:

Fact grain determines which measures can be summed without duplication; an implicit join may multiply amounts.

D:

A surrogate mapping prevents accidental joins across separate source identities that share numeric IDs.

Question 153

A source fact has line-level detail but the target metric requires order-level results. What should be defined first?

  1. Declare the intended fact grain and aggregate line measures to the correct order key.
  2. Create one conformed date dimension containing agreed calendar and fiscal attributes.
  3. Maintain Type 2 dimension versions with effective dates and durable surrogate keys.
  4. Separate conformed customer attributes into a customer dimension and retain fact foreign keys.

Explanation

Correct Answer

A:

Fact grain determines which measures can be summed without duplication; an implicit join may multiply amounts. Test orders containing multiple lines.

Incorrect Answers

B:

Shared date semantics keep financial reporting consistent across facts; per-report date parsing risks mismatched periods.

C:

Type 2 history associates old facts with the dimension attributes valid at transaction time.

D:

Dimension separation reduces repeated descriptive attributes in high-volume facts and supports consistent slicing.

Question 154

IoT sensors publish continuously and must land in Delta tables without waiting for a nightly batch. Which ingestion pattern fits?

  1. Use a continuously running supported stream-to-Delta ingestion flow with durable progress state.
  2. Use an explicit supported sliding time-window aggregation with a five-minute hop if overlapping updates are needed.
  3. Aggregate using event time with an explicit late-data policy rather than only processing time.
  4. Deduplicate on a stable event identifier with a defined state or retention boundary.

Explanation

Correct Answer

A:

Streaming sinks can commit ongoing events to Delta while checkpoints support recovery. Confirm records continue arriving after the initial run.

Incorrect Answers

B:

Sliding windows evaluate overlapping event-time intervals and differ from disjoint tumbling windows.

C:

Event-time windows represent when the underlying event occurred, independent of transport delays.

D:

Duplicate delivery should not inflate business counts; deduplication needs a key and an appropriate state lifetime.

Question 155

An event consumer restarts and reprocesses its last batch. Which design prevents doubled order totals?

  1. Persist an immutable raw stream layer and derive cleaned or aggregate outputs downstream.
  2. Use an explicit supported sliding time-window aggregation with a five-minute hop if overlapping updates are needed.
  3. Use a durable checkpoint with a compatible transactional sink or idempotent write logic.
  4. Aggregate using event time with an explicit late-data policy rather than only processing time.

Explanation

Correct Answer

C:

Recovery may replay input; correctness depends on durable offsets, compatible state, and safe target commits. Restart after a simulated failure and compare totals.

Incorrect Answers

A:

Retaining original events makes reprocessing and investigation possible when transformation rules change.

B:

Sliding windows evaluate overlapping event-time intervals and differ from disjoint tumbling windows.

D:

Event-time windows represent when the underlying event occurred, independent of transport delays.

Question 156

A streaming source contains event timestamps later than their arrival time. Which clock should drive business-time windowing?

  1. Use a durable checkpoint with a compatible transactional sink or idempotent write logic.
  2. Persist an immutable raw stream layer and derive cleaned or aggregate outputs downstream.
  3. Aggregate using event time with an explicit late-data policy rather than only processing time.
  4. Use a continuously running supported stream-to-Delta ingestion flow with durable progress state.

Explanation

Correct Answer

C:

Event-time windows represent when the underlying event occurred, independent of transport delays. Inject a delayed event and inspect its assigned time window.

Incorrect Answers

A:

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

B:

Retaining original events makes reprocessing and investigation possible when transformation rules change.

D:

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

Question 157

A streaming telemetry flow must retain raw events for forensic replay in addition to a cleaned aggregate. What design is best?

  1. Aggregate using event time with an explicit late-data policy rather than only processing time.
  2. Persist an immutable raw stream layer and derive cleaned or aggregate outputs downstream.
  3. Deduplicate on a stable event identifier with a defined state or retention boundary.
  4. Use a durable checkpoint with a compatible transactional sink or idempotent write logic.

Explanation

Correct Answer

B:

Retaining original events makes reprocessing and investigation possible when transformation rules change. Replay a subset from raw events into a new calculation.

Incorrect Answers

A:

Event-time windows represent when the underlying event occurred, independent of transport delays.

C:

Duplicate delivery should not inflate business counts; deduplication needs a key and an appropriate state lifetime.

D:

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

Question 158

A streaming sink accumulates many duplicate sensor readings with identical event IDs. What should be added?

  1. Capture raw payload plus schema/version metadata and validate into a curated stream.
  2. Persist an immutable raw stream layer and derive cleaned or aggregate outputs downstream.
  3. Deduplicate on a stable event identifier with a defined state or retention boundary.
  4. Use a continuously running supported stream-to-Delta ingestion flow with durable progress state.

Explanation

Correct Answer

C:

Duplicate delivery should not inflate business counts; deduplication needs a key and an appropriate state lifetime. Publish repeat event IDs and inspect the persisted result.

Incorrect Answers

A:

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

B:

Retaining original events makes reprocessing and investigation possible when transformation rules change.

D:

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

Question 159

A live feed has variable schema versions across devices. What should ingestion preserve before strict typing?

  1. Use a continuously running supported stream-to-Delta ingestion flow with durable progress state.
  2. Use a durable checkpoint with a compatible transactional sink or idempotent write logic.
  3. Aggregate using event time with an explicit late-data policy rather than only processing time.
  4. Capture raw payload plus schema/version metadata and validate into a curated stream.

Explanation

Correct Answer

D:

Raw preservation avoids silent data loss when evolving fields do not fit a single rigid schema. Inspect both old and new device payloads.

Incorrect Answers

A:

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

B:

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

C:

Event-time windows represent when the underlying event occurred, independent of transport delays.

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.

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