DP-750적중율높은시험덤프최신덤프데모

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Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Prepare and process data30-35%- Ingest and transform data
  • 1. Apply medallion architecture patterns
  • 2. Transform data using SQL and Python
  • 3. Implement Delta Lake tables
  • 4. Implement data quality controls
  • 5. Implement streaming data processing
  • 6. Optimize storage and table performance
  • 7. Model and partition data
  • 8. Use Auto Loader and batch ingestion
Topic 2: Set up and configure an Azure Databricks environment15-20%- Create and configure Azure Databricks workspaces
  • 1. Configure compute resources and clusters
  • 2. Configure networking and connectivity
  • 3. Configure workspace settings
  • 4. Manage Databricks runtimes
Topic 3: Deploy and maintain data pipelines and workloads30-35%- Manage production workloads
  • 1. Create and manage Lakeflow Jobs
  • 2. Implement CI/CD processes
  • 3. Monitor and troubleshoot pipelines
  • 4. Integrate Git-based development workflows
  • 5. Deploy workloads using Databricks Asset Bundles
  • 6. Optimize workload performance and reliability
  • 7. Maintain production data engineering solutions
Topic 4: Secure and govern Unity Catalog objects15-20%- Implement governance and security
  • 1. Manage catalogs, schemas, and tables
  • 2. Manage data lineage and auditing
  • 3. Implement data-sharing capabilities
  • 4. Configure Unity Catalog
  • 5. Implement access control and permissions

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최신 Microsoft Certified: Fabric Data Engineer Associate DP-750 무료샘플문제 (Q16-Q21):

질문 # 16
You have an Azure Databricks workspace that is enabled for Unity Catalog. You plan to run the following PySpark code.

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

정답：

설명：

Explanation:
For HOTSPOT questions, each statement must be evaluated against the actual PySpark code shown in the answer area. Key evaluation principles:
DataFrames are immutable - every transformation returns a new DataFrame; the original is unchanged.
Transformations (filter, select, groupBy, join) are lazy and only execute when an action (show, count, write) is called.
Null handling: df.filter(col != None) is incorrect in PySpark due to SQL null semantics; use col.isNotNull() or dropna() instead. Schema changes: using mergeSchema=true or schema evolution handles new columns.
Write modes: ' overwrite ' replaces existing data; ' append ' adds to it.
Always check whether the code uses the correct Delta format (.format( ' delta ' )), Unity Catalog three-part naming, and whether write operations include a checkpointLocation for streaming queries. Evaluate each statement strictly on what the code does, not on what it might intend to do.
Reference: https://learn.microsoft.com/en-us/azure/databricks/pyspark/basics


질문 # 17
You have an Azure Databricks workspace that is enabled for Unity Catalog. You plan to run the following PySpark code.

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

정답：

설명：

Explanation:
For HOTSPOT questions, each statement must be evaluated against the actual PySpark code shown in the answer area. Key evaluation principles:
DataFrames are immutable - every transformation returns a new DataFrame; the original is unchanged.
Transformations (filter, select, groupBy, join) are lazy and only execute when an action (show, count, write) is called.
Null handling: df.filter(col != None) is incorrect in PySpark due to SQL null semantics; use col.isNotNull() or dropna() instead. Schema changes: using mergeSchema=true or schema evolution handles new columns.
Write modes: 'overwrite' replaces existing data; 'append' adds to it.
Always check whether the code uses the correct Delta format (.format('delta')), Unity Catalog three-part naming, and whether write operations include a checkpointLocation for streaming queries. Evaluate each statement strictly on what the code does, not on what it might intend to do.
Reference: https://learn.microsoft.com/en-us/azure/databricks/pyspark/basics


질문 # 18
You have an Azure Databricks workspace named Workspace1 that contains a lakehouse and is enabled for Unity Catalog.
You have a connection to a Microsoft SQL Server database named DB1.
You need to expose the schemas and tables of DB1 to meet the following requirements:
* The schemas and tables can be queried in Databricks.
* The schemas and tables appear alongside other Unity Catalog objects.
* The data is NOT copied into Databricks-managed storage.
Solution: You create a foreign catalog in Catalog Explorer.
Does this meet the goal?

정답：B

설명：
The correct answer is A - Yes.
A foreign catalog created through Lakehouse Federation in Catalog Explorer is the correct solution for all three requirements. Here's why it works:
The schemas and tables of DB1 can be queried in Databricks - Lakehouse Federation pushes the query down to the external SQL Server and returns results, so analysts write normal SQL in Databricks.
They appear alongside other Unity Catalog objects - the foreign catalog sits in the same three-tier hierarchy as native catalogs, schemas, and tables, visible in Catalog Explorer alongside all other Unity Catalog assets.
The data is NOT copied into Databricks-managed storage - foreign catalogs query data in place at the source; nothing is replicated or materialised in Databricks storage.
This is exactly the scenario Lakehouse Federation was built for.
Reference: https://learn.microsoft.com/en-us/azure/databricks/query-federation/lakehouse-federation


질문 # 19
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that writes records to a Delta table named Table1 by using a data quality rule named rule1 You need to meet the following requirements:
* Records that violate rule! must NOT be written to Table1. but the pipeline must continue processing valid records.
* Data engineers must be able to review expectation metrics by using minimal development effort.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

정답：

설명：

Explanation:
Two things are needed here:
For the rule enforcement: use @dlt.expect_or_drop. This drops any record that violates rule1 before it reaches Table1, while the pipeline continues processing all valid records. The table only ever receives clean data.
For reviewing metrics: the Lakeflow SDP Pipeline UI is the right tool - zero development effort required.
The pipeline graph shows expectation pass/fail counts directly on each table node, and the event log provides a detailed per-batch breakdown of how many records were dropped and why. Data engineers can inspect this at any time without writing additional monitoring queries or connecting external dashboards.
This combination is one of the strongest arguments for SDP over hand-coded Structured Streaming:
expectation observability is built in, not bolted on.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta-live-tables/expectations


질문 # 20
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1.
Table1 stores customer profile data.
Business users must analyze how customer profile records change over time. They must also be able to query earlier versions of the table.
You need to implement a solution that:
* Maintains persistent historical versions of customer profile records for long-term analysis.
* Allows users to query earlier versions of the Delta table.
* Minimizes maintenance effort.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

정답：

설명：

Explanation:
To record historical changes: Implement a Type 2 slowly changing dimension (SCD).
To support temporal analysis: Use Delta Lake time travel.
A Type 2 slowly changing dimension preserves customer-profile history by inserting a new record whenever a tracked attribute changes instead of overwriting the existing record. Effective dates, expiration dates, version values, or current-record indicators can identify which version applied during a particular period. This provides persistent business history for long-term analysis. Delta Lake time travel supports temporal analysis of the physical table by allowing users to query an earlier version with VERSION AS OF or TIMESTAMP AS OF. Time travel is useful for auditing and reproducing previous results, but its availability depends on retained Delta log entries and data files. Therefore, it should not replace a Type 2 SCD for permanent customer history. Together, the two features satisfy the historical-record and earlier-version requirements.


질문 # 21
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