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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Ensuring Data Security and Compliance	10%	- Implement access control and permissions - Ensure data privacy and compliance - Secure data at rest and in transit
Topic 2: Data Ingestion & Acquisition	7%	- Handle incremental and batch data loads - Use Auto Loader and structured streaming - Ingest data from diverse sources
Topic 3: Cost & Performance Optimisation	13%	- Apply cost management best practices - Improve query and pipeline performance - Optimize compute and storage resources
Topic 4: Data Governance	7%	- Use Unity Catalog for governance - Enforce data policies and standards - Manage data assets and metadata
Topic 5: Data Sharing and Federation	5%	- Implement Lakehouse Federation - Use Delta Sharing for secure data sharing - Manage cross-platform data access
Topic 6: Debugging and Deploying	10%	- Deploy using Asset Bundles, CLI, and APIs - Troubleshoot and debug pipelines - Implement CI/CD and DevOps practices
Topic 7: Data Transformation, Cleansing, and Quality	10%	- Implement schema evolution and management - Enforce data quality standards - Apply data cleansing and validation rules
Topic 8: Data Modelling	6%	- Implement dimensional and relational models - Design Medallion Architecture - Optimize table design and partitioning
Topic 9: Developing Code for Data Processing using Python and SQL	22%	- Implement complex data processing logic - Write efficient and maintainable code - Use Databricks-specific libraries and APIs
Topic 10: Monitoring and Alerting	10%	- Track data lineage and metrics - Monitor pipeline performance and health - Set up alerts and notifications

>> Databricks-Certified-Data-Engineer-Professional資訊 <<

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最新的 Databricks Certification Databricks-Certified-Data-Engineer-Professional 免費考試真題 (Q231-Q236):

問題 #231
The Databricks CLI is use to trigger a run of an existing job by passing the job_id parameter. The response that the job run request has been submitted successfully includes a filed run_id.
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from Which statement describes what the number alongside this field represents?

A. The total number of jobs that have been run in the workspace.
B. The number of times the job definition has been run in the workspace.
C. The job_id is returned in this field.
D. The globally unique ID of the newly triggered run.
E. The job_id and number of times the job has been are concatenated and returned.

答案：D

解題說明：
When triggering a job run using the Databricks CLI, the run_id field in the response represents a globally unique identifier for that particular run of the job. This run_id is distinct from the job_id.
While the job_id identifies the job definition and is constant across all runs of that job, the run_id is unique to each execution and is used to track and query the status of that specific job run within the Databricks environment. This distinction allows users to manage and reference individual executions of a job directly.

問題 #232
Each configuration below is identical to the extent that each cluster has 400 GB total of RAM 160 total cores and only one Executor per VM.
Given an extremely long-running job for which completion must be guaranteed, which cluster configuration will be able to guarantee completion of the job in light of one or more VM failures?

A. - Total VMs: 8
- 50 GB per Executor
- 20 Cores / Executor
B. - Total VMs: 1
- 400 GB per Executor
- 160 Cores/Executor
C. - Total VMs: 16
- 25 GB per Executor
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
- 10 Cores / Executor
D. - Total VMs: 4
- 100 GB per Executor
- 40 Cores / Executor
E. - Total VMs: 2
- 200 GB per Executor
- 80 Cores / Executor

答案：C

解題說明：
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

問題 #233
Review the following error traceback:

Which statement describes the error being raised?

A. The code executed was PvSoark but was executed in a Scala notebook.
B. There is no column in the table named heartrateheartrateheartrate
C. There is a type error because a DataFrame object cannot be multiplied.
D. There is a type error because a column object cannot be multiplied.
E. There is a syntax error because the heartrate column is not correctly identified as a column.

答案：B

解題說明：
https://sparkbyexamples.com/spark/spark-cannot-resolve-given-input-columns/

問題 #234
A data engineering team is migrating off its legacy Hadoop platform. As part of the process, they are evaluating storage formats for performance comparison. The legacy platform uses ORC and RCFile formats. After converting a subset of data to Delta Lake, they noticed significantly better query performance. Upon investigation, they discovered that queries reading from Delta tables leveraged a Shuffle Hash Join, whereas queries on legacy formats used Sort Merge Joins. The queries reading Delta Lake data also scanned less data. Which reason could be attributed to the difference in query performance?

A. The queries against the ORC tables leveraged the dynamic data skipping optimization but not the dynamic file pruning optimization.
B. Shuffle Hash Joins are always more efficient than Sort Merge Joins.
C. Delta Lake enables data skipping and file pruning using a vectorized Parquet reader.
D. The queries against the Delta Lake tables were able to leverage the dynamic file pruning optimization.

答案：C

解題說明：
Delta Lake outperforms legacy Hadoop formats because it leverages Parquet-based storage, data skipping, and file pruning. According to Databricks documentation, Delta Lake automatically stores detailed statistics (min/max values and file-level metadata) in the transaction log. During query planning, the engine uses these statistics to skip entire files that do not match query filters, a process called data skipping and file pruning. Additionally, Delta uses a vectorized Parquet reader, which reduces I/O and CPU overhead. Together, these optimizations allow Delta to scan significantly less data and produce more efficient physical query plans (e.g., Shuffle Hash Join instead of Sort Merge Join). The performance gain is due to efficient data skipping, not the inherent superiority of join type.

問題 #235
A platform engineer needs to report the resource consumption, categorized by SKU tier, across all workspaces. The engineer decides to use the system.billing.usage system table to create a query. Which SQL query will accurately return the daily usage by product?

答案：C

解題說明：
This query correctly aggregates usage at a daily granularity by truncating the usage start timestamp to the day and summing the usage quantity, which represents DBUs. Grouping by both the derived daily value and the SKU name ensures usage is accurately categorized by product tier across all workspaces.

問題 #236
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