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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Data Sharing and Federation | - Lakehouse Federation
|
| Topic 2: Data Transformation, Cleansing, and Quality | - Advanced Data Transformation
|
| Topic 3: Debugging and Deploying | - Debugging and Troubleshooting
|
| Topic 4: Data Ingestion & Acquisition | - Design and implement data ingestion pipelines
|
| Topic 5: Monitoring and Alerting | - Monitoring
|
| Topic 6: Developing Code for Data Processing using Python and SQL | - Using Python and Tools for Development
|
| Topic 7: Data Modelling | - Scalable Data Models
|
| Topic 8: Cost & Performance Optimisation | - Cost Optimization
|
| Topic 9: Data Governance | - Metadata and Discoverability
|
| Topic 10: Ensuring Data Security and Compliance | - Compliance
|
Databricks Certified Data Engineer Professional Sample Questions:
A job runs four independent tasks (X, Y, Z, W) in parallel to process regional sales data. The Data Engineering team recently updated its cluster policy to ban cost-prohibitive instance types. Task Y now fails due to the newly enforced cluster policy restricting the use of a specific instance type.
A data engineer needs to resolve the failure quickly without disrupting the other tasks. How should the data engineer resolve the failure of tasks?
- A. Use "Repair run", override the cluster configuration for Task Y to use a permitted instance type, and let Databricks re-run only Task Y.
- B. Manually create a new cluster for Task Y, update the job configuration, and trigger a full re-run.
- C. Edit the global cluster policy to allow the restricted instance type, then re-run the entire job.
- D. Delete the failed run, disable the cluster policy, and re-execute all tasks.
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A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.
The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.
Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?
- A. Tungsten encoding used by Databricks is optimized for storing string data: newly-added native support for querying JSON strings means that string types are always most efficient.
- B. Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.
- C. Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
- D. By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.
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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: 16
- 25 GB per Executor
- 10 Cores / Executor - B. - Total VMs: 1
- 400 GB per Executor
- 160 Cores/Executor - C. - Total VMs: 8
- 50 GB per Executor
- 20 Cores / Executor - D. - Total VMs: 4
- 100 GB per Executor
- 40 Cores / Executor - E. - Total VMs: 2
- 200 GB per Executor
- 80 Cores / Executor
A data architect is designing a Databricks solution to efficiently process data for different business requirements. In which scenario should a data engineer use a materialized view compared to a streaming table?
- A. Ingesting data from Apache Kafka topics with sub-second processing requirements for immediate alerting.
- B. Processing high-volume, continuous clickstream data from a website to monitor user behavior in real-time.
- C. Implementing a CDC (Change Data Capture) pipeline that needs to detect and respond to database changes within seconds.
- D. Precomputing complex aggregations and joins from multiple large tables to accelerate BI dashboard performance.
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A data engineer needs to create an application that will collect information about the latest job run including the repair history. How should the data engineer format the request?
- A. Call/api/2.1/jobs/runs/get with the job_id and include_history parameters
- B. Call/api/2.1/jobs/runs/get with the run_id and include_history parameters
- C. Call/api/2.1/jobs/runs/list with the run_id and include_history parameters
- D. Call/api/2.1/jobs/runs/list with the job_id and include_history parameters
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