Databricks Certified Data Engineer Professional Exam: Databricks-Certified-Data-Engineer-Professional Exam
"Databricks Certified Data Engineer Professional Exam", also known as Databricks-Certified-Data-Engineer-Professional exam, is a Databricks Certification. With the complete collection of questions and answers, PrepAwayPDF has assembled to take you through 250 Q&As to your Databricks-Certified-Data-Engineer-Professional Exam preparation. In the Databricks-Certified-Data-Engineer-Professional exam resources, you will cover every field and category in Databricks Certification Certification helping to ready you for your successful Databricks Certification.
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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Processing | 28% | - Data Transformation - Spark SQL - Structured Streaming - ETL Pipelines |
| Data Modeling and Storage | 20% | - File Formats - Storage Optimization - Data Modeling |
| Databricks Lakehouse Platform | 24% | - Data Management - Delta Lake - Lakehouse Architecture - Unity Catalog |
| Data Quality and Governance | 12% | - Governance - Data Lineage - Data Quality |
| Monitoring and Troubleshooting | 16% | - Monitoring - Troubleshooting - Performance Optimization |
Databricks Certified Data Engineer Professional Sample Questions:
A data engineering workspace was automatically enabled for Unity Catalog, creating a workspace catalog. New team members report they can create tables in the default schema but cannot access table in other schemas within the same workspace catalog. Why are the new team members unable to access tables in other schemas?
- A. New users only receive CREATE TABLE privileges on the default schema.
- B. Workspace catalog permissions are not subject to inheritance rules.
- C. Tables in other schemas require additional BROWSEprivileges that new users don't receive automatically
- D. Workspace users receive USE CATALOG and specific privileges on default schema only.
Correct Answer: D 🗳️
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Which method can be used to determine the total wall-clock time it took to execute a query?
- A. Open the Query Profiler associated with that query and use the Total wall-clock duration metric.
- B. In the Spark UI, take the sum of all task durations that ran across all stages for all jobs associated with that query.
- C. Open the Query Profiler associated with that query and use the Aggregated task time metric.
- D. In the Spark UI, take the job duration of the longest-running job associated with that query.
Correct Answer: A 🗳️
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When monitoring a complex workload, being able to see the query plan is critical to understanding what the workload is doing. Where can the visualization of the query plan be found?
- A. In the Spark UI, under the SQL/DataFrame tab
- B. In the Query Profiler, under the Stages tab
- C. In the Query Profiler, under Query Source
- D. In the Spart UI, under the Jobs tab
Correct Answer: A 🗳️
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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.
Correct Answer: C 🗳️
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A data engineer is tasked with ensuring that a Delta table in Databricks continuously retains deleted files for 15 days (instead of the default 7 days), in order to permanently comply with the organization's data retention policy. Which code snippet correctly sets this retention period for deleted files?
- A. spark.sql("ALTER TABLE my_table SET TBLPROPERTIES
('delta.deletedFileRetentionDuration' = 'interval 15 days')") - B. from delta.tables import *
deltaTable = DeltaTable.forPath(spark, "/mnt/data/my_table")
deltaTable.deletedFileRetentionDuration = "interval 15 days" - C. spark.sql("VACUUM my_table RETAIN 15 HOURS")
- D. spark.conf.set("spark.databricks.delta.deletedFileRetentionDuration", "15 days")
Correct Answer: A 🗳️
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