Exam Name: Databricks Certified Data Engineer Professional Exam
Certification Provider: Databricks
Corresponding Certification: Databricks Certification
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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data Modeling and Transformation | - Performance optimization techniques - Spark SQL transformations - Dimensional modeling concepts |
| Production Pipelines and Orchestration | - Databricks Workflows - Error handling and recovery strategies - Job scheduling and monitoring |
| Databricks Lakehouse Platform Architecture | - Medallion architecture (Bronze, Silver, Gold) - Data governance concepts (Unity Catalog basics) - Workspace and cluster architecture |
| Data Ingestion and Processing | - Batch and streaming ingestion with Auto Loader - ETL pipeline design patterns - Structured Streaming fundamentals |
| Delta Lake and Data Management | - Time travel and versioning - Schema evolution and enforcement - Delta Lake transactions and ACID properties |
Databricks Certified Data Engineer Professional Sample Questions:
A data engineer is evaluating tools to build a production-grade data pipeline. The team must process change data from cloud object storage, filter out or isolate invalid records, and ensure the timely delivery of clean data to downstream consumers. The team is small, under tight deadlines, and wants to minimize operational overhead while keeping pipelines auditable and maintainable.
Which approach should the data engineer implement?
- A. Use LDP to build declarative pipelines with Streaming Tables and Materialized Views, leveraging built-in support for data expectations and incremental processing.
- B. Implement ingestion using Auto Loader with Structured Streaming, and manage invalid data handling and table updates using checkpointing and merge logic.
- C. Ingest data directly into Delta tables via Spark jobs, apply data quality filters using UDFs, and use LDP for creating Materialized Views.
- D. Use a hybrid approach: Ingest with Auto Loader into Bronze tables, then process using SQL queries in Databricks Workflows to generate cleaned Silver and Gold tables on a schedule.
Correct Answer: A 🗳️
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A platform engineer is creating catalogs and schemas for the development team to use.
The engineer has created an initial catalog, catalog_A, and initial schema, schema_A. The engineer has also granted USE CATALOG, USE SCHEMA, and CREATE TABLE to the development team so that the engineer can begin populating the schema with new tables.
Despite being owner of the catalog and schema, the engineer noticed that they do not have access to the underlying tables in Schema_A.
What explains the engineer's lack of access to the underlying tables?
- A. Users granted with USE CATALOG can modify the owner's permissions to downstream tables.
- B. The owner of the schema does not automatically have permission to tables within the schema, but can grant them to themselves at any point.
- C. The platform engineer needs to execute a REFRESH statement as the table permissions did not automatically update for owners.
- D. Permissions explicitly given by the table creator are the only way the Platform Engineer could access the underlying tables in their schema.
Correct Answer: B 🗳️
Explanation: Only visible for PrepAwayExam members. You can sign-up / login (it's free).
A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?
- A. Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.
- B. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
- C. Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.
- D. Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.
- E. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.
Correct Answer: E 🗳️
Explanation: Only visible for PrepAwayExam members. You can sign-up / login (it's free).
A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?
- A. Workspace audit logs
- B. Cluster Event Log
- C. Ganglia
- D. Driver's log file
- E. Executor's log file
Correct Answer: B 🗳️
Explanation: Only visible for PrepAwayExam members. You can sign-up / login (it's free).
A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impressions led to monetizable clicks.
In the code below, Impressions is a streaming DataFrame with a watermark ("event_time", "10 minutes")
The data engineer notices the query slowing down significantly.
Which solution would improve the performance?
- A. Joining on event time constraint: clickTime >= impressionTime - interval 3 hours and removing watermarks
- B. Joining on event time constraint: clickTime == impressionTime using a leftOuter join
- C. Joining on event time constraint: clickTime >= impressionTime AND clickTime <= impressionTime interval 1 hour
- D. Joining on event time constraint: clickTime + 3 hours < impressionTime - 2 hours
Correct Answer: C 🗳️
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