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 |
|---|---|
| Topic 1: Production Pipelines and Orchestration | - Job scheduling and monitoring - Error handling and recovery strategies - Databricks Workflows |
| Topic 2: Data Modeling and Transformation | - Spark SQL transformations - Dimensional modeling concepts - Performance optimization techniques |
| Topic 3: Delta Lake and Data Management | - Time travel and versioning - Delta Lake transactions and ACID properties - Schema evolution and enforcement |
| Topic 4: Data Ingestion and Processing | - ETL pipeline design patterns - Batch and streaming ingestion with Auto Loader - Structured Streaming fundamentals |
| Topic 5: Databricks Lakehouse Platform Architecture | - Medallion architecture (Bronze, Silver, Gold) - Data governance concepts (Unity Catalog basics) - Workspace and cluster architecture |
Databricks Certified Data Engineer Professional Sample Questions:
A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.
Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.
Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?
- A. Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
- B. Both commands will fail. No new variables, tables, or views will be created.
- C. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.
- D. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.
- E. Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af: if this entity exists, Cmd 2 will succeed.
Correct Answer: C 🗳️
Explanation: Only visible for TestKingFree members. You can sign-up / login (it's free).
A Delta Lake table was created with the below query:
Realizing that the original query had a typographical error, the below code was executed:
ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
Which result will occur after running the second command?
- A. A new Delta transaction log Is created for the renamed table.
- B. The table reference in the metastore is updated and no data is changed.
- C. All related files and metadata are dropped and recreated in a single ACID transaction.
- D. The table reference in the metastore is updated and all data files are moved.
- E. The table name change is recorded in the Delta transaction log.
Correct Answer: B 🗳️
Explanation: Only visible for TestKingFree members. You can sign-up / login (it's free).
The following code has been migrated to a Databricks notebook from a legacy workload:
The code executes successfully and provides the logically correct results, however, it takes over
20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?
- A. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
- B. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
- C. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
- D. Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
- E. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
Correct Answer: E 🗳️
Explanation: Only visible for TestKingFree members. You can sign-up / login (it's free).
An external object storage container has been mounted to the location /mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:
After the database was successfully created and permissions configured, a member of the finance team runs the following code:
If all users on the finance team are members of the finance group, which statement describes how the tx_sales table will be created?
- A. An managed table will be created in the storage container mounted to /mnt/finance_eda_bucket.
- B. A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.
- C. A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.
- D. An external table will be created in the storage container mounted to /mnt/finance eda bucket.
- E. A managed table will be created in the DBFS root storage container.
Correct Answer: A 🗳️
Explanation: Only visible for TestKingFree members. You can sign-up / login (it's free).
What describes a primary technical challenge in ensuring consistent PII masking across all nodes in large-scale, distributed Databricks batch and streaming pipelines?
- A. Masking functions must be standardized and managed through Unity Catalog, with enforcement applied across all relevant datasets to avoid any data inconsistency.
- B. Dynamic data masking is applied only at rest, so it does not affect query performance.
- C. PII masking is only required for direct identifiers.
- D. Native masking in Databricks automatically synchronizes with all downstream external Databricks systems.
Correct Answer: A 🗳️
Explanation: Only visible for TestKingFree members. You can sign-up / login (it's free).
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