- Exam Code: Databricks-Certified-Data-Engineer-Professional
- 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 |
|---|---|
| Delta Lake and Data Management | - Schema evolution and enforcement - Time travel and versioning - Delta Lake transactions and ACID properties |
| Data Ingestion and Processing | - ETL pipeline design patterns - Batch and streaming ingestion with Auto Loader - Structured Streaming fundamentals |
| Production Pipelines and Orchestration | - Databricks Workflows - Job scheduling and monitoring - Error handling and recovery strategies |
| Data Modeling and Transformation | - Dimensional modeling concepts - Performance optimization techniques - Spark SQL transformations |
| 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:
1. The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalid latitude and longitude values in the activity_details table have been breaking their ability to use other geolocation processes.
A junior engineer has written the following code to add CHECK constraints to the Delta Lake table:
A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.
Which statement explains the cause of this failure?
A) The activity details table already contains records that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table.
B) The current table schema does not contain the field valid coordinates; schema evolution will need to be enabled before altering the table to add a constraint.
C) The activity details table already exists; CHECK constraints can only be added during initial table creation.
D) The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.
E) Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.
2. When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?
A) Cluster: Existing All-Purpose Cluster;
Retries: None;
Maximum Concurrent Runs: 1
B) Cluster: New Job Cluster;
Retries: None;
Maximum Concurrent Runs: 1
C) Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
D) Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
E) Cluster: New Job Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: Unlimited
3. 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
4. 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 fail. No new variables, tables, or views will be created.
B) Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.
C) 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.
D) Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.
E) Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
5. A junior developer complains that the code in their notebook isn't producing the correct results in the development environment. A shared screenshot reveals that while they're using a notebook versioned with Databricks Repos, they're using a personal branch that contains old logic. The desired branch named dev-2.3.9 is not available from the branch selection dropdown.
Which approach will allow this developer to review the current logic for this notebook?
A) Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch
B) Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9
C) Merge all changes back to the main branch in the remote Git repository and clone the repo again
D) Use Repos to merge the current branch and the dev-2.3.9 branch, then make a pull request to sync with the remote repository
E) Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: C | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: E |
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