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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Developing Code for Data Processing using Python and SQL | ~22% | - Implement scalable Python/SQL code and project structures - Manage dependencies, libraries, and UDFs - Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader |
| Cost and Performance Optimization | ~13% | - Optimize queries, clusters, and storage - Leverage system tables and observability tools |
| Data Modeling | ~10% | - Apply dimensional modeling techniques - Design scalable Delta Lake schemas and clustering |
| Monitoring, Logging, and Troubleshooting | ~8% | - Use Spark UI, Query Profiler, and system tables - Diagnose common pipeline and job failures |
| CI/CD, Testing, and Deployment | ~6% | - Deploy with Declarative Automation Bundles, CLI, and REST API - Implement testing and deployment pipelines |
| Streaming Workloads and Change Data Capture | ~11% | - Apply AUTO CDC APIs and exactly-once semantics - Implement reliable streaming pipelines |
| Data Sharing and Federation | ~8% | - Configure Delta Sharing and Lakehouse Federation |
| Data Transformation, Cleansing, and Quality | ~12% | - Enforce data quality and quarantine bad data - Apply advanced Spark transformations |
| Security and Governance | ~10% | - Manage Unity Catalog permissions and ACLs - Implement row-level security, column masking, and compliance |
Databricks Certified Data Engineer Professional Sample Questions:
1. A data engineering team is setting up deployment automation. To deploy workspace assets remotely using the Databricks CLI command, they must configure it with proper authentication.
Which authentication approach will provide the highest level of security?
A) Use a service principal ID and its OAuth client secret.
B) Use a service principal and its Personal Access Token.
C) Use a shared user account and its OAuth client secret.
D) Use a service principal with OAuth token federation.
2. A data engineering team needs to create a SQL Alert that monitors data quality across multiple columns in their customer table. They want to trigger an alert when both the percentage of customers with missing email addresses exceeds 15% AND the percentage of customers with invalid phone number formats exceeds 10%. Which SQL query pattern is appropriate for implementing this multi-column alert condition?
A) SELECT email, phone FROM customers WHERE email IS NULL AND phone NOT RLIKE 'ˆ[0-9-
+()\\s]+$'
B) SELECT COUNT (*) FROM customers WHERE email IS NULL OR phone_format_invalid = true
C) SELECT email_null_pct, phone_invalid_pct FROM (SELECT (COUNT(CASE WHEN email IS NULL THEN 1 END) *
100.0/COUNT (*)) as email_null_pct, (COUNT(CASE WHEN phone NOT RLIKE 'ˆ[0-9-+()\\s]+$' THEN 1 END)*
100.0/COUNT (*)) as phone_invalid_pct FROM customers)
D) SELECT CASE WHEN email_null_pct >15 AND phone_invalid_pct> 10 THEN 1 ELSE 0 END FROM (SELECT (COUNT (CASE WHEN email IS NULL THEN 1 END) * 100.0 / COUNT (*)) as phone_invalid_pct FROM customers) metrics
3. 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) Cluster Event Log
B) Ganglia
C) Executor's log file
D) Workspace audit logs
E) Driver's log file
4. A data team is implementing an append-only Delta Lake pipeline that processes both batch and streaming data. They want to ensure that schema changes in the source data are automatically incorporated without breaking the pipeline. Which configuration should the team use when writing data to the Delta table?
A) overwriteSchema = true
B) mergeSchema = true
C) ignoreChanges = false
D) validateSchema = false
5. Which of the following technologies can be used to identify key areas of text when parsing Spark Driver log4j output?
A) Julia
B) Scala Datasets
C) pyspsark.ml.feature
D) Regex
E) C++
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C | Question # 3 Answer: A | Question # 4 Answer: B | Question # 5 Answer: D |
