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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Developing Code for Data Processing using Python and SQL | 22% | - Implement complex data processing logic - Use Databricks-specific libraries and APIs - Write efficient and maintainable code |
| Topic 2: Ensuring Data Security and Compliance | 10% | - Ensure data privacy and compliance - Implement access control and permissions - Secure data at rest and in transit |
| Topic 3: Debugging and Deploying | 10% | - Implement CI/CD and DevOps practices - Troubleshoot and debug pipelines - Deploy using Asset Bundles, CLI, and APIs |
| Topic 4: Data Ingestion & Acquisition | 7% | - Handle incremental and batch data loads - Use Auto Loader and structured streaming - Ingest data from diverse sources |
| Topic 5: Data Governance | 7% | - Manage data assets and metadata - Use Unity Catalog for governance - Enforce data policies and standards |
| Topic 6: Data Sharing and Federation | 5% | - Manage cross-platform data access - Use Delta Sharing for secure data sharing - Implement Lakehouse Federation |
| Topic 7: Data Transformation, Cleansing, and Quality | 10% | - Apply data cleansing and validation rules - Enforce data quality standards - Implement schema evolution and management |
| Topic 8: Monitoring and Alerting | 10% | - Monitor pipeline performance and health - Track data lineage and metrics - Set up alerts and notifications |
| Topic 9: Cost & Performance Optimisation | 13% | - Optimize compute and storage resources - Apply cost management best practices - Improve query and pipeline performance |
| Topic 10: Data Modelling | 6% | - Design Medallion Architecture - Implement dimensional and relational models - Optimize table design and partitioning |
Databricks Certified Data Engineer Professional Sample Questions:
1. A Data Engineer is building a fraud detection pipeline that calls out to Open AI, via a Python library, and needs to include an access token when using the API. Which Databricks CLI command should the Data Engineer use to create the secret?
A) databricks tokens put-token SCOPE KEY; dbutils.tokens.get (SCOPE, KEY)
B) databricks tokens put-token KEY SCOPE; dbutils.secrets.get (KEY, SCOPE)
C) databricks secrets put-secret SCOPE KEY; dbutils.secrets.get (SCOPE, KEY)
D) databricks secrets put-secret KEY SCOPE; dbutils.secrets.get (KEY, SCOPE)
2. A data engineer is running a groupBy aggregation on a massive user activity log grouped by user_id. A few users have millions of records, causing task skew and long runtimes. Which technique will fix the skew in this aggregation?
A) Increase the Spark driver memory and retry.
B) Use salting by adding a random prefix to skewed keys before aggregation, then aggregate again after removing the prefix.
C) Use reduceByKey instead of groupBy to avoid shuffles.
D) Filter out the skewed users before the aggregation.
3. 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 Delta Lake tables were able to leverage the dynamic file pruning optimization.
B) Delta Lake enables data skipping and file pruning using a vectorized Parquet reader.
C) Shuffle Hash Joins are always more efficient than Sort Merge Joins.
D) The queries against the ORC tables leveraged the dynamic data skipping optimization but not the dynamic file pruning optimization.
4. The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property "contains_pii" = true.
The following SQL DDL statement is executed to create a new table:
Which command allows manual confirmation that these three requirements have been met?
A) SHOW TBLPROPERTIES dev.pii test
B) DESCRIBE DETAIL dev.pii test
C) DESCRIBE HISTORY dev.pii test
D) SHOW TABLES dev
E) DESCRIBE EXTENDED dev.pii test
5. The data engineering team maintains the following code:
Assuming that this code produces logically correct results and the data in the source table has been de-duplicated and validated, which statement describes what will occur when this code is executed?
A) The silver_customer_sales table will be overwritten by aggregated values calculated from all records in the gold_customer_lifetime_sales_summary table as a batch job.
B) An incremental job will detect if new rows have been written to the silver_customer_sales table; if new rows are detected, all aggregates will be recalculated and used to overwrite the gold_customer_lifetime_sales_summary table.
C) An incremental job will leverage running information in the state store to update aggregate values in the gold_customer_lifetime_sales_summary table.
D) The gold_customer_lifetime_sales_summary table will be overwritten by aggregated values calculated from all records in the silver_customer_sales table as a batch job.
E) A batch job will update the gold_customer_lifetime_sales_summary table, replacing only those rows that have different values than the current version of the table, using customer_id as the primary key.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: B | Question # 4 Answer: E | Question # 5 Answer: D |
Reginald -
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