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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Monitoring and Alerting	10%	- Set up alerts and notifications - Track data lineage and metrics - Monitor pipeline performance and health
Topic 2: Data Governance	7%	- Enforce data policies and standards - Use Unity Catalog for governance - Manage data assets and metadata
Topic 3: Debugging and Deploying	10%	- Troubleshoot and debug pipelines - Implement CI/CD and DevOps practices - Deploy using Asset Bundles, CLI, and APIs
Topic 4: Data Modelling	6%	- Design Medallion Architecture - Optimize table design and partitioning - Implement dimensional and relational models
Topic 5: Data Ingestion & Acquisition	7%	- Handle incremental and batch data loads - Ingest data from diverse sources - Use Auto Loader and structured streaming
Topic 6: Ensuring Data Security and Compliance	10%	- Ensure data privacy and compliance - Secure data at rest and in transit - Implement access control and permissions
Topic 7: Data Sharing and Federation	5%	- Use Delta Sharing for secure data sharing - Manage cross-platform data access - Implement Lakehouse Federation
Topic 8: Cost & Performance Optimisation	13%	- Apply cost management best practices - Improve query and pipeline performance - Optimize compute and storage resources
Topic 9: Developing Code for Data Processing using Python and SQL	22%	- Write efficient and maintainable code - Use Databricks-specific libraries and APIs - Implement complex data processing logic
Topic 10: Data Transformation, Cleansing, and Quality	10%	- Apply data cleansing and validation rules - Implement schema evolution and management - Enforce data quality standards

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q71-Q76):

NEW QUESTION # 71
The data engineer is using Spark's MEMORY_ONLY storage level. Which indicators should the data engineer look for in the spark UI's Storage tab to signal that a cached table is not performing optimally?

A. On Heap Memory Usage is within 75% of off Heap Memory usage
B. The RDD Block Name included the '' annotation signaling failure to cache
C. Size on Disk is> 0
D. The number of Cached Partitions> the number of Spark Partitions
E. Size on Disk is < Size in Memory

Answer: C

Explanation:
When using Spark's MEMORY_ONLY storage level, the ideal scenario is that the data is fully cached in memory, and the Size on Disk should be 0 (indicating that the data is not spilled to disk). If the Size on Disk is greater than 0, it suggests that some data has been spilled to disk, which can lead to degraded performance as reading from disk is slower than reading from memory.

NEW QUESTION # 72
The data engineering team maintains the following code:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

Assuming that this code produces logically correct results and the data in the source tables has been de-duplicated and validated, which statement describes what will occur when this code is executed?

A. An incremental job will leverage information in the state store to identify unjoined rows in the source tables and write these rows to the enriched_iteinized_orders_by_account table.
B. An incremental job will detect if new rows have been written to any of the source tables; if new rows are detected, all results will be recalculated and used to overwrite the enriched_itemized_orders_by_account table.
C. A batch job will update the enriched_itemized_orders_by_account table, replacing only those rows that have different values than the current version of the table, using accountID as the primary key.
D. No computation will occur until enriched_itemized_orders_by_account is queried; upon query materialization, results will be calculated using the current valid version of data in each of the three tables referenced in the join logic.
E. The enriched_itemized_orders_by_account table will be overwritten using the current valid version of data in each of the three tables referenced in the join logic.

Answer: E

Explanation:
This is the correct answer because it describes what will occur when this code is executed. The Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from code uses three Delta Lake tables as input sources: accounts, orders, and order_items. These tables are joined together using SQL queries to create a view called new_enriched_itemized_orders_by_account, which contains information about each order item and its associated account details. Then, the code uses write.format("delta").mode("overwrite") to overwrite a target table called enriched_itemized_orders_by_account using the data from the view. This means that every time this code is executed, it will replace all existing data in the target table with new data based on the current valid version of data in each of the three input tables.

NEW QUESTION # 73
A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on task A.
If tasks A and B complete successfully but task C fails during a scheduled run, which statement describes the resulting state?

A. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until ail tasks have successfully been completed.
B. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task C failed, all commits will be rolled back automatically.
C. All logic expressed in the notebook associated with tasks A and B will have been successfully completed; any changes made in task C will be rolled back due to task failure.
D. All logic expressed in the notebook associated with tasks A and B will have been successfully completed; some operations in task C may have completed successfully.
E. All logic expressed in the notebook associated with task A will have been successfully completed; tasks B and C will not commit any changes because of stage failure.

Answer: D

Explanation:
The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV.
The resulting state after running the second command is that an external table will be created in the storage container mounted to /mnt/finance_eda_bucket with the new name prod.sales_by_store. The command will not change any data or move any files in the storage container; it will only update the table reference in the metastore and create a new Delta transaction log for the renamed table.

NEW QUESTION # 74
A large company seeks to implement a near real-time solution involving hundreds of pipelines with parallel updates of many tables with extremely high volume and high velocity data.
Which of the following solutions would you implement to achieve this requirement?

A. Isolate Delta Lake tables in their own storage containers to avoid API limits imposed by cloud vendors.
B. Store all tables in a single database to ensure that the Databricks Catalyst Metastore can load balance overall throughput.
C. Partition ingestion tables by a small time duration to allow for many data files to be written in parallel.
D. Configure Databricks to save all data to attached SSD volumes instead of object storage, increasing file I/O significantly.
E. Use Databricks High Concurrency clusters, which leverage optimized cloud storage connections to maximize data throughput.

Answer: E

Explanation:
High Concurrency clusters in Databricks are designed for multiple concurrent users and workloads. They provide fine-grained sharing of cluster resources and are optimized for operations such as running multiple parallel queries and updates. This would be suitable for a solution that involves many pipelines with parallel updates, especially with high volume and high velocity data.

NEW QUESTION # 75
Two data engineers are working on the same Databricks notebook in separate branches. Both have edited the same section of code. When one tries to merge the other's branch into their own using the Databricks Git folders UI, a merge conflict occurs on that notebook file. The UI highlights the conflict and presents options for resolution. How should the data engineers resolve this merge conflict using Databricks Git folders?

A. Delete the conflicted notebook file via the Databricks workspace UI, commit the deletion, and recreate the notebook from scratch in a new commit to bypass the conflict entirely.
B. Use the Git folders UI to manually edit the notebook file, selecting the desired lines from both versions and removing the conflict markers, then mark the conflict as resolved.
C. Abort the merge, discard all local changes, and try the merge operation again without reviewing the conflicting code.
D. Use the Git CLI in the cluster's web terminal to force-push the conflicted merge (git push -force), overriding the remote branch with the local version and discarding changes.

Answer: B

Explanation:
In the Databricks Git folders integration, when merge conflicts arise in notebooks, the UI provides a visual diff editor that highlights conflicting code segments. Users can manually choose which changes to keep from each branch, edit directly in the notebook UI, and remove conflict markers.
After resolving, the engineer must mark the conflict as resolved, save, and commit the final version.
This process ensures that both contributors' valid code segments are merged correctly and version history is maintained.
Forcing a push (C) or deleting notebooks (B) introduces data loss or versioning issues. Aborting without review (A) violates collaborative best practices. Therefore, D is the only correct and Databricks-approved way to resolve notebook merge conflicts.

NEW QUESTION # 76
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