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

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

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q109-Q114):

NEW QUESTION # 109
The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.
MERGE INTO customers
USING (
SELECT updates.customer_id as merge_ey, updates .*
FROM updates
UNION ALL
SELECT NULL as merge_key, updates .*
FROM updates JOIN customers
ON updates.customer_id = customers.customer_id
WHERE customers.current = true AND updates.address <> customers.address ) staged_updates ON customers.customer_id = mergekey WHEN MATCHED AND customers. current = true AND customers.address <> staged_updates.address THEN UPDATE SET current = false, end_date = staged_updates.effective_date WHEN NOT MATCHED THEN INSERT (customer_id, address, current, effective_date, end_date) VALUES (staged_updates.customer_id, staged_updates.address, true, staged_updates.effective_date, null) Which statement describes this implementation?

A. The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
B. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
C. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
D. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.

Answer: A

Explanation:
The provided MERGE statement is a classic implementation of a Type 2 SCD in a data warehousing context. In this approach, historical data is preserved by keeping old records (marking them as not current) and adding new records for changes. Specifically, when a match is found and there's a change in the address, the existing record in the customers table is updated to mark it as no longer current (current = false), and an end date is assigned (end_date = staged_updates.effective_date). A new record for the customer is then inserted with the updated information, marked as current. This method ensures that the full history of changes to customer information is maintained in the table, allowing for time-based analysis of customer data.

NEW QUESTION # 110
A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

A. Task queueing resulting from improper thread pool assignment.
B. Spill resulting from attached volume storage being too small.
C. Network latency due to some cluster nodes being in different regions from the source data
D. Credential validation errors while pulling data from an external system.
E. Skew caused by more data being assigned to a subset of spark-partitions.

Answer: E

Explanation:
This is the correct answer because skew is a common situation that causes increased duration of the overall job. Skew occurs when some partitions have more data than others, resulting in uneven distribution of work among tasks and executors. Skew can be caused by various factors, such as skewed data distribution, improper partitioning strategy, or join operations with skewed keys. Skew can lead to performance issues such as long-running tasks, wasted resources, or even task failures due to memory or disk spills.

NEW QUESTION # 111
A data architect has heard about lake's built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability. Which piece of information is critical to this decision?

A. Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.
B. Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.
C. Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires setting multiple fields in a single update.
D. Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.
E. Delta Lake only supports Type 0 tables; once records are inserted to a Delta Lake table, they cannot be modified.

Answer: B

Explanation:
Delta Lake's time travel feature allows users to access previous versions of a table, providing a powerful tool for auditing and versioning. However, using time travel as a long-term versioning solution for auditing purposes can be less optimal in terms of cost and performance, especially as the volume of data and the number of versions grow. For maintaining a full history of valid street addresses as they appear in a customers table, using a Type 2 table (where each update creates a new record with versioning) might provide better scalability and performance by avoiding the overhead associated with accessing older versions of a large table. While Type 1 tables, where existing records are overwritten with new values, seem simpler and can leverage time travel for auditing, the critical piece of information is that time travel might not scale well in cost or latency for long-term versioning needs, making a Type 2 approach more viable for performance and scalability.

NEW QUESTION # 112
Two of the most common data locations on Databricks are the DBFS root storage and external object storage mounted with dbutils.fs.mount().
Which of the following statements is correct?

A. By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.
B. The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.
C. Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.
D. DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems.
E. The DBFS root stores files in ephemeral block volumes attached to the driver, while mounted directories will always persist saved data to external storage between sessions.

Answer: D

Explanation:
DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems. DBFS is not a physical file system, but a layer over the object storage that provides a unified view of data across different data sources. By default, the DBFS root is accessible to all users in the workspace, and the access to mounted data sources depends on the permissions of the storage account or container. Mounted storage volumes do not need to have full public read and write permissions, but they do require a valid connection string or access key to be provided when mounting. Both the DBFS root and mounted storage can be accessed when using %sh in a Databricks notebook, as long as the cluster has FUSE enabled. The DBFS root does not store files in ephemeral block volumes attached to the driver, but in the object storage associated with the workspace. Mounted directories will persist saved data to external storage between sessions, unless they are unmounted or deleted.

NEW QUESTION # 113
The Databricks CLI is use to trigger a run of an existing job by passing the job_id parameter. The response that the job run request has been submitted successfully includes a filed run_id.
Which statement describes what the number alongside this field represents?

A. The globally unique ID of the newly triggered run.
B. The total number of jobs that have been run in the workspace.
C. The job_id and number of times the job has been are concatenated and returned.
D. The job_id is returned in this field.
E. The number of times the job definition has been run in the workspace.

Answer: A

Explanation:
When triggering a job run using the Databricks CLI, the run_id field in the response represents a globally unique identifier for that particular run of the job. This run_id is distinct from the job_id.
While the job_id identifies the job definition and is constant across all runs of that job, the run_id is unique to each execution and is used to track and query the status of that specific job run within the Databricks environment. This distinction allows users to manage and reference individual executions of a job directly.

NEW QUESTION # 114
......

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