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

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

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q183-Q188):

NEW QUESTION # 183
A Databricks SQL dashboard has been configured to monitor the total number of records present in a collection of Delta Lake tables using the following query pattern:
SELECT COUNT (*) FROM table
Which of the following describes how results are generated each time the dashboard is updated?

A. The total count of records is calculated from the Delta transaction logs
B. The total count of rows will be returned from cached results unless REFRESH is run
C. The total count of records is calculated from the Hive metastore
D. The total count of records is calculated from the parquet file metadata
E. The total count of rows is calculated by scanning all data files

Answer: A

Explanation:
Delta Lake maintains a transaction log that records details about every change made to a table.
When you execute a count operation on a Delta table, Delta Lake can use the information in the transaction log to calculate the total number of records without having to scan all the data files.
This is because the transaction log includes information about the number of records in each file, allowing for an efficient aggregation of these counts to get the total number of records in the table.

NEW QUESTION # 184
A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

A. Databricks supports Spark SQL and JDBC; all logic can be directly migrated from the source system without refactoring.
B. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.
C. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
D. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.
E. All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.

Answer: E

NEW QUESTION # 185
A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI.
Upon reviewing their work, the engineer realizes that they are listed as the "Owner" for each job.
They attempt to transfer "Owner" privileges to the "DevOps" group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?

A. Other than the default "admins" group, only individual users can be granted privileges on jobs.
B. Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.
C. The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.
D. Only workspace administrators can grant "Owner" privileges to a group.
E. A user can only transfer job ownership to a group if they are also a member of that group.

Answer: B

Explanation:
A job cannot have more than one owner. A job cannot have a group as an owner.

NEW QUESTION # 186
A view is registered with the following code:

Both users and orders are Delta Lake tables.
Which statement describes the results of querying recent_orders?

A. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
B. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
C. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
D. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.

Answer: D

NEW QUESTION # 187
A member of the data engineering team has submitted a short notebook that they wish to schedule as part of a larger data pipeline. Assume that the commands provided below produce the logically correct results when run as presented.
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

Which command should be removed from the notebook before scheduling it as a job?

A. Cmd 5
B. Cmd 6
C. Cmd 4
D. Cmd 2
E. Cmd 3

Answer: B

Explanation:
When scheduling a Databricks notebook as a job, it's generally recommended to remove or modify commands that involve displaying output, such as using the display() function. Displaying data using display() is an interactive feature designed for exploration and visualization within the notebook interface and may not work well in a production job context.
The finalDF.explain() command, which provides the execution plan of the DataFrame transformations and actions, is often useful for debugging and optimizing queries. While it doesn't Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from display interactive visualizations like display(), it can still be informative for understanding how Spark is executing the operations on your DataFrame.

NEW QUESTION # 188
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