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

Section	Weight	Objectives
Delta Lake	20-25%	- Delta Lake operations 1. Delta Live Tables 2. Merge, update, delete operations 3. Schema evolution and enforcement - Delta Lake fundamentals 1. Optimize and Z-order 2. ACID transactions 3. Time travel and data versioning
Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Jobs and job scheduling 2. Monitoring and alerting 3. Task dependencies and orchestration
Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Data governance fundamentals 2. Bronze, silver, gold data layers 3. Differences between data lake, data warehouse, and lakehouse
Data Processing with Spark	25-30%	- Python and SQL for data engineering 1. Performance optimization techniques 2. Built-in and user-defined functions 3. Spark APIs in Python - Spark DataFrames and Spark SQL 1. Window functions 2. Spark SQL queries and functions 3. DataFrame operations and transformations
Data Ingestion	15-20%	- Batch ingestion methods 1. DBR autoloader 2. Integration with external systems 3. Spark APIs for ingestion - Streaming ingestion 1. Kafka integration 2. Structured streaming fundamentals

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q90-Q95):

NEW QUESTION # 90
A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company's data is stored in regional cloud storage in the United States.
The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed.
Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?

A. Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be made based upon what is most convenient for the workspace administrator.
B. Databricks leverages user workstations as the driver during interactive development; as such, users should always use a workspace deployed in a region they are physically near.
C. Databricks notebooks send all executable code from the user's browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.
D. Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored.
E. Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.

Answer: D

Explanation:
This is the correct answer because it accurately informs this decision. The decision is about where the Databricks workspace used by the contractors should be deployed. The contractors are based in India, while all the company's data is stored in regional cloud storage in the United States. When choosing a region for deploying a Databricks workspace, one of the important factors to consider is the proximity to the data sources and sinks. Cross-region reads and writes can incur significant costs and latency due to network bandwidth and data transfer fees. Therefore, whenever possible, compute should be deployed in the same region the data is stored to optimize performance and reduce costs. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Workspace" section; Databricks Documentation, under
"Choose a region" section.

NEW QUESTION # 91
A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?

A. Databricks has autotuned to a smaller target file size based on the overall size of data in the table
B. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
C. Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations
D. Databricks has autotuned to a smaller target file size based on the amount of data in each partition

Answer: C

Explanation:
This is the correct answer because Databricks has a feature called Auto Optimize, which automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones and sorting data within each file by a specified column. However, Auto Optimize also considers the trade-off between file size and merge performance, and may choose a smaller target file size to reduce the duration of merge operations, especially for streaming workloads that frequently update existing records. Therefore, it is possible that Auto Optimize has autotuned to a smaller target file size based on the characteristics of the streaming production job. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Auto Optimize" section. https://docs.databricks.com/en/delta/tune-file-size.html#autotune-table 'Autotune file size based on workload'

NEW QUESTION # 92
You are currently working on reloading customer_sales tables using the below query
1. INSERT OVERWRITE customer_sales
2. SELECT * FROM customers c
3. INNER JOIN sales_monthly s on s.customer_id = c.customer_id
After you ran the above command, the Marketing team quickly wanted to review the old data that was in the table. How does INSERT OVERWRITE impact the data in the customer_sales table if you want to see the previous version of the data prior to running the above statement?

A. Overwrites the data in the table, all historical versions of the data, you can not time travel to previous versions
B. Appends the data to the current version, you can time travel to previous versions
C. Overwrites the data in the table but preserves all historical versions of the data, you can time travel to previous versions
D. By default, overwrites the data and schema, you cannot perform time travel
E. Overwrites the current version of the data but clears all historical versions of the data, so you can not time travel to previous versions.

Answer: C

Explanation:
Explanation
The answer is, INSERT OVERWRITE Overwrites the current version of the data but preserves all historical versions of the data, you can time travel to previous versions.
1.INSERT OVERWRITE customer_sales
2.SELECT * FROM customers c
3.INNER JOIN sales s on s.customer_id = c.customer_id
Let's just assume that this is the second time you are running the above statement, you can still query the prior version of the data using time travel, and any DML/DDL except DROP TABLE creates new PARQUET files so you can still access the previous versions of data.
SQL Syntax for Time travel
SELECT * FROM table_name as of [version number]
with customer_sales example
SELECT * FROM customer_sales as of 1 -- previous version
SELECT * FROM customer_sales as of 2 -- current version
You see all historical changes on the table using DESCRIBE HISTORY table_name Note: the main difference between INSERT OVERWRITE and CREATE OR REPLACE TABLE(CRAS) is that CRAS can modify the schema of the table, i.e it can add new columns or change data types of existing columns. By default INSERT OVERWRITE only overwrites the data.
INSERT OVERWRITE can also be used to update the schema when
spark.databricks.delta.schema.autoMerge.enabled is set true if this option is not enabled and if there is a schema mismatch command INSERT OVERWRITEwill fail.
Any DML/DDL operation(except DROP TABLE) on the Delta table preserves the historical ver-sion of the data.

NEW QUESTION # 93
A data engineer wants to enforce the principle of least privilege when configuring ACLs for Databricks jobs in a collaborative workspace.
Which approach should the data engineer use?

A. Grant all users CAN MANAGE permission on all jobs to avoid access issues.
B. Grant CAN RUN permission to everyone and CAN MANAGE to a single admin group.
C. Assign users only the minimum permission level (e.g., CAN RUN or CAN VIEW) required for their role on each job.
D. Use only folder-level permissions and avoid setting permissions on individual jobs.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks follows the principle of least privilege (PoLP) to ensure users and groups have only the permissions required for their specific role.
For Databricks Jobs, three primary permission levels exist:
CAN VIEW - Allows viewing job configurations and runs.
CAN RUN - Allows triggering job runs.
CAN MANAGE - Allows full control, including editing and deletion.
To enforce PoLP, each user should be assigned only the specific permission required for their task. This approach prevents unauthorized edits and limits the risk of accidental deletions or job misconfigurations.
Granting broad permissions such as "ALL" or "CAN MANAGE to everyone" violates Databricks security best practices. Thus, the correct answer is D.

NEW QUESTION # 94
The data engineering team has configured a job to process customer requests to be forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta Lake tables using default table settings.
The team has decided to process all deletions from the previous week as a batch job at 1am each Sunday. The total duration of this job is less than one hour. Every Monday at 3am, a batch job executes a series ofVACUUMcommands on all Delta Lake tables throughout the organization.
The compliance officer has recently learned about Delta Lake's time travel functionality. They are concerned that this might allow continued access to deleted data.
Assuming all delete logic is correctly implemented, which statement correctly addresses this concern?

A. Because the default data retention threshold is 7 days, data files containing deleted records will be retained until the vacuum job is run 8 days later.
B. Because the vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours.
C. Because the default data retention threshold is 24 hours, data files containing deleted records will be retained until the vacuum job is run the following day.
D. Because Delta Lake time travel provides full access to the entire history of a table, deleted records can always be recreated by users with full admin privileges.
E. Because Delta Lake's delete statements have ACID guarantees, deleted records will be permanently purged from all storage systems as soon as a delete job completes.

Answer: A

Explanation:
https://learn.microsoft.com/en-us/azure/databricks/delta/vacuum

NEW QUESTION # 95
......

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