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

Section	Weight	Objectives
Topic 1: Python for Data Engineering	10-15%	- Work with Spark APIs in Python - Implement user-defined functions (UDFs) - Use PySpark for data processing
Topic 2: Apache Spark Data Processing Fundamentals	20-25%	- Apply transformations and actions on DataFrames - Use Spark SQL for data processing - Create and use Spark DataFrames - Work with structured data types (arrays, maps, structs)
Topic 3: Data Pipeline Architecture	15-20%	- Implement incremental data processing - Monitor and optimize pipeline performance - Understand ELT vs ETL patterns - Design data pipelines for batch and streaming
Topic 4: Spark SQL and DataFrames	15-20%	- Join and union DataFrames - Write and execute Spark SQL queries - Handle null values and data quality - Aggregate and group data
Topic 5: Lakehouse Platform Concepts	10-15%	- Explain data governance and security concepts - Describe key Databricks Lakehouse platform components - Understand the Lakehouse architecture and its benefits
Topic 6: Delta Lake Fundamentals	20-25%	- Write to and read from Delta tables - Understand ACID transactions and time travel - Explain Delta Lake features and benefits - Create and manage Delta tables

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q193-Q198):

NEW QUESTION # 193
A data engineer is working with two tables. Each of these tables is displayed below in its entirety.

The data engineer runs the following query to join these tables together:

Which of the following will be returned by the above query?

A. Option B
B. Option E
C. Option D
D. Option C
E. Option A

Answer: E

Explanation:
Option A is the correct answer because it shows the result of an INNER JOIN between the two tables. An INNER JOIN returns only the rows that have matching values in both tables based on the join condition. In this case, the join condition is ON a.customer_id = c.customer_id, which means that only the rows that have the same customer ID in both tables will be included in the output. The output will have four columns:
customer_id, name, account_id, and overdraft_amt. The output will have four rows, corresponding to the four customers who have accounts in the account table.
The use of INNER JOIN can be referenced from Databricks documentation on SQL JOIN or from other sources like W3Schools or GeeksforGeeks.

NEW QUESTION # 194
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The code block used by the data engineer is below:

If the data engineer only wants the query to process all of the available data in as many batches as required, which of the following lines of code should the data engineer use to fill in the blank?

A. processingTime(1)
B. trigger(processingTime="once")
C. trigger(continuous="once")
D. trigger(availableNow=True)
E. trigger(parallelBatch=True)

Answer: D

Explanation:
https://spark.apache.org/docs/latest/api/python/reference/pyspark.ss/api/pyspark.sql.streaming.DataStreamWriter.trigger.html

NEW QUESTION # 195
An engineering manager wants to monitor the performance of a recent project using a Databricks SQL query. For the first week following the project's release, the manager wants the query results to be updated every minute. However, the manager is concerned that the compute resources used for the query will be left running and cost the organization a lot of money beyond the first week of the project's release.
Which of the following approaches can the engineering team use to ensure the query does not cost the organization any money beyond the first week of the project's release?

A. They can set a limit to the number of DBUs that are consumed by the SQL Endpoint.
B. They cannot ensure the query does not cost the organization money beyond the first week of the project's release.
C. They can set a limit to the number of individuals that are able to manage the query's refresh schedule.
D. They can set the query's refresh schedule to end on a certain date in the query scheduler.
E. They can set the query's refresh schedule to end after a certain number of refreshes.

Answer: D

Explanation:
In Databricks SQL, you can use scheduled query executions to update your dashboards or enable routine alerts. By default, your queries do not have a schedule. To set the schedule, you can use the dropdown pickers to specify the frequency, period, starting time, and time zone. You can also choose to end the schedule on a certain date by selecting the End date checkbox and picking a date from the calendar. This way, you can ensure that the query does not run beyond the first week of the project's release and does not incur any additional cost. Option A is incorrect, as setting a limit to the number of DBUs does not stop the query from running. Option B is incorrect, as there is no option to end the schedule after a certain number of refreshes. Option C is incorrect, as there is a way to ensure the query does not cost the organization money beyond the first week of the project's release. Option D is incorrect, as setting a limit to the number of individuals who can manage the query's refresh schedule does not affect the query's execution or cost. Reference: Schedule a query, Schedule a query - Azure Databricks - Databricks SQL

NEW QUESTION # 196
A data engineering team needs to ingest historical CSV files from a cloud storage location that already contains 50,000 existing files. They also expect new files to arrive continuously going forward. The team wants to use Auto Loader to incrementally process both existing files and new arrivals efficiently. Which Auto Loader mode should the team configure for this use case?

A. Use directory listing mode initially to process the 50,000 existing files, then reconfigure the stream to use file notification mode for ongoing ingestion of new arrivals.
B. Use file notification mode because it scales efficiently for both large numbers of existing files and continuous new arrivals by using cloud storage events rather than repeatedly listing directories.
C. Use directory listing mode because it provides better performance for the initial processing of
50,000 existing files through optimized incremental listing capabilities.
D. Use file notification mode only after all existing files have been processed, as it cannot efficiently handle large volumes of pre-existing files.

Answer: B

Explanation:
File notification mode is faster and more scalable for large file volumes. Auto Loader performs an initial listing to discover the 50,000 existing files, then uses cloud file events to efficiently detect new arrivals without repeatedly scanning the directory. Switching modes is unnecessary.

NEW QUESTION # 197
An organization needs to share a dataset stored in its Databricks Unity Catalog with an external partner who uses a different data platform that is not Databricks. The goal is to maintain data security and ensure the partner can access the data efficiently. Which method should the data engineer use to securely share the dataset with the external partner?

A. Using Delta Sharing with the open sharing protocol
B. Exporting data as CSV files and emailing them
C. Using a third-party API to access the Delta table
D. Databricks-to-Databricks Sharing

Answer: A

Explanation:
Delta Sharing with the open sharing protocol allows secure data sharing from Unity Catalog to external partners, even if they are not using Databricks, while maintaining governance and efficient access.

NEW QUESTION # 198
......

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