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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q303-Q308):

NEW QUESTION # 303
A data architect has determined that a table of the following format is necessary:

Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above format regardless of whether a table already exists with this name?

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

Answer: E

NEW QUESTION # 304
A data engineer needs to create a table in Databricks using data from a CSV file at location /path/to/csv.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

A. FROM CSV
B. USING DELTA
C. USING CSV
D. None of these lines of code are needed to successfully complete the task
E. FROM "path/to/csv"

Answer: C

NEW QUESTION # 305
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. Exporting data as CSV files and emailing them
B. Using a third-party API to access the Delta table
C. Databricks-to-Databricks Sharing
D. Using Delta Sharing with the open sharing protocol

Answer: D

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 # 306
A data engineer is loading a dataset into a Delta table but expects the schema to evolve over time as new columns are added. The pipeline should automatically handle new fields without failing ingestion jobs. Which Delta Lake option should the engineer enable during the write operation?

A. mergeSchema
B. optimizeWrite
C. overwriteSchema
D. autoCompact

Answer: A

NEW QUESTION # 307
A data engineer wants to schedule their Databricks SQL dashboard to refresh every hour, but they only want the associated SQL endpoint to be running when It is necessary. The dashboard has multiple queries on multiple datasets associated with it. The data that feeds the dashboard is automatically processed using a Databricks Job.
Which approach can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

A. 0 They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
B. Q They can turn on the Auto Stop feature for the SQL endpoint.
C. O They can reduce the cluster size of the SQL endpoint.
D. O They can set up the dashboard's SQL endpoint to be serverless.

Answer: B

Explanation:
To minimize the total running time of the SQL endpoint used in the refresh schedule of a dashboard in Databricks, the most effective approach is to utilize the Auto Stop feature. This feature allows the SQL endpoint to automatically stop after a period of inactivity, ensuring that it only runs when necessary, such as during the dashboard refresh or when actively queried. This minimizes resource usage and associated costs by ensuring the SQL endpoint is not running idle outside of these operations.
Reference:
Databricks documentation on SQL endpoints: SQL Endpoints in Databricks

NEW QUESTION # 308
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