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

Section	Weight	Objectives
Data Governance & Quality	11%	- Data quality and reliability 1. Data validation and quality checks 2. Schema enforcement and evolution - Unity Catalog implementation 1. Data governance model 2. Permissions and access control
Productionizing Data Pipelines	18%	- Deployment and CI/CD 1. Databricks Asset Bundles 2. Version control integration - Monitoring and troubleshooting 1. Pipeline reliability and recovery 2. Logging and error handling - Workflow orchestration 1. Scheduling, triggers, and dependencies 2. Lakeflow Jobs creation and management
Development and Ingestion	30%	- Data ingestion patterns and methods 1. Connecting to external data sources 2. Delta Lake basics and usage 3. Batch and streaming ingestion - Notebook development fundamentals 1. Using PySpark and Spark SQL 2. Data exploration and validation
Data Processing & Transformations	31%	- Query optimization and performance 1. Optimization strategies 2. Understanding query plans - Data transformation techniques 1. DataFrame operations and transformations 2. Aggregations, joins, and window functions 3. Complex data processing logic
Databricks Intelligence Platform	10%	- Platform architecture and core concepts 1. Compute options: clusters, SQL warehouses, serverless 2. Workspace navigation and management 3. Data layout and optimization: partitioning, file sizing, caching

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q215-Q220):

NEW QUESTION # 215
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. trigger(parallelBatch=True)
B. processingTime(1)
C. trigger(processingTime="once")
D. trigger(availableNow=True)
E. trigger(continuous="once")

Answer: D

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

NEW QUESTION # 216
A new data engineering team team. has been assigned to an ELT project. The new data engineering team will need full privileges on the database customers to fully manage the project.
Which of the following commands can be used to grant full permissions on the database to the new data engineering team?

A. GRANT SELECT CREATE MODIFY USAGE PRIVILEGES ON DATABASE customers TO team;
B. GRANT SELECT PRIVILEGES ON DATABASE customers TO teams;
C. GRANT USAGE ON DATABASE customers TO team;
D. GRANT ALL PRIVILEGES ON DATABASE customers TO team;
E. GRANT ALL PRIVILEGES ON DATABASE team TO customers;

Answer: D

Explanation:
Explanation
To grant full privileges on the database "customers" to the new data engineering team, you can use the GRANT ALL PRIVILEGES command as shown in option E. This command provides the team with all possible privileges on the specified database, allowing them to fully manage it.

NEW QUESTION # 217
A data engineer has left the organization. The data team needs to transfer ownership of the data engineer's Delta tables to a new data engineer. The new data engineer is the lead engineer on the data team.
Assuming the original data engineer no longer has access, which of the following individuals must be the one to transfer ownership of the Delta tables in Data Explorer?

A. This transfer is not possible
B. Original data engineer
C. New lead data engineer
D. Workspace administrator
E. Databricks account representative

Answer: D

Explanation:
Explanation
https://docs.databricks.com/sql/admin/transfer-ownership.html

NEW QUESTION # 218
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

A. Replace "transactions" with the path to the location of the Delta table
B. Replace schema(schema) with option ("maxFilesPerTrigger", 1)
C. Replace format("delta") with format("stream")
D. Replace predict with a stream-friendly prediction function
E. Replace spark.read with spark.readStream

Answer: E

Explanation:
To read from a stream source, the data engineer needs to use the spark.readStream method instead of the spark.
read method. The spark.readStream method returns a DataStreamReader object that can be used to specify the details of the input source, such as the format, the schema, the path, and the options. The spark.read method is only suitable for batch processing, not streaming processing. The other changes are not necessary or correct for reading from a stream source. References: Structured Streaming Programming Guide, Read a stream, Databricks Data Sources

NEW QUESTION # 219
A data engineer and data analyst are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL. The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables. Which change will need to be made to the pipeline when migrating to Delta Live Tables?

A. The pipeline will need to be written entirely in SQL.
B. The pipeline can have different notebook sources in SQL & Python.
C. The pipeline will need to use a batch source in place of a streaming source.
D. The pipeline will need to be written entirely in Python.

Answer: B

Explanation:
When migrating to Delta Live Tables (DLT), you can use both SQL and Python in the same pipeline. DLT supports multi-language notebooks, allowing data engineers to continue using Python for certain transformations and data analysts to use SQL for their specific requirements.

NEW QUESTION # 220
......

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