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

SectionWeightObjectives
Python for Data Engineering10-15%- Use PySpark for data processing
- Implement user-defined functions (UDFs)
- Work with Spark APIs in Python
Apache Spark Data Processing Fundamentals20-25%- Create and use Spark DataFrames
- Apply transformations and actions on DataFrames
- Work with structured data types (arrays, maps, structs)
- Use Spark SQL for data processing
Delta Lake Fundamentals20-25%- Write to and read from Delta tables
- Explain Delta Lake features and benefits
- Create and manage Delta tables
- Understand ACID transactions and time travel
Lakehouse Platform Concepts10-15%- Describe key Databricks Lakehouse platform components
- Understand the Lakehouse architecture and its benefits
- Explain data governance and security concepts
Spark SQL and DataFrames15-20%- Aggregate and group data
- Write and execute Spark SQL queries
- Join and union DataFrames
- Handle null values and data quality
Data Pipeline Architecture15-20%- Monitor and optimize pipeline performance
- Design data pipelines for batch and streaming
- Understand ELT vs ETL patterns
- Implement incremental data processing

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q124-Q129):

NEW QUESTION # 124
A data engineer must fully qualify a table named orders that lives in the retail schema within the prod catalog.
Which reference is correct in Unity Catalog?

Answer: A

Explanation:
Unity Catalog uses a three-level namespace in the order catalog.schema.table, so the correct reference is prod.retail.orders. Two-level names resolve against the current catalog, which can produce unexpected results when the session default differs.


NEW QUESTION # 125
A data engineer needs to audit who made changes to a Delta table in the last 7 days. Which SQL statement returns the record of past operations, including timestamps, users, and operation types?

Answer: A

Explanation:
DESCRIBE HISTORY my_table returns the Delta table's operation history, including timestamps, operation types, and user information. The results can then be filtered to the last seven days.


NEW QUESTION # 126
A data engineer has joined an existing project and they see the following query in the project repository:
CREATE STREAMING LIVE TABLE loyal_customers AS
SELECT customer_id -
FROM STREAM(LIVE.customers)
WHERE loyalty_level = 'high';
Which of the following describes why the STREAM function is included in the query?

Answer: C

Explanation:
The STREAM function is used to process data from a streaming live table or view, which is a table or view that contains data that has been added only since the last pipeline update. Streaming live tables and views are stateful, meaning that they retain the state of the previous pipeline run and only process new data based on the current query. This is useful for incremental processing of streaming or batch data sources. The customers table in the query is a streaming live table, which means that it contains the latest data from the source. The STREAM function enables the query to read the data from the customers table incrementally and create another streaming live table named loyal_customers, which contains the customer IDs of the customers with high loyalty level. Reference: Difference between LIVE TABLE and STREAMING LIVE TABLE, CREATE STREAMING TABLE, Load data using streaming tables in Databricks SQL.


NEW QUESTION # 127
A data engineer works for an organization that must meet a stringent Service Level Agreement (SLA) that demands minimal runtime errors and high availability for its data processing pipelines. The data engineer wants to avoid the operational overhead of managing and tuning clusters.
Which architectural solution will meet the requirements?

Answer: B


NEW QUESTION # 128
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?

Answer: A

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 # 129
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