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

SectionWeightObjectives
Topic 1: Delta Lake Fundamentals20-25%- 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 2: Python for Data Engineering10-15%- Work with Spark APIs in Python
- Use PySpark for data processing
- Implement user-defined functions (UDFs)
Topic 3: Spark SQL and DataFrames15-20%- Write and execute Spark SQL queries
- Handle null values and data quality
- Aggregate and group data
- Join and union DataFrames
Topic 4: Lakehouse Platform Concepts10-15%- Understand the Lakehouse architecture and its benefits
- Describe key Databricks Lakehouse platform components
- Explain data governance and security concepts
Topic 5: Data Pipeline Architecture15-20%- Design data pipelines for batch and streaming
- Implement incremental data processing
- Monitor and optimize pipeline performance
- Understand ELT vs ETL patterns
Topic 6: Apache Spark Data Processing Fundamentals20-25%- Create and use Spark DataFrames
- Apply transformations and actions on DataFrames
- Use Spark SQL for data processing
- Work with structured data types (arrays, maps, structs)

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q295-Q300):

NEW QUESTION # 295
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?

Answer: B

Explanation:
The best practice is to use "Complete" as output mode instead of "append" when working with aggregated tables. Since gold layer is work final aggregated tables, the only option with output mode as complete is option


NEW QUESTION # 296
A data engineering project involves processing large batches of data on a daily schedule using ETL. The jobs are resource-intensive and vary in size, requiring a scalable, cost-efficient compute solution that can automatically scale based on the workload. Which compute approach will satisfy the needs described?

Answer: B

Explanation:
Job Clusters are ephemeral clusters created for scheduled or automated workloads. They are cost-efficient, scale automatically to match workload demands, and terminate after job completion, making them the ideal choice for daily large-batch ETL processing.


NEW QUESTION # 297
A data engineer is attempting to write Python and SQL in the same command cell and is running into an error The engineer thought that it was possible to use a Python variable in a select statement.
Why does the command fail?

Answer: B


NEW QUESTION # 298
Which file format is used for storing Delta Lake Table?

Answer: C

Explanation:
Delta Lake tables use the Parquet format as their underlying storage format. Delta Lake enhances Parquet by adding a transaction log that keeps track of all the operations performed on the table. This allows features like ACID transactions, scalable metadata handling, and schema enforcement, making it an ideal choice for big data processing and management in environments like Databricks.
References:Databricks documentation on Delta Lake: Delta Lake Overview


NEW QUESTION # 299
A data engineer needs to apply custom logic to string column city in table stores for a specific use case. In order to apply this custom logic at scale, the data engineer wants to create a SQL user-defined function (UDF).
Which of the following code blocks creates this SQL UDF?

Answer: C

Explanation:
https://www.databricks.com/blog/2021/10/20/introducing-sql-user-defined-functions.html


NEW QUESTION # 300
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