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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q27-Q32):

NEW QUESTION # 27
A data engineer is onboarding a new Bronze ingestion pipeline in Databricks with Unity Catalog. The team wants Databricks to handle storage layout, apply platform optimizations over time, and simplify lifecycle management so that when a table is dropped, its underlying data is also cleaned up according to Databricks- managed retention policies.
Which table type should the data engineer create for these ingestion tables?

Answer: B


NEW QUESTION # 28
Which of the following describes the relationship between Gold tables and Silver tables?

Answer: A

Explanation:
According to the medallion lakehouse architecture, gold tables are the final layer of data that powers analytics, machine learning, and production applications. They are often highly refined and aggregated, containing data that has been transformed into knowledge, rather than just information. Silver tables, on the other hand, are the intermediate layer of data that represents a validated, enriched version of the raw data from the bronze layer.
They provide an enterprise view of all its key business entities, concepts and transactions, but they may not have all the aggregations and calculations that are required for specific use cases. Therefore, gold tables are more likely to contain aggregations than silver tables. References:
* What is the medallion lakehouse architecture?
* What is a Medallion Architecture?


NEW QUESTION # 29
Which of the following data lakehouse features results in improved data quality over a traditional data lake?

Answer: B

Explanation:
Explanation
One of the key features of a data lakehouse that results in improved data quality over a traditional data lake is its support for ACID (Atomicity, Consistency, Isolation, Durability) transactions. ACID transactions provide data integrity and consistency guarantees, ensuring that operations on the data are reliable and that data is not left in an inconsistent state due to failures or concurrent access. In a traditional data lake, such transactional guarantees are often lacking, making it challenging to maintain data quality, especially in scenarios involving multiple data writes, updates, or complex transformations. A data lakehouse, by offering ACID compliance, helps maintain data quality by providing strong consistency and reliability, which is crucial for data pipelines and analytics.


NEW QUESTION # 30
A data engineer needs to optimize the data layout and query performance for an e-commerce transactions Delta table. The table is partitioned by "purchase_date" a date column which helps with time-based queries but does not optimize searches on user statistics "customer_id", a high- cardinality column.
The table is usually queried with filters on "customer_id" within specific date ranges, but since this data is spread across multiple files in each partition, it results in full partition scans and increased runtime and costs.
How should the data engineer optimize the Data Layout for efficient reads?

Answer: C

Explanation:
Since queries filter by customer_id within purchase_date ranges, partitioning only by purchase_date causes inefficient scans across partitions. The correct optimization is to use Liquid Clustering on both customer_id and purchase_date, which dynamically reorganizes data to optimize for both dimensions and avoids the drawbacks of high-cardinality static partitioning.


NEW QUESTION # 31
A data engineer has a Delta table containing old data files that are no longer referenced after several update and delete operations. To remove these unused files and reclaim storage space, which Delta Lake command should be executed?

Answer: B


NEW QUESTION # 32
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