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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q300-Q305):

NEW QUESTION # 300
A production ETL notebook runs once nightly and currently executes on a shared all-purpose cluster that stays running 24 hours a day.
Which change reduces cost while preserving the schedule?

A. Configure the scheduled job to use a job cluster that is created at run start and terminated at run end.
B. Convert the notebook to a global temp view refreshed on demand.
C. Increase the all-purpose cluster's minimum worker count to speed up the run.
D. Move the notebook to a SQL warehouse with auto-stop disabled.

Answer: A

Explanation:
Job clusters are provisioned when the job run starts and terminated when it completes, so compute is billed only for the run duration and at the lower Jobs Compute rate. All-purpose clusters left running continuously are billed at a higher rate even when idle.

NEW QUESTION # 301
A data engineering team uses Declarative Automation Bundles, formerly known as Databricks Asset Bundles, to deploy the same codebase across development, test, and production environments. The team wants environment-specific behavior to be applied only through bundle configuration, without modifying notebooks, job definitions, or deployment logic during promotion.
Which approach applies environment-specific configuration while meeting this requirement?

A. Parameterize environment-specific values inside notebooks and set those parameters whenever each job run is triggered.
B. Deploy the same bundle once and edit job settings in the workspace UI for each environment after deployment.
C. Define bundle variables and reference them from bundle resources, and then override the variable values for each environment using bundle targets.
D. Use separate Git branches for each environment so each branch can contain environment-specific configuration values.

Answer: C

NEW QUESTION # 302
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 cade block used by the data engineer is below:

If the data engineer only wants the query to execute a micro-batch to process data every 5 seconds, which of the following lines of code should the data engineer use to fill in the blank?

A. trigger(once="5 seconds")
B. trigger()
C. trigger("5 seconds")
D. trigger(continuous="5 seconds")
E. trigger(processingTime="5 seconds")

Answer: E

Explanation:
The processingTime option specifies a time-based trigger interval for fixed interval micro-batches. This means that the query will execute a micro-batch to process data every 5 seconds, regardless of how much data is available. This option is suitable for near-real time processing workloads that require low latency and consistent processing frequency. The other options are either invalid syntax (A, C), default behavior (B), or experimental feature (E). Reference: Databricks Documentation - Configure Structured Streaming trigger intervals, Databricks Documentation - Trigger.

NEW QUESTION # 303
A data engineer needs to read files from cloud object storage into a Spark DataFrame in Databricks. The files are stored in CSV format with headers and comma delimiters. Which Spark DataFrame reader option ensures that column names are correctly inferred from the first row?

A. mode
B. delimiter
C. header
D. inferSchema

Answer: C

NEW QUESTION # 304
A data engineer needs to find all customers who have never placed an order. The customers table contains customer_id, customer_name, and other customer attributes. The orders table contains order_id, customer_id, and other order attributes. Both tables share customer_id as the join key. The result must include only customer columns with no order-related columns in the output. Which expression exactly achieves this requirement?

A. Left outer join from orders to customers
B. Full outer join, then filter where order_id IS NULL
C. Left anti-join from customers to orders
D. Inner join, then filter for NULL order_id

Answer: C

Explanation:
A left anti-join returns only rows from customers that have no matching customer_id in orders, and it outputs only columns from the left-side customers table.

NEW QUESTION # 305
......

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