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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q213-Q218):

NEW QUESTION # 213
A Structured Streaming job is stopped and restarted after a code change. The engineer wants the job to resume from where it left off rather than reprocessing all source data.
Which configuration is required?

A. Specifying a stable checkpointLocation on the write stream
B. Setting maxFilesPerTrigger to 1
C. Setting outputMode to complete
D. Enabling mergeSchema on the target table

Answer: A

Explanation:
The checkpoint location stores stream progress and offset information, allowing the query to resume exactly where it stopped after a restart or failure. Each streaming query must have its own dedicated checkpoint directory; reusing or deleting it causes reprocessing or incorrect results.

NEW QUESTION # 214
Which of the following describes a benefit of creating an external table from Parquet rather than CSV when using a CREATE TABLE AS SELECT statement?

A. Parquet files will become Delta tables
B. Parquet files have the ability to be optimized
C. Parquet files can be partitioned
D. Parquet files have a well-defined schema
E. CREATE TABLE AS SELECT statements cannot be used on files

Answer: D

Explanation:
Option C is the correct answer because Parquet files have a well-defined schema that is embedded within the data itself. This means that the data types and column names of the Parquet files are automatically detected and preserved when creating an external table from them. This also enables the use of SQL and other structured query languages to access and analyze the data. CSV files, on the other hand, do not have a schema embedded in them, and require specifying the schema explicitly or inferring it from the data when creating an external table from them. This can lead to errors or inconsistencies in the data types and column names, and also increase the processing time and complexity.

NEW QUESTION # 215
A data engineer is maintaining an ETL pipeline code with a GitHub repository linked to their Databricks account. The data engineer wants to deploy the ETL pipeline to production as a databricks workflow. Which approach should the data engineer use?

A. Maintain workflow_config.json and deploy it using Databricks CLI
B. Maintain workflow_config.json and deploy it using Terraform
C. Manually create and manage the workflow in UI
D. Databricks Asset Bundles (DAB) + GitHub Integration

Answer: D

Explanation:
The best practice for deploying ETL pipelines to production is to use Databricks Asset Bundles (DAB) with GitHub integration. DAB provides a declarative YAML-based structure for defining workflows and resources, supports CI/CD, and integrates seamlessly with GitHub, enabling version-controlled, automated deployments to production.

NEW QUESTION # 216
A data engineer needs to parse only png files in a directory that contains files with different suffixes. Which code should the data engineer use to achieve this task?

Answer: D

NEW QUESTION # 217
What is the primary function of the Silver layer in the Databricks medallion architecture?

A. Aggregate and enrich data for business analytics
B. Ingest raw data in its original state
C. Validate, clean, and deduplicate data for further processing
D. Store historical data solely for auditing purposes

Answer: C

Explanation:
The Silver layer in the Databricks Medallion Architecture is responsible for validating, cleaning, and deduplicating raw Bronze data. This prepares data in a structured and reliable form, making it ready for downstream enrichment and analytics in the Gold layer.

NEW QUESTION # 218
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