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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q149-Q154):

NEW QUESTION # 149
A Data Engineer is building a simple data pipeline using Delta Live Tables (DLT) in Databricksto ingest customer data. The raw customer data is stored in a cloud storage location in JSON format. The task is to create a DLT pipeline that reads the rawJSON data and writes it into a Delta table for further processing.
Which code snippet will correctly ingest the raw JSON data and create a Delta table using DLT?

Answer: C

NEW QUESTION # 150
A data engineer that is new to using Python needs to create a Python function to add two integers together and return the sum?
Which of the following code blocks can the data engineer use to complete this task?

Answer: D

Explanation:
Explanation
https://www.w3schools.com/python/python_functions.asp

NEW QUESTION # 151
A data engineer is loading a dataset into a Delta table but expects the schema to evolve over time as new columns are added. The pipeline should automatically handle new fields without failing ingestion jobs. Which Delta Lake option should the engineer enable during the write operation?

A. overwriteSchema
B. mergeSchema
C. optimizeWrite
D. autoCompact

Answer: B

NEW QUESTION # 152
A single Job runs two notebooks as two separate tasks. A data engineer has noticed that one of the notebooks is running slowly in the Job's current run. The data engineer asks a tech lead for help in identifying why this might be the case.
Which of the following approaches can the tech lead use to identify why the notebook is running slowly as part of the Job?

A. They can navigate to the Tasks tab in the Jobs UI and click on the active run to review the processing notebook.
B. There is no way to determine why a Job task is running slowly.
C. They can navigate to the Runs tab in the Jobs UI to immediately review the processing notebook.
D. They can navigate to the Runs tab in the Jobs UI and click on the active run to review the processing notebook.
E. They can navigate to the Tasks tab in the Jobs UI to immediately review the processing notebook.

Answer: D

NEW QUESTION # 153
A team creates YAML manifests that declare jobs, resources, and dependencies, then deploys them to Databricks using the Databricks CLI. The deployment succeeds.
Which feature are they using?

A. Terraform
B. GitHub
C. Databricks Asset Bundles
D. DataOps

Answer: C

Explanation:
Databricks Asset Bundles are a Databricks CLI feature that lets teams define Databricks projects "as code" using YAML configuration files. In a bundle, metadata is expressed in YAML (for example, a required databricks.yml at the root), and the configuration can declare resources (such as Databricks Jobs/workflows and other supported assets), artifacts, and deployment targets. After defining these YAML manifests, teams use the Databricks CLI to validate, deploy, and run the bundle into a Databricks workspace, which matches the scenario described (YAML + CLI deployment). This approach standardizes how workflows and related resources are packaged and promoted across environments (dev/test/prod) while keeping definitions version-controlled and reproducible. GitHub is only a hosting/version-control platform (not the YAML deployment feature itself), Terraform is a separate IaC tool, and "DataOps" is a methodology rather than a specific Databricks feature. (Databricks Documentation)

NEW QUESTION # 154
......

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