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

Section	Weight	Objectives
Topic 1: Productionizing Data Pipelines	18%	- Deployment and CI/CD 1. Databricks Asset Bundles 2. Version control integration - Workflow orchestration 1. Scheduling, triggers, and dependencies 2. Lakeflow Jobs creation and management - Monitoring and troubleshooting 1. Pipeline reliability and recovery 2. Logging and error handling
Topic 2: Data Governance & Quality	11%	- Data quality and reliability 1. Data validation and quality checks 2. Schema enforcement and evolution - Unity Catalog implementation 1. Data governance model 2. Permissions and access control
Topic 3: Data Processing & Transformations	31%	- Data transformation techniques 1. Complex data processing logic 2. Aggregations, joins, and window functions 3. DataFrame operations and transformations - Query optimization and performance 1. Understanding query plans 2. Optimization strategies
Topic 4: Databricks Intelligence Platform	10%	- Platform architecture and core concepts 1. Compute options: clusters, SQL warehouses, serverless 2. Data layout and optimization: partitioning, file sizing, caching 3. Workspace navigation and management
Topic 5: Development and Ingestion	30%	- Notebook development fundamentals 1. Using PySpark and Spark SQL 2. Data exploration and validation - Data ingestion patterns and methods 1. Connecting to external data sources 2. Batch and streaming ingestion 3. Delta Lake basics and usage

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

NEW QUESTION # 27
A data engineer needs to ingest JSON change data from Salesforce into Unity Catalog-governed Delta tables with a low-code, fully managed experience. Which Databricks capability should the data engineer use?

A. Auto Loader reading JSON files from an S3 bucket exported by Salesforce
B. A Lakeflow Connect managed Salesforce connector that writes into Unity Catalog Delta tables
C. Use the Salesforce REST API in a PySpark notebook to query changed records and write them to Delta tables
D. Auto Loader with a file arrival trigger on DBFS

Answer: B

Explanation:
Lakeflow Connect's managed Salesforce connector provides a low-code, fully managed ingestion pipeline that incrementally loads Salesforce data into Unity Catalog-governed Delta tables.

NEW QUESTION # 28
Which of the following can be used to simplify and unify siloed data architectures that are specialized for specific use cases?

A. All of these
B. None of these
C. Data lakehouse
D. Data lake
E. Data warehouse

Answer: C

Explanation:
A data lakehouse is a new paradigm that can be used to simplify and unify siloed data architectures that are specialized for specific use cases. A data lakehouse combines the best of both data lakes and data warehouses, providing a single platform that supports diverse data types, open standards, low-cost storage, high-performance queries, ACID transactions, schema enforcement, and governance. A data lakehouse enables data engineers to build reliable and scalable data pipelines that can serve various downstream applications and users, such as data science, machine learning, analytics, and reporting. A data lakehouse leverages the power of Delta Lake, a storage layer that brings reliability and performance to data lakes. References: What is a data lakehouse?, Delta Lake, Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics

NEW QUESTION # 29
A data engineer runs a statement every day to copy the previous day's sales into the table transactions. Each day's sales are in their own file in the location "/transactions/raw".
Today, the data engineer runs the following command to complete this task:

After running the command today, the data engineer notices that the number of records in table transactions has not changed.
Which of the following describes why the statement might not have copied any new records into the table?

A. The names of the files to be copied were not included with the FILES keyword.
B. The previous day's file has already been copied into the table.
C. The PARQUET file format does not support COPY INTO.
D. The COPY INTO statement requires the table to be refreshed to view the copied rows.
E. The format of the files to be copied were not included with the FORMAT_OPTIONS keyword.

Answer: B

NEW QUESTION # 30
After a failed multi-task job run, a data engineer changes the cluster size and updates a notebook path for one task. The data engineer wants to re-run just the failed/skipped tasks with the updated settings and keep the run lineage intact. Which strategy should the data engineer use to preserve the run history?

A. Increase the task retry count and click 'Retry' on the failed task to automatically apply new cluster settings within the same run.
B. Use Jobs REST API repairRun to re-run failed tasks; the repair uses the current job and task settings and appears in the run history.
C. Export the job as a Databricks Asset Bundle, redeploy it, and re-run only the modified tasks from the bundle.
D. Use 'Run now' to start a new run, so that the updated settings apply; the new run replaces the prior run history.

Answer: B

Explanation:
The repairRun capability allows rerunning only failed or skipped tasks while preserving the original run lineage, and it applies the current job and task settings during the repair execution.

NEW QUESTION # 31
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: C

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

NEW QUESTION # 32
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