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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q224-Q229):

NEW QUESTION # 224
A data engineer needs to reduce the number of small files generated during streaming ingestion into Delta tables. Small files are causing performance issues during downstream queries. Which Delta Lake feature automatically compacts small files during writes?

A. VACUUM
B. autoCompact
C. CACHE
D. OPTIMIZE

Answer: B

NEW QUESTION # 225
A data organization leader is upset about the data analysis team's reports being different from the data engineering team's reports. The leader believes the siloed nature of their organization's data engineering and data analysis architectures is to blame.
Which of the following describes how a data lakehouse could alleviate this issue?

A. Both teams would respond more quickly to ad-hoc requests
B. Both teams would be able to collaborate on projects in real-time
C. Both teams would autoscale their work as data size evolves
D. Both teams would use the same source of truth for their work
E. Both teams would reorganize to report to the same department

Answer: D

NEW QUESTION # 226
Which method should a Data Engineer apply to ensure Workflows are being triggered on schedule?

A. Scheduled Workflows process data as it arrives at configured sources.
B. Scheduled Workflows run continuously until manually stopped.
C. Scheduled Workflows require an always-running cluster, which is more expensive but reduces processing latency.
D. Scheduled Workflows can reduce resource consumption and expense since the cluster runs only long enough to execute the pipeline.

Answer: D

Explanation:
Scheduled Workflows in Databricks are designed to trigger jobs at specified intervals. These workflows can be configured to start a cluster when the job is scheduled, and the cluster will only run for the duration of the task. Once the workflow completes, the cluster can be terminated, which helps to reduce resource consumption and costs since you are only paying for the cluster while the workflow is running.

NEW QUESTION # 227
A data engineer needs access to a table new_table, but they do not have the correct permissions. They can ask the table owner for permission, but they do not know who the table owner is.
Which of the following approaches can be used to identify the owner of new_table?

A. Review the Permissions tab in the table's page in Data Explorer
B. Review the Owner field in the table's page in Data Explorer
C. There is no way to identify the owner of the table
D. All of these options can be used to identify the owner of the table
E. Review the Owner field in the table's page in the cloud storage solution

Answer: B

Explanation:
he approach that can be used to identify the owner of new_table is to review the Owner field in the table's page in Data Explorer. Data Explorer is a web-based interface that allows users to browse, create, and manage data objects such as tables, views, and functions in Databricks1. The table's page in Data Explorer provides various information about the table, such as its schema, partitions, statistics, history, and permissions2. The Owner field shows the name and email address of the user who created or owns the table3. The data engineer can use this information to contact the table owner and request for permission to access the table.
The other options are not correct or reliable for identifying the owner of new_table. Reviewing the Permissions tab in the table's page in Data Explorer can show the users and groups who have access to the table, but not necessarily the owner4. Reviewing the Owner field in the table's page in the cloud storage solution can be misleading, as the owner of the data files may not be the same as the owner of the table5.
There is a way to identify the owner of the table, as explained above, so option E is false.
References:
* 1: Data Explorer | Databricks on AWS
* 2: Table details | Databricks on AWS
* 3: Set owner when creating a view in databricks sql - Databricks - 9978
* 4: Table access control | Databricks on AWS
* 5: External tables | Databricks on AWS

NEW QUESTION # 228
A data engineer has been given a new record of data:
id STRING = 'a1'
rank INTEGER = 6
rating FLOAT = 9.4
Which of the following SQL commands can be used to append the new record to an existing Delta table my_table?

A. INSERT INTO my_table VALUES ('a1', 6, 9.4)
B. UPDATE VALUES ('a1', 6, 9.4) my_table
C. my_table UNION VALUES ('a1', 6, 9.4)
D. UPDATE my_table VALUES ('a1', 6, 9.4)
E. INSERT VALUES ( 'a1' , 6, 9.4) INTO my_table

Answer: A

NEW QUESTION # 229
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