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

SectionObjectives
Topic 1: Data Processing and Transformations- Apache Spark SQL operations (joins, aggregations, filtering)
- Delta Lake fundamentals (tables, transactions, optimization)
- PySpark DataFrame transformations
- User-defined functions (UDFs)
Topic 2: Data Governance and Quality- Data access control and governance
- Unity Catalog basics
- Data quality concepts and management
Topic 3: Productionizing Data Pipelines- Pipeline deployment and operationalization
- Databricks Workflows / Jobs orchestration
- Scheduling and monitoring jobs
Topic 4: Databricks Lakehouse Platform Fundamentals- Workspace, architecture, and core platform concepts
- Clusters, notebooks, and basic Databricks environment usage
Topic 5: Data Ingestion and ELT Development- ETL patterns and transformations
- Handling structured and semi-structured data
- Data ingestion using Spark SQL and PySpark

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q15-Q20):

NEW QUESTION # 15
A data engineer at a company that uses Databricks with Unity Catalog needs to share a collection of tables with an external partner who also uses a Databricks workspace enabled for Unity Catalog. The data engineer decides to use Delta Sharing to accomplish this. What is the first piece of information the data engineer should request from the external partner to set up Delta Sharing?

Answer: B

Explanation:
To configure Delta Sharing between two Databricks workspaces with Unity Catalog, the first requirement is the sharing identifier of the partner's Unity Catalog metastore, which enables secure setup of the share and recipient relationship.


NEW QUESTION # 16
A data engineer wants to create a data entity from a couple of tables. The data entity must be used by other data engineers in other sessions. It also must be saved to a physical location.
Which of the following data entities should the data engineer create?

Answer: E

Explanation:
A table is a data entity that is stored in a physical location and can be accessed by other data engineers in other sessions. A table can be created from one or more tables using the CREATE TABLE or CREATE TABLE AS SELECT commands. A table can also be registered from an existing DataFrame using the spark.catalog.createTable method. A table can be queried using SQL or DataFrame APIs. A table can also be updated, deleted, or appended using the MERGE INTO command or the DeltaTable API. Reference:
Create a table
Create a table from a query result
Register a table from a DataFrame
[Query a table]
[Update, delete, or merge into a table]


NEW QUESTION # 17
A data engineer is setting up access control in Unity Catalog and needs to ensure that a group of data analysts can query tables but not modify data. Which permission should the data engineer grant to the data analysts?

Answer: D

Explanation:
Granting the SELECT permission in Unity Catalog allows data analysts to query and read data from tables without being able to modify, insert, or delete records. This enforces read-only access, which matches the requirement.


NEW QUESTION # 18
A data engineering team needs to integrate two data sources into Databricks:
- Clickstream Events: Ingesting 5,000 events per second from an Apache
Kafka topic.
- Customer Master Data: Syncing only changed records every 4 hours from a Snowflake database.
The solution must meet two key requirements:
- Process the streaming clickstream data with a latency of under 30
seconds.
- Prevent the reprocessing of customer master data that has not
changed.
Which data ingestion approach meets these requirements?

Answer: B

Explanation:
Structured Streaming provides continuous, low-latency ingestion from Kafka. A Lakeflow Connect managed connector in incremental mode tracks source changes and ingests only changed Snowflake records after the initial load, preventing unnecessary reprocessing.


NEW QUESTION # 19
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?

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.
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 # 20
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