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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q247-Q252):

NEW QUESTION # 247
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 Owner field in the table's page in Data Explorer
B. All of these options can be used to identify the owner of the table
C. Review the Owner field in the table's page in the cloud storage solution
D. There is no way to identify the owner of the table
E. Review the Permissions tab in the table's page in Data Explorer

Answer: A

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 # 248
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 Runs 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 Tasks tab in the Jobs UI and click on the active run to review the processing notebook.
D. They can navigate to the Runs tab in the Jobs UI to immediately review the processing notebook.
E. They can navigate to the Tasks tab in the Jobs UI to immediately review the processing notebook.

Answer: A

NEW QUESTION # 249
A data engineer needs to determine whether to use the built-in Databricks Notebooks versioning or version their project using Databricks Repos.
Which of the following is an advantage of using Databricks Repos over the Databricks Notebooks versioning?

A. Databricks Repos provides the ability to comment on specific changes
B. Databricks Repos supports the use of multiple branches
C. Databricks Repos automatically saves development progress
D. Databricks Repos allows users to revert to previous versions of a notebook
E. Databricks Repos is wholly housed within the Databricks Lakehouse Platform

Answer: B

Explanation:
Explanation
An advantage of using Databricks Repos over the built-in Databricks Notebooks versioning is the ability to work with multiple branches. Branching is a fundamental feature ofversion control systems like Git, which Databricks Repos is built upon. It allows you to create separate branches for different tasks, features, or experiments within your project. This separation helps in parallel development and experimentation without affecting the main branch or the work of other team members. Branching provides a more organized and collaborative development environment, making it easier to merge changes and manage different development efforts. While Databricks Notebooks versioning also allows you to track versions of notebooks, it may not provide the same level of flexibility and collaboration as branching in Databricks Repos.

NEW QUESTION # 250
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 have a well-defined schema
B. Parquet files have the ability to be optimized
C. Parquet files can be partitioned
D. Parquet files will become Delta tables
E. CREATE TABLE AS SELECT statements cannot be used on files

Answer: A

NEW QUESTION # 251
A data engineer needs to enforce row-level security on main.secure.events(region STRING, event_id STRING). Members of the account group all_regions must see all rows. All other users must see only rows where region = 'EU'.
Which SQL sequence satisfies the requirement?

A. CREATE FUNCTION main.secure.region_filter(region STRING)
RETURN IF(is_account_group_member('all_regions'), true, region = 'EU'); ALTER TABLE main.secure.events ALTER COLUMN region SET MASK main.secure.region_filter;
B. CREATE FUNCTION main.secure.region_filter(region STRING)
RETURN IF(is_account_group_member('all_regions'), true, region = 'EU'); ALTER VIEW main.secure.events SET ROW FILTER main.secure.region_filter ON (region);
C. CREATE FUNCTION main.secure.region_filter(region STRING)
RETURN IF(is_account_group_member('all_regions'), true, region = 'EU'); ALTER TABLE main.secure.events SET ROW FILTER main.secure.region_filter ON (region);
D. CREATE FUNCTION main.secure.region_filter(region STRING)
RETURN IF(is_account_group_member('all_regions'), true, region = 'EU'); ALTER TABLE main.secure.events SET ROW FILTER main.secure.region_filter USING COLUMNS (region);

Answer: C

Explanation:
A row-filter function must return a Boolean value indicating whether each row is visible to the current user. The function in option A returns true for members of the account-level all_regions group, allowing them to see every row. For other users, it returns the result of region = 'EU', limiting visibility to European records. is_account_group_member checks direct or indirect membership in an account-level group. The ALTER TABLE ... SET ROW FILTER ... ON (region) statement then associates the function with the table and passes the region column to it for every query. A column mask transforms displayed column values rather than removing rows. The object is a table, not a view, and USING COLUMNS is not the correct row-filter attachment syntax. Therefore, option A is correct.

NEW QUESTION # 252
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