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

Section	Weight	Objectives
Topic 1: Data Processing	28%	- ETL Pipelines - Structured Streaming - Spark SQL - Data Transformation
Topic 2: Data Modeling and Storage	20%	- File Formats - Data Modeling - Storage Optimization
Topic 3: Monitoring and Troubleshooting	16%	- Performance Optimization - Monitoring - Troubleshooting
Topic 4: Data Quality and Governance	12%	- Data Quality - Governance - Data Lineage
Topic 5: Databricks Lakehouse Platform	24%	- Delta Lake - Lakehouse Architecture - Unity Catalog - Data Management

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q75-Q80):

NEW QUESTION # 75
A Delta Lake table was created with the below query:

Consider the following query:
DROP TABLE prod.sales_by_store
If this statement is executed by a workspace admin, which result will occur?

A. An error will occur because Delta Lake prevents the deletion of production data.
B. Data will be marked as deleted but still recoverable with Time Travel.
C. The table will be removed from the catalog but the data will remain in storage.
D. Nothing will occur until a COMMIT command is executed.
E. The table will be removed from the catalog and the data will be deleted.

Answer: E

Explanation:
When a table is dropped in Delta Lake, the table is removed from the catalog and the data is deleted. This is because Delta Lake is a transactional storage layer that provides ACID guarantees. When a table is dropped, the transaction log is updated to reflect the deletion of the table and the data is deleted from the underlying storage.

NEW QUESTION # 76
The data architect has decided that once data has been ingested from external sources into the Databricks Lakehouse, table access controls will be leveraged to manage permissions for all production tables and views.
The following logic was executed to grant privileges for interactive queries on a production database to the core engineering group.
GRANT USAGE ON DATABASE prod TO eng;
GRANT SELECT ON DATABASE prod TO eng;
Assuming these are the only privileges that have been granted to the eng group and that these users are not workspace administrators, which statement describes their privileges?

A. Group members are able to list all tables in the prod database but are not able to see the results of any queries on those tables.
B. Group members have full permissions on the prod database and can also assign permissions to other users or groups.
C. Group members are able to create, query, and modify all tables and views in the prod database, but cannot define custom functions.
D. Group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database.
E. Group members are able to query and modify all tables and views in the prod database, but cannot create new tables or views.

Answer: D

Explanation:
The GRANT USAGE ON DATABASE prod TO eng command grants the eng group the permission to use the prod database, which means they can list and access the tables and views in the database. The GRANT SELECT ON DATABASE prod TO eng command grants the eng group the permission to select data from the tables and views in the prod database, which means they can query the data using SQL or DataFrame API. However, these commands do not grant the eng group any other permissions, such as creating, modifying, or deleting tables and views, or defining custom functions. Therefore, the eng group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database.

NEW QUESTION # 77
A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.
Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.
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Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?

A. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.
B. Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
C. Both commands will fail. No new variables, tables, or views will be created.
D. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.
E. Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af: if this entity exists, Cmd 2 will succeed.

Answer: A

Explanation:
This is the correct answer because Cmd 1 is written in Python and uses a list comprehension to extract the country names from the geo_lookup table and store them in a Python variable named countries af. This variable will contain a list of strings, not a PySpark DataFrame or a SQL view.
Cmd 2 is written in SQL and tries to create a view named sales af by selecting from the sales table where city is in countries af. However, this command will fail because countries af is not a valid SQL entity and cannot be used in a SQL query. To fix this, a better approach would be to use spark.sql() to execute a SQL query in Python and pass the countries af variable as a parameter.

NEW QUESTION # 78
A table named user_ltv is being used to create a view that will be used by data analysts on Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the marketing group executes the following query:
SELECT * FROM email_ltv
Which statement describes the results returned by this query?

A. The email, age. and ltv columns will be returned with the values in user ltv.
B. The email and ltv columns will be returned with the values in user itv.
C. Only the email and itv columns will be returned; the email column will contain all null values.
D. Only the email and ltv columns will be returned; the email column will contain the string
"REDACTED" in each row.
E. Three columns will be returned, but one column will be named "redacted" and contain only null values.

Answer: D

Explanation:
The code creates a view called email_ltv that selects the email and ltv columns from a table called user_ltv, which has the following schema: email STRING, age INT, ltv INT. The code also uses the CASE WHEN expression to replace the email values with the string "REDACTED" if the user is not a member of the marketing group. The user who executes the query is not a member of the marketing group, so they will only see the email and ltv columns, and the email column will contain the string "REDACTED" in each row.

NEW QUESTION # 79
A data engineer needs to capture pipeline settings from an existing in the workspace, and use them to create and version a JSON file to create a new pipeline. Which command should the data engineer enter in a web terminal configured with the Databricks CLI?

A. Use the alone command to create a copy of an existing pipeline; use the get JSON command to get the pipeline definition; save this to git
B. Stop the existing pipeline; use the returned settings in a reset command
C. Use the get command to capture the settings for the existing pipeline; remove the pipeline_id and rename the pipeline; use this in a create command
D. Use list pipelines to get the specs for all pipelines; get the pipeline spec from the return results parse and use this to create a pipeline

Answer: C

Explanation:
The Databricks CLI provides a way to automate interactions with Databricks services. When dealing with pipelines, you can use the databricks pipelines get --pipeline-id command to capture the settings of an existing pipeline in JSON format. This JSON can then be modified by removing the pipeline_id to prevent conflicts and renaming the pipeline to create a new pipeline. The modified JSON file can then be used with the databricks pipelines create command to create a new pipeline with those settings.
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NEW QUESTION # 80
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