質問 1:Which of the following approaches should be used to send the Databricks Job owner an email in the case that the Job fails?
A. There is no way to notify the Job owner in the case of Job failure
B. Setting up an Alert in the Job page
C. MLflow Model Registry Webhooks
D. Manually programming in an alert system in each cell of the Notebook
E. Setting up an Alert in the Notebook
正解:B
解説: (Topexam メンバーにのみ表示されます)
質問 2:A data engineer has left the organization. The data team needs to transfer ownership of the data engineer's Delta tables to a new data engineer. The new data engineer is the lead engineer on the data team.
Assuming the original data engineer no longer has access, which of the following individuals must be the one to transfer ownership of the Delta tables in Data Explorer?
A. New lead data engineer
B. Databricks account representative
C. Original data engineer
D. Workspace administrator
E. This transfer is not possible
正解:D
解説: (Topexam メンバーにのみ表示されます)
質問 3:A data engineer is attempting to drop a Spark SQL table my_table and runs the following command:
DROP TABLE IF EXISTS my_table;
After running this command, the engineer notices that the data files and metadata files have been deleted from the file system.
Which of the following describes why all of these files were deleted?
A. The table's data was larger than 10 GB
B. The table did not have a location
C. The table was managed
D. The table's data was smaller than 10 GB
E. The table was external
正解:C
解説: (Topexam メンバーにのみ表示されます)
質問 4:A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Development mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?
A. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.
B. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
C. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
D. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
E. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist until the pipeline is shut down.
正解:E
解説: (Topexam メンバーにのみ表示されます)
質問 5:A data engineer needs to apply custom logic to identify employees with more than 5 years of experience in array column employees in table stores. The custom logic should create a new column exp_employees that is an array of all of the employees with more than 5 years of experience for each row. In order to apply this custom logic at scale, the data engineer wants to use the FILTER higher-order function.
Which of the following code blocks successfully completes this task?
A. Option E
B. Option A
C. Option C
D. Option D
E. Option B
正解:B
解説: (Topexam メンバーにのみ表示されます)
質問 6:Which of the following is a benefit of the Databricks Lakehouse Platform embracing open source technologies?
A. Cloud-specific integrations
B. Avoiding vendor lock-in
C. Ability to scale workloads
D. Simplified governance
E. Ability to scale storage
正解:B
解説: (Topexam メンバーにのみ表示されます)
質問 7:A data engineer has joined an existing project and they see the following query in the project repository:
CREATE STREAMING LIVE TABLE loyal_customers AS
SELECT customer_id -
FROM STREAM(LIVE.customers)
WHERE loyalty_level = 'high';
Which of the following describes why the STREAM function is included in the query?
A. The data in the customers table has been updated since its last run.
B. The customers table is a reference to a Structured Streaming query on a PySpark DataFrame.
C. The STREAM function is not needed and will cause an error.
D. The customers table is a streaming live table.
E. The table being created is a live table.
正解:D
解説: (Topexam メンバーにのみ表示されます)
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Databricks Databricks-Certified-Data-Engineer-Associate 認定試験の出題範囲:
トピック | 出題範囲 |
---|
トピック 1 | - Incremental Data Processing: In this topic questions about identifying Delta Lake, benefits of ACID transactions, a scenario to use an external table, location of a table, the benefits of Zordering, the kind of files, CTAS as a solution, the impact of ON VIOLATION DROP ROW and ON VIOLATION FAIL UPDATE, and the necessary component to create a new DLT pipeline. Moreover, the topic also discusses directory structure of Delta Lake files, generated column, adding a table comment, and the benefits of the MERGE command.
|
トピック 2 | - Databricks Lakehouse Platform: This topic covers the relationship between the data lakehouse and the data warehouse, the improvement in data quality, comparing and contrasting silver and gold tables, elements of the Databricks Platform Architecture, and differentiating between all-purpose clusters and jobs clusters. Moreover, it identifies how cluster software is versioned, how clusters can be filtered, how to use multiple languages, how to run one notebook, how notebooks can be shared, Git operations, and limitations in Databricks Notebooks. Lastly, the topic describes how clusters are terminated, how to use multiple languages, and how Databricks Repos enables CI
- CD workflows.
|
トピック 3 | - Production Pipelines: It focuses on identifying the advantages of using multiple tasks in Jobs, a suitable scenario where predecessor task should be set up, CRON as an opportunity for scheduling opportunity, and how an alert can be sent via email. The topic also discusses setting up a predecessor task in Jobs, reviewing a task's execution history, and debugging a failed task. Lastly, it delves into setting up a retry policy in case of failure and creating an alert in the case of a failed task.
|
トピック 4 | - Data Governance: It identifies one of the four areas of data governance, Unity Catalog securables, and the cluster security modes. It also discusses how to create a UC-enabled all-purpose cluster and a DBSQL warehouse. The topic explains how to implement data object access control, create a DBSQL warehouse, and e a UC-enabled all-purpose cluster.
|
トピック 5 | - ELT with Apache Spark: It focuses on extracting data, identifying the prefix, creating a view, duplicating rows, creating a new table, utilizing the dot, parsing JSON, and defining a SQL UDF. Moreover, the topic delves into describing the security model, identifying the location of a function, and identifying the PIVOT.
|
参照:https://www.databricks.com/learn/certification/data-engineer-associate
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