質問 1:Which of the following must be specified when creating a new Delta Live Tables pipeline?
A. A location of a target database for the written data
B. A path to cloud storage location for the written data
C. The preferred DBU/hour cost
D. A key-value pair configuration
E. At least one notebook library to be executed
正解:E
解説: (Topexam メンバーにのみ表示されます)
質問 2:Which of the following describes the relationship between Gold tables and Silver tables?
A. Gold tables are more likely to contain aggregations than Silver tables.
B. Gold tables are more likely to contain truthful data than Silver tables.
C. Gold tables are more likely to contain a less refined view of data than Silver tables.
D. Gold tables are more likely to contain valuable data than Silver tables.
E. Gold tables are more likely to contain more data than Silver tables.
正解:A
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. The data engineer wants to delete all table metadata and data.
They run the following command:
DROP TABLE IF EXISTS my_table
While the object no longer appears when they run SHOW TABLES, the data files still exist.
Which of the following describes why the data files still exist and the metadata 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:E
解説: (Topexam メンバーにのみ表示されます)
質問 4:In which of the following file formats is data from Delta Lake tables primarily stored?
A. A proprietary, optimized format specific to Databricks
B. CSV
C. JSON
D. Parquet
E. Delta
正解:D
解説: (Topexam メンバーにのみ表示されます)
質問 5:A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The code block used by the data engineer is below:
If the data engineer only wants the query to process all of the available data in as many batches as required, which of the following lines of code should the data engineer use to fill in the blank?
A. trigger(continuous="once")
B. trigger(availableNow=True)
C. processingTime(1)
D. trigger(processingTime="once")
E. trigger(parallelBatch=True)
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 data lakehouse features results in improved data quality over a traditional data lake?
A. A data lakehouse supports ACID-compliant transactions.
B. A data lakehouse stores data in open formats.
C. A data lakehouse provides storage solutions for structured and unstructured data.
D. A data lakehouse allows the use of SQL queries to examine data.
E. A data lakehouse enables machine learning and artificial Intelligence workloads.
正解:A
解説: (Topexam メンバーにのみ表示されます)
質問 7:A data engineer is running code in a Databricks Repo that is cloned from a central Git repository. A colleague of the data engineer informs them that changes have been made and synced to the central Git repository. The data engineer now needs to sync their Databricks Repo to get the changes from the central Git repository.
Which of the following Git operations does the data engineer need to run to accomplish this task?
A. Pull
B. Merge
C. Push
D. Clone
E. Commit
正解:A
解説: (Topexam メンバーにのみ表示されます)
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 | - 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.
|
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 | - 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 4 | - 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 5 | - 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.
|
参照:https://www.databricks.com/learn/certification/data-engineer-associate
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