[2024年08月24日]Databricks-Certified-Data-Engineer-Associate試験問題集PDF正確率保証と更新された問題
合格させるDatabricks-Certified-Data-Engineer-Associate試験にはリアルテストエンジンPDFには102問題あります
質問 # 23
A new data engineering team team has been assigned to an ELT project. The new data engineering team will need full privileges on the table sales to fully manage the project.
Which of the following commands can be used to grant full permissions on the database to the new data engineering team?
- A. GRANT ALL PRIVILEGES ON TABLE sales TO team;
- B. GRANT SELECT CREATE MODIFY ON TABLE sales TO team;
- C. GRANT SELECT ON TABLE sales TO team;
- D. GRANT USAGE ON TABLE sales TO team;
- E. GRANT ALL PRIVILEGES ON TABLE team TO sales;
正解：A
質問 # 24
A data engineering team has noticed that their Databricks SQL queries are running too slowly when they are submitted to a non-running SQL endpoint. The data engineering team wants this issue to be resolved.
Which of the following approaches can the team use to reduce the time it takes to return results in this scenario?
- A. They can turn on the Serverless feature for the SQL endpoint.
- B. They can increase the maximum bound of the SQL endpoint's scaling range
- C. They can increase the cluster size of the SQL endpoint.
- D. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to
"Reliability Optimized." - E. They can turn on the Auto Stop feature for the SQL endpoint.
正解：C
解説：
Explanation
https://www.databricks.com/blog/2022/03/10/top-5-databricks-performance-tips.html
質問 # 25
A data engineer needs to apply custom logic to string column city in table stores for a specific use case. In order to apply this custom logic at scale, the data engineer wants to create a SQL user-defined function (UDF).
Which of the following code blocks creates this SQL UDF?
- A.
- B.
- C.
- D.
- E.
正解：D
解説：
Explanation
https://www.databricks.com/blog/2021/10/20/introducing-sql-user-defined-functions.html
質問 # 26
A data engineer wants to create a new table containing the names of customers that live in France.
They have written the following command:
A senior data engineer mentions that it is organization policy to include a table property indicating that the new table includes personally identifiable information (PII).
Which of the following lines of code fills in the above blank to successfully complete the task?
- A. COMMENT "Contains PII"
- B. There is no way to indicate whether a table contains PII.
- C. "COMMENT PII"
- D. TBLPROPERTIES PII
- E. PII
正解：D
質問 # 27
An engineering manager uses a Databricks SQL query to monitor ingestion latency for each data source. The manager checks the results of the query every day, but they are manually rerunning the query each day and waiting for the results.
Which of the following approaches can the manager use to ensure the results of the query are updated each day?
- A. They can schedule the query to refresh every 1 day from the query's page in Databricks SQL.
- B. They can schedule the query to run every 1 day from the Jobs UI.
- C. They can schedule the query to run every 12 hours from the Jobs UI.
- D. They can schedule the query to refresh every 1 day from the SQL endpoint's page in Databricks SQL.
- E. They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL.
正解：A
解説：
Databricks SQL allows users to schedule queries to run automatically at a specified frequency and time zone.
This can help users to keep their dashboards or alerts updated with the latest data. To schedule a query, users need to do the following steps:
* In the Query Editor, click Schedule > Add schedule to open a menu with schedule settings.
* Choose when to run the query. Use the dropdown pickers to specify the frequency, period, starting time, and time zone. Optionally, select the Show cron syntax checkbox to edit the schedule in Quartz Cron Syntax.
* Choose More options to show optional settings. Users can also choose a name for the schedule, and a SQL warehouse to power the query.
* Click Create. The query will run automatically according to the schedule.
The other options are incorrect because they do not refer to the correct location or frequency to schedule the query. The query's page in Databricks SQL is the place where users can edit, run, or schedule the query. The SQL endpoint's page in Databricks SQL is the place where users can manage the SQL warehouses and SQL endpoints. The Jobs UI is the place where users can create, run, or schedule jobs that execute notebooks, JARs, or Python scripts. References: Schedule a query, What are Databricks SQL alerts?, Jobs.
質問 # 28
A data analyst has a series of queries in a SQL program. The data analyst wants this program to run every day.
They only want the final query in the program to run on Sundays. They ask for help from the data engineering team to complete this task.
Which of the following approaches could be used by the data engineering team to complete this task?
- A. They could only run the entire program on Sundays.
- B. They could submit a feature request with Databricks to add this functionality.
- C. They could wrap the queries using PySpark and use Python's control flow system to determine when to run the final query.
- D. They could automatically restrict access to the source table in the final query so that it is only accessible on Sundays.
- E. They could redesign the data model to separate the data used in the final query into a new table.
正解：C
質問 # 29
Which of the following data workloads will utilize a Gold table as its source?
- A. A job that aggregates uncleaned data to create standard summary statistics
- B. A job that cleans data by removing malformatted records
- C. A job that enriches data by parsing its timestamps into a human-readable format
- D. A job that ingests raw data from a streaming source into the Lakehouse
- E. A job that queries aggregated data designed to feed into a dashboard
正解：E
解説：
A Gold table is a table that contains highly refined and aggregated data that powers analytics, machine learning, and production applications. It represents data that has been transformed into knowledge, rather than just information. A Gold table is typically the final output of a medallion lakehouse architecture, where data flows from Bronze to Silver to Gold tables, with each layer improving the structure and quality of data. A job that queries aggregated data designed to feed into a dashboard is an example of a data workload that will utilize a Gold table as its source, as it requires data that is ready for consumption and analysis. The other options are either data workloads that will use a Bronze or Silver table as their source, or data workloads that will produce a Gold table as their output. References: Databricks Documentation - What is the medallion lakehouse architecture?, Databricks Documentation - What is a Medallion Architecture?, K21Academy - Delta Lake Architecture & Azure Databricks Workspace.
質問 # 30
Which of the following must be specified when creating a new Delta Live Tables pipeline?
- A. At least one notebook library to be executed
- B. A key-value pair configuration
- C. The preferred DBU/hour cost
- D. A path to cloud storage location for the written data
- E. A location of a target database for the written data
正解：A
解説：
Explanation
https://docs.databricks.com/en/delta-live-tables/tutorial-pipelines.html
質問 # 31
In order for Structured Streaming to reliably track the exact progress of the processing so that it can handle any kind of failure by restarting and/or reprocessing, which of the following two approaches is used by Spark to record the offset range of the data being processed in each trigger?
- A. Checkpointing and Write-ahead Logs
- B. Structured Streaming cannot record the offset range of the data being processed in each trigger.
- C. Checkpointing and Idempotent Sinks
- D. Write-ahead Logs and Idempotent Sinks
- E. Replayable Sources and Idempotent Sinks
正解：C
質問 # 32
In which of the following file formats is data from Delta Lake tables primarily stored?
- A. Parquet
- B. Delta
- C. A proprietary, optimized format specific to Databricks
- D. CSV
- E. JSON
正解：A
解説：
Explanation
https://docs.delta.io/latest/delta-faq.html
質問 # 33
A data engineer has realized that the data files associated with a Delta table are incredibly small. They want to compact the small files to form larger files to improve performance.
Which of the following keywords can be used to compact the small files?
- A. REPARTITION
- B. REDUCE
- C. OPTIMIZE
- D. VACUUM
- E. COMPACTION
正解：C
解説：
The keyword that can be used to compact the small files associated with a Delta table is OPTIMIZE. The OPTIMIZE command performs file compaction on a Delta table by rewriting a set of small files into a set of larger files1. This can improve the performance of queries that scan the table by reducing the number of files that need to be read and the amount of metadata that needs to be processed1. The OPTIMIZE command can also optionally sort the data within each file by a given set of columns, which can further improve the query performance by enabling data skipping and predicate pushdown1. The OPTIMIZE command can be applied to the whole table or to a specific partition of the table1.
The other keywords are not suitable for compacting the small files associated with a Delta table. REDUCE is a keyword used in the SQL syntax for aggregating data using a user-defined function2. COMPACTION is not a valid keyword in SQL or Python. REPARTITION is a keyword used in the Python syntax for changing the number of partitions of a DataFrame or an RDD3. VACUUM is a keyword used to remove files that are no longer referenced by a Delta table and are older than a retention threshold4.
Reference:
1: OPTIMIZE | Databricks on AWS
2: REDUCE | Databricks on AWS
3: repartition | Databricks on AWS
4: VACUUM | Databricks on AWS
質問 # 34
A data engineer that is new to using Python needs to create a Python function to add two integers together and return the sum?
Which of the following code blocks can the data engineer use to complete this task?
- A.
- B.
- C.
- D.
- E.
正解：C
解説：
Explanation
https://www.w3schools.com/python/python_functions.asp
質問 # 35
A data engineer is designing a data pipeline. The source system generates files in a shared directory that is also used by other processes. As a result, the files should be kept as is and will accumulate in the directory. The data engineer needs to identify which files are new since the previous run in the pipeline, and set up the pipeline to only ingest those new files with each run.
Which of the following tools can the data engineer use to solve this problem?
- A. Databricks SQL
- B. Auto Loader
- C. Delta Lake
- D. Unity Catalog
- E. Data Explorer
正解：B
質問 # 36
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. There is no way to determine why a Job task is running slowly.
- B. They can navigate to the Tasks tab in the Jobs UI to immediately review the processing notebook.
- 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 Runs tab in the Jobs UI and click on the active run to review the processing notebook.
正解：E
解説：
Explanation
The job run details page contains job output and links to logs, including information about the success or failure of each task in the job run. You can access job run details from the Runs tab for the job. To view job run details from the Runs tab, click the link for the run in the Start time column in the runs list view. To return to the Runs tab for the job, click the Job ID value.
If the job contains multiple tasks, click a task to view task run details, including:
the cluster that ran the task
the Spark UI for the task
logs for the task
metrics for the task
https://docs.databricks.com/en/workflows/jobs/monitor-job-runs.html#job-run-details
質問 # 37
A data engineer needs to use a Delta table as part of a data pipeline, but they do not know if they have the appropriate permissions.
In which of the following locations can the data engineer review their permissions on the table?
- A. Databricks Filesystem
- B. Dashboards
- C. Jobs
- D. Data Explorer
- E. Repos
正解：D
解説：
Data Explorer is a graphical interface that allows you to browse, create, and manage data objects such as databases, tables, and views in your workspace. You can also review and modify the permissions on these data objects using Data Explorer. To access Data Explorer, you can click on the Data icon in the sidebar, or use the
%sql magic command in a notebook. You can then select a database and a table, and click on the Permissions tab to view and edit the access control lists (ACLs) for the table. You can also use SQL commands such as SHOW GRANT and GRANT to query and modify the permissions on a Delta table. References:
* Data Explorer
* Access control for Delta tables
* SHOW GRANT
* [GRANT]
質問 # 38
What is stored in a Databricks customer's cloud account?
- A. Notebooks
- B. Data
- C. Databricks web application
- D. Cluster management metadata
正解：B
解説：
In a Databricks customer's cloud account, the primary elements stored include:
Data: This is the central type of content stored in the customer's cloud account. Data might include various datasets, tables, and files that are used and managed through Databricks platforms.
Notebooks: These are also stored within a customer's cloud account. Notebooks include scripts, notes, and other information necessary for data analysis and processing tasks.
Cluster management metadata is indeed managed through the cloud, but it's primarily handled by Databricks rather than stored directly in the customer's account. The Databricks web application itself is not stored within the customer's cloud account; rather, it's a service provided by Databricks.
Reference:
Databricks documentation: Data in Databricks
質問 # 39
In order for Structured Streaming to reliably track the exact progress of the processing so that it can handle any kind of failure by restarting and/or reprocessing, which of the following two approaches is used by Spark to record the offset range of the data being processed in each trigger?
- A. Structured Streaming cannot record the offset range of the data being processed in each trigger.
- B. Write-ahead Logs and Idempotent Sinks
- C. Replayable Sources and Idempotent Sinks
- D. Checkpointing and Idempotent Sinks
- E. Checkpointing and Write-ahead Logs
正解：E
解説：
Structured Streaming uses checkpointing and write-ahead logs to record the offset range of the data being processed in each trigger. This ensures that the engine can reliably track the exact progress of the processing and handle any kind of failure by restarting and/or reprocessing. Checkpointing is the mechanism of saving the state of a streaming query to fault-tolerant storage (such as HDFS) so that it can be recovered after a failure.
Write-ahead logs are files that record the offset range of the data being processed in each trigger and are written to the checkpoint location before the processing starts. These logs are used to recover the query state and resume processing from the last processed offset range in case of a failure. References: Structured Streaming Programming Guide, Fault Tolerance Semantics
質問 # 40
Which of the following describes when to use the CREATE STREAMING LIVE TABLE (formerly CREATE INCREMENTAL LIVE TABLE) syntax over the CREATE LIVE TABLE syntax when creating Delta Live Tables (DLT) tables using SQL?
- A. CREATE STREAMING LIVE TABLE is redundant for DLT and it does not need to be used.
- B. CREATE STREAMING LIVE TABLE should be used when the previous step in the DLT pipeline is static.
- C. CREATE STREAMING LIVE TABLE should be used when data needs to be processed incrementally.
- D. CREATE STREAMING LIVE TABLE should be used when the subsequent step in the DLT pipeline is static.
- E. CREATE STREAMING LIVE TABLE should be used when data needs to be processed through complicated aggregations.
正解：C
解説：
Explanation
The CREATE STREAMING LIVE TABLE syntax is used when you want to create Delta Live Tables (DLT) tables that are designed for processing data incrementally. This is typically used when your data pipeline involves streaming or incremental data updates, and you want the table to stay up to date as new data arrives.
It allows you to define tables that can handle data changes incrementally without the need for full table refreshes.
質問 # 41
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