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GAQM Databricks-Certified-Data-Engineer-Associate（DataBricks Certified Data Engineer Associate）認定試験は、データの専門家にとって挑戦的で非常に尊敬される認定です。 DataBricksプラットフォームに焦点を当てて、データエンジニアリングにおける個人の知識とスキルをテストするように設計されています。試験に合格した個人は、キャリアを前進させ、収益の可能性を高めるのに役立つ貴重な認定を受けます。
認定試験は、データビリックを使用してデータパイプラインを構築および維持するデータエンジン、データアナリスト、およびデータサイエンティスト向けに設計されています。この試験は、データエンジニアリングとDatabricksの個人のスキルを検証し、効率的なデータパイプラインを設計および実装できるようにすることを目的としています。認定試験の目的は、データ処理とデータモデリングに関する候補者の知識、ならびにDataBricksを使用して大きなデータセットを処理する能力をテストすることを目的としています。
質問 # 59
A new data engineering team has been assigned to work on a project. The team will need access to database customers in order to see what tables already exist. The team has its own group team.
Which of the following commands can be used to grant the necessary permission on the entire database to the new team?
- A. GRANT CREATE ON DATABASE customers TO team;
- B. GRANT VIEW ON CATALOG customers TO team;
- C. GRANT CREATE ON DATABASE team TO customers;
- D. GRANT USAGE ON DATABASE customers TO team;
- E. GRANT USAGE ON CATALOG team TO customers;
正解：D
解説：
1: The correct command to grant the necessary permission on the entire database to the new team is to use the GRANT USAGE command. The GRANT USAGE command grants the principal the ability to access the securable object, such as a database, schema, or table. In this case, the securable object is the database customers, and the principal is the group team. By granting usage on the database, the team will be able to see what tables already exist in the database. Option E is the only option that uses the correct syntax and the correct privilege type for this scenario. Option A uses the wrong privilege type (VIEW) and the wrong securable object (CATALOG). Option B uses the wrong privilege type (CREATE), which would allow the team to create new tables in the database, but not necessarily see the existing ones. Option C uses the wrong securable object (CATALOG) and the wrong principal (customers). Option D uses the wrong securable object (team) and the wrong principal (customers). Reference: GRANT, Privilege types, Securable objects, Principals
質問 # 60
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:
Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?
- A. Replace "transactions" with the path to the location of the Delta table
- B. Replace format("delta") with format("stream")
- C. Replace spark.read with spark.readStream
- D. Replace schema(schema) with option ("maxFilesPerTrigger", 1)
- E. Replace predict with a stream-friendly prediction function
正解：C
解説：
1: To read from a stream source, the data engineer needs to use the spark.readStream method instead of the spark.read method. The spark.readStream method returns a DataStreamReader object that can be used to specify the details of the input source, such as the format, the schema, the path, and the options. The spark.read method is only suitable for batch processing, not streaming processing. The other changes are not necessary or correct for reading from a stream source. Reference: Structured Streaming Programming Guide, Read a stream, Databricks Data Sources
質問 # 61
A data engineering team has two tables. The first table march_transactions is a collection of all retail transactions in the month of March. The second table april_transactions is a collection of all retail transactions in the month of April. There are no duplicate records between the tables.
Which of the following commands should be run to create a new table all_transactions that contains all records from march_transactions and april_transactions without duplicate records?
- A. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
INNER JOIN SELECT * FROM april_transactions; - B. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
INTERSECT SELECT * from april_transactions; - C. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
MERGE SELECT * FROM april_transactions; - D. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
OUTER JOIN SELECT * FROM april_transactions; - E. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
UNION SELECT * FROM april_transactions;
正解：E
解説：
The correct command to create a new table that contains all records from two tables without duplicate records is to use the UNION operator. The UNION operator combines the results of two queries and removes any duplicate rows. The INNER JOIN, OUTER JOIN, and MERGE operators do not remove duplicate rows, and the INTERSECT operator only returns the rows that are common to both tables. Therefore, option B is the only correct answer. References: Databricks SQL Reference - UNION, Databricks SQL Reference - JOIN, Databricks SQL Reference - MERGE, [Databricks SQL Reference - INTERSECT]
質問 # 62
Which of the following commands will return the number of null values in the member_id column?
- A. SELECT count_if(member_id IS NULL) FROM my_table;
- B. SELECT count(member_id) FROM my_table;
- C. SELECT null(member_id) FROM my_table;
- D. SELECT count(member_id) - count_null(member_id) FROM my_table;
- E. SELECT count_null(member_id) FROM my_table;
正解：A
解説：
Explanation
https://docs.databricks.com/en/sql/language-manual/functions/count.html Returns A BIGINT.
If * is specified also counts row containing NULL values.
If expr are specified counts only rows for which all expr are not NULL.
If DISTINCT duplicate rows are not counted.
質問 # 63
A data engineer is working with two tables. Each of these tables is displayed below in its entirety.
The data engineer runs the following query to join these tables together:
Which of the following will be returned by the above query?
- A. Option B
- B. Option A
- C. Option E
- D. Option C
- E. Option D
正解：B
解説：
Option A is the correct answer because it shows the result of an INNER JOIN between the two tables. An INNER JOIN returns only the rows that have matching values in both tables based on the join condition. In this case, the join condition is ON a.customer_id = c.customer_id, which means that only the rows that have the same customer ID in both tables will be included in the output. The output will have four columns:
customer_id,name, account_id, and overdraft_amt. The output will have four rows, corresponding to the four customers who have accounts in the account table.
References: The use of INNER JOIN can be referenced from Databricks documentation on SQL JOIN or from other sources like W3Schools or GeeksforGeeks.
質問 # 64
A data organization leader is upset about the data analysis team's reports being different from the data engineering team's reports. The leader believes the siloed nature of their organization's data engineering and data analysis architectures is to blame.
Which of the following describes how a data lakehouse could alleviate this issue?
- A. Both teams would use the same source of truth for their work
- B. Both teams would reorganize to report to the same department
- C. Both teams would respond more quickly to ad-hoc requests
- D. Both teams would autoscale their work as data size evolves
- E. Both teams would be able to collaborate on projects in real-time
正解：A
解説：
A data lakehouse is a data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data12. By using a data lakehouse, both the data analysis and data engineering teams can access the same data sources and formats, ensuring data consistency and quality across their reports. A data lakehouse also supports schema enforcement and evolution, data validation, and time travel to old table versions, which can help resolve data conflicts and errors1. References: 1: What is a Data Lakehouse? - Databricks 2: What is a data lakehouse? | IBM
質問 # 65
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 smaller than 10 GB
- B. The table did not have a location
- C. The table's data was larger than 10 GB
- D. The table was external
- E. The table was managed
正解：E
解説：
The reason why all of the data files and metadata files were deleted from the file system after dropping the table is that the table was managed. A managed table is a table that is created and managed by Spark SQL. It stores both the data and the metadata in the default location specified by the spark.sql.warehouse.dir configuration property. When a managed table is dropped, both the data and the metadata are deleted from the file system.
Option B is not correct, as the size of the table's data does not affect the behavior of dropping the table.
Whether the table's data is smaller or larger than 10 GB, the data files and metadata files will be deleted if the table is managed, and will be preserved if the table is external.
Option C is not correct, for the same reason as option B.
Option D is not correct, as an external table is a table that is created and managed by the user. It stores the data in a user-specified location, and only stores the metadata in the Spark SQL catalog. When an external table is dropped, only the metadata is deleted from the catalog, but the data files are preserved in the file system.
Option E is not correct, as a table must have a location to store the data. If the location is not specified by the user, it will use the default location for managed tables. Therefore, a table without a location is a managed table, and dropping it will delete both the data and the metadata.
References:
* Managing Tables
* [Databricks Data Engineer Professional Exam Guide]
質問 # 66
A data engineer has a single-task Job that runs each morning before they begin working. After identifying an upstream data issue, they need to set up another task to run a new notebook prior to the original task.
Which of the following approaches can the data engineer use to set up the new task?
- A. They can create a new job from scratch and add both tasks to run concurrently.
- B. They can clone the existing task to a new Job and then edit it to run the new notebook.
- C. They can clone the existing task in the existing Job and update it to run the new notebook.
- D. They can create a new task in the existing Job and then add the original task as a dependency of the new task.
- E. They can create a new task in the existing Job and then add it as a dependency of the original task.
正解：E
解説：
To set up the new task to run a new notebook prior to the original task in a single-task Job, the data engineer can use the following approach: In the existing Job, create a new task that corresponds to the new notebook that needs to be run. Set up the new task with the appropriate configuration, specifying the notebook to be executed and any necessary parameters or dependencies. Once the new task is created, designate it as a dependency of the original task in the Job configuration. This ensures that the new task is executed before the original task.
質問 # 67
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：C
解説：
https://www.databricks.com/blog/2021/10/20/introducing-sql-user-defined-functions.html
質問 # 68
A data analyst has created a Delta table sales that is used by the entire data analysis team. They want help from the data engineering team to implement a series of tests to ensure the data is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following commands could the data engineering team use to access sales in PySpark?
- A. spark.sql("sales")
- B. spark.delta.table("sales")
- C. SELECT * FROM sales
- D. spark.table("sales")
- E. There is no way to share data between PySpark and SQL.
正解：D
解説：
The data engineering team can use the spark.table method to access the Delta table sales in PySpark. This method returns a DataFrame representation of the Delta table, which can be used for further processing or testing. The spark.table method works for any table that is registered in the Hive metastore or the Spark catalog, regardless of the file format1. Alternatively, the data engineering team can also use the DeltaTable.forPath method to load the Delta table from its path2. Reference: 1: SparkSession | PySpark 3.2.0 documentation 2: Welcome to Delta Lake's Python documentation page - delta-spark 2.4.0 documentation
質問 # 69
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 SQL endpoint's page in Databricks SQL.
- B. They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL.
- C. They can schedule the query to refresh every 1 day from the query's page in Databricks SQL.
- D. They can schedule the query to run every 12 hours from the Jobs UI.
- E. They can schedule the query to run every 1 day from the Jobs UI.
正解：C
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. Reference: Schedule a query, What are Databricks SQL alerts?, Jobs.
質問 # 70
A data engineer has realized that they made a mistake when making a daily update to a table. They need to use Delta time travel to restore the table to a version that is 3 days old. However, when the data engineer attempts to time travel to the older version, they are unable to restore the data because the data files have been deleted.
Which of the following explains why the data files are no longer present?
- A. The DELETE HISTORY command was run on the table
- B. The TIME TRAVEL command was run on the table
- C. The VACUUM command was run on the table
- D. The HISTORY command was run on the table
- E. The OPTIMIZE command was nun on the table
正解：C
解説：
The VACUUM command is used to remove files that are no longer referenced by a Delta table and are older than the retention threshold1. The default retention period is 7 days2, but it can be changed by setting the delta.logRetentionDuration and delta.deletedFileRetentionDuration configurations3. If the VACUUM command was run on the table with a retention period shorter than 3 days, then the data files that were needed to restore the table to a 3-day-old version would have been deleted. The other commands do not delete data files from the table. The TIME TRAVEL command is used to query a historical version of the table4. The DELETE HISTORY command is not a valid command in Delta Lake. The OPTIMIZE command is used to improve the performance of the table by compacting small files into larger ones5. The HISTORY command is used to retrieve information about the operations performed on the table. Reference: 1: VACUUM | Databricks on AWS 2: Work with Delta Lake table history | Databricks on AWS 3: [Delta Lake configuration | Databricks on AWS] 4: Work with Delta Lake table history - Azure Databricks 5: [OPTIMIZE | Databricks on AWS] : [HISTORY | Databricks on AWS]
質問 # 71
In which of the following scenarios should a data engineer select a Task in the Depends On field of a new Databricks Job Task?
- A. When another task has the same dependency libraries as the new task
- B. When another task needs to fail before the new task begins
- C. When another task needs to be replaced by the new task
- D. When another task needs to successfully complete before the new task begins
- E. When another task needs to use as little compute resources as possible
正解：D
解説：
A data engineer can create a multi-task job in Databricks that consists of multiple tasks that run in a specific order. Each task can have one or more dependencies, which are other tasks that must run before the current task. The Depends On field of a new Databricks Job Task allows the data engineer to specify the dependencies of the task. The data engineer should select a task in the Depends On field when they want the new task to run only after the selected task has successfully completed. This can help the data engineer to create a logical sequence of tasks that depend on each other's outputs or results. For example, a data engineer can create a multi-task job that consists of the following tasks:
* Task A: Ingest data from a source using Auto Loader
* Task B: Transform the data using Spark SQL
* Task C: Write the data to a Delta Lake table
* Task D: Analyze the data using Spark ML
* Task E: Visualize the data using Databricks SQL
In this case, the data engineer can set the dependencies of each task as follows:
* Task A: No dependencies
* Task B: Depends on Task A
* Task C: Depends on Task B
* Task D: Depends on Task C
* Task E: Depends on Task D
This way, the data engineer can ensure that each task runs only after the previous task has successfully completed, and the data flows smoothly from ingestion to visualization.
The other options are incorrect because they do not describe valid scenarios for selecting a task in the Depends On field. The Depends On field does not affect the following aspects of a task:
* Whether the task needs to be replaced by another task
* Whether the task needs to fail before another task begins
* Whether the task has the same dependency libraries as another task
* Whether the task needs to use as little compute resources as possible References: Create a multi-task job, Run tasks conditionally in a Databricks job, Databricks Jobs.
質問 # 72
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 should be used when data needs to be processed through complicated aggregations.
- B. CREATE STREAMING LIVE TABLE should be used when the previous step in the DLT pipeline is static.
- C. CREATE STREAMING LIVE TABLE is redundant for DLT and it does not need to be used.
- 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 incrementally.
正解：E
解説：
A streaming live table or view processes data that has been added only since the last pipeline update.
Streaming tables and views are stateful; if the defining query changes, new data will be processed based on the new query and existing data is not recomputed. This is useful when data needs to be processed incrementally, such as when ingesting streaming data sources or performing incremental loads from batch data sources. A live table or view, on the other hand, may be entirely computed when possible to optimize computation resources and time. This is suitable when data needs to be processed in full, such as when performing complex transformations or aggregations that require scanning all the data. References: Difference between LIVE TABLE and STREAMING LIVE TABLE, CREATE STREAMING TABLE, Load data using streaming tables in Databricks SQL.
質問 # 73
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(availableNow=True)
- B. trigger(continuous="once")
- C. trigger(processingTime="once")
- D. processingTime(1)
- E. trigger(parallelBatch=True)
正解：A
解説：
Explanation
https://stackoverflow.com/questions/71061809/trigger-availablenow-for-delta-source-streaming-queries-in-pyspa
質問 # 74
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 is wholly housed within the Databricks Lakehouse Platform
- B. Databricks Repos supports the use of multiple branches
- C. Databricks Repos allows users to revert to previous versions of a notebook
- D. Databricks Repos provides the ability to comment on specific changes
- E. Databricks Repos automatically saves development progress
正解：B
質問 # 75
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