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試験コード：Databricks-Certified-Data-Engineer-Professional

試験名称：Databricks Certified Data Engineer Professional Exam

最近更新時間：2024-05-16

問題と解答：全127問

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質問 1：
A nightly job ingests data into a Delta Lake table using the following code:

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?
A. return spark.readStream.load("bronze")
B. return spark.read.option("readChangeFeed", "true").table ("bronze")
C.

D. return spark.readStream.table("bronze")
E. def new_records():
正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 2：
A Structured Streaming job deployed to production has been experiencing delays during peak hours of the day. At present, during normal execution, each microbatch of data is processed in less than 3 seconds. During peak hours of the day, execution time for each microbatch becomes very inconsistent, sometimes exceeding 30 seconds. The streaming write is currently configured with a trigger interval of 10 seconds.
Holding all other variables constant and assuming records need to be processed in less than 10 seconds, which adjustment will meet the requirement?
A. Decrease the trigger interval to 5 seconds; triggering batches more frequently may prevent records from backing up and large batches from causing spill.
B. Decrease the trigger interval to 5 seconds; triggering batches more frequently allows idle executors to begin processing the next batch while longer running tasks from previous batches finish.
C. Use the trigger once option and configure a Databricks job to execute the query every 10 seconds; this ensures all backlogged records are processed with each batch.
D. Increase the trigger interval to 30 seconds; setting the trigger interval near the maximum execution time observed for each batch is always best practice to ensure no records are dropped.
E. The trigger interval cannot be modified without modifying the checkpoint directory; to maintain the current stream state, increase the number of shuffle partitions to maximize parallelism.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 3：
A Delta Lake table in the Lakehouse named customer_parsams is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
Immediately after each update succeeds, the data engineer team would like to determine the difference between the new version and the previous of the table. Given the current implementation, which method can be used?
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
A. Parse the Delta Lake transaction log to identify all newly written data files.
B. Execute a query to calculate the difference between the new version and the previous version using Delta Lake's built-in versioning and time travel functionality.
C. Execute DESCRIBE HISTORY customer_churn_params to obtain the full operation metrics for the update, including a log of all records that have been added or modified.
D. Parse the Spark event logs to identify those rows that were updated, inserted, or deleted.
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 4：
The data engineering team maintains the following code:
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Assuming that this code produces logically correct results and the data in the source tables has been de-duplicated and validated, which statement describes what will occur when this code is executed?
A. A batch job will update the enriched_itemized_orders_by_account table, replacing only those rows that have different values than the current version of the table, using accountID as the primary key.
B. An incremental job will leverage information in the state store to identify unjoined rows in the source tables and write these rows to the enriched_iteinized_orders_by_account table.
C. No computation will occur until enriched_itemized_orders_by_account is queried; upon query materialization, results will be calculated using the current valid version of data in each of the three tables referenced in the join logic.
D. The enriched_itemized_orders_by_account table will be overwritten using the current valid version of data in each of the three tables referenced in the join logic.
E. An incremental job will detect if new rows have been written to any of the source tables; if new rows are detected, all results will be recalculated and used to overwrite the enriched_itemized_orders_by_account table.
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 5：
A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?
A. Executor's log file
B. Ganglia
C. Workspace audit logs
D. Driver's log file
E. Cluster Event Log
正解：E
解説: (Topexam メンバーにのみ表示されます)

質問 6：
A data engineer is configuring a pipeline that will potentially see late-arriving, duplicate records.
In addition to de-duplicating records within the batch, which of the following approaches allows Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from the data engineer to deduplicate data against previously processed records as it is inserted into a Delta table?
A. Perform a full outer join on a unique key and overwrite existing data.
B. Rely on Delta Lake schema enforcement to prevent duplicate records.
C. Perform an insert-only merge with a matching condition on a unique key.
D. Set the configuration delta.deduplicate = true.
E. VACUUM the Delta table after each batch completes.
正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 7：
When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM's resources?
A. Bytes Received never exceeds 80 million bytes per second
B. The five Minute Load Average remains consistent/flat
C. Network I/O never spikes
D. Total Disk Space remains constant
E. CPU Utilization is around 75% Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
正解：E
解説: (Topexam メンバーにのみ表示されます)

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Databricks Certified Data Engineer Professional 認定 Databricks-Certified-Data-Engineer-Professional 試験問題:

1. The data engineering team maintains a table of aggregate statistics through batch nightly updates. This includes total sales for the previous day alongside totals and averages for a variety of time periods including the 7 previous days, year-to-date, and quarter-to-date. This table is named store_saies_summary and the schema is as follows:

The table daily_store_sales contains all the information needed to update store_sales_summary.
The schema for this table is:
store_id INT, sales_date DATE, total_sales FLOAT
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from If daily_store_sales is implemented as a Type 1 table and the total_sales column might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in the store_sales_summary table?

A) Use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update.
B) Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and overwrite the store_sales_summary table with each Update.
C) Implement the appropriate aggregate logic as a Structured Streaming read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
D) Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and append new rows nightly to the store_sales_summary table.
E) Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.

2. The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?

A) Add a parallel table write to the current production pipeline, updating a new sales table that varies Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from as required from marketing table.
B) Instruct the marketing team to download results as a CSV and email them to the sales organization.
C) Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.
D) Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.
E) Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.

3. Which REST API call can be used to review the notebooks configured to run as tasks in a multi- task job?

A) /jobs/get
B) /jobs/runs/get
C) /jobs/runs/list
D) /jobs/runs/get-output
E) /jobs/list

4. 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) Stop the existing pipeline; use the returned settings in a reset command
B) 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
C) 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
D) 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

5. A table is registered with the following code:
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Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

A) All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
B) Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
C) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
D) The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
E) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.

質問と回答：

質問 # 1
正解： B

質問 # 2
正解： C

質問 # 3
正解： A

質問 # 4
正解： B

質問 # 5
正解： A

Databricks-Certified-Data-Engineer-Professional 関連試験: Associate-Developer-Apache-Spark - Databricks Certified Associate Developer for Apache Spark 3.0 Exam; Databricks-Certified-Data-Engineer-Associate - Databricks Certified Data Engineer Associate Exam; Databricks-Certified-Professional-Data-Engineer - Databricks Certified Professional Data Engineer Exam; Databricks-Certified-Professional-Data-Scientist - Databricks Certified Professional Data Scientist Exam

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