最新なDatabricks Databricks-Certified-Data-Engineer-Professional問題集(127題)、真実試験の問題を全部にカバー!

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  • 試験コード:Databricks-Certified-Data-Engineer-Professional
  • 試験名称:Databricks Certified Data Engineer Professional Exam
  • 問題数:127 問題と回答
  • 最近更新時間:2025-06-27
  • PDF版 Demo
  • PC ソフト版 Demo
  • オンライン版 Demo
  • 価格:12900.00 5999.00  
質問 1:
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: (Pass4Test メンバーにのみ表示されます)

質問 2:
A Delta Lake table was created with the below query:

Consider the following query:
DROP TABLE prod.sales_by_store
If this statement is executed by a workspace admin, which result will occur?
A. An error will occur because Delta Lake prevents the deletion of production data.
B. The table will be removed from the catalog but the data will remain in storage.
C. The table will be removed from the catalog and the data will be deleted.
D. Nothing will occur until a COMMIT command is executed.
E. Data will be marked as deleted but still recoverable with Time Travel.
正解:C
解説: (Pass4Test メンバーにのみ表示されます)

質問 3:
Which statement characterizes the general programming model used by Spark Structured Streaming?
A. Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.
B. Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.
C. Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.
D. Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.
E. Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.
正解:A
解説: (Pass4Test メンバーにのみ表示されます)

質問 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 alone command to create a copy of an existing pipeline; use the get JSON command to get the pipeline definition; save this to git
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 get command to capture the settings for the existing pipeline; remove the pipeline_id and rename the pipeline; use this in a create command
正解:D
解説: (Pass4Test メンバーにのみ表示されます)

質問 5:
The data engineering team has configured a job to process customer requests to be forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta Lake tables using default table settings.
The team has decided to process all deletions from the previous week as a batch job at 1am each Sunday. The total duration of this job is less than one hour. Every Monday at 3am, a batch job executes a series of VACUUM commands on all Delta Lake tables throughout the organization.
The compliance officer has recently learned about Delta Lake's time travel functionality. They are concerned that this might allow continued access to deleted data.
Assuming all delete logic is correctly implemented, which statement correctly addresses this concern?
A. Because Delta Lake time travel provides full access to the entire history of a table, deleted records can always be recreated by users with full admin privileges.
B. Because the default data retention threshold is 24 hours, data files containing deleted records will be retained until the vacuum job is run the following day.
C. Because the default data retention threshold is 7 days, data files containing deleted records will be retained until the vacuum job is run 8 days later.Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
D. Because the vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours.
E. Because Delta Lake's delete statements have ACID guarantees, deleted records will be permanently purged from all storage systems as soon as a delete job completes.
正解:C
解説: (Pass4Test メンバーにのみ表示されます)

質問 6:
Which statement describes the default execution mode for Databricks Auto Loader?
A. New files are identified by listing the input directory; the target table is materialized by directory querying all valid files in the source directory.
B. Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; new files are incrementally and impotently into the target Delta Lake table.
C. Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; the target table is materialized by directly querying all valid files in the source directory.
D. New files are identified by listing the input directory; new files are incrementally and idempotently loaded into the target Delta Lake table.
E. Webhook trigger Databricks job to run anytime new data arrives in a source directory; new data automatically merged into target tables using rules inferred from the data.
正解:D
解説: (Pass4Test メンバーにのみ表示されます)

質問 7:
A table is registered with the following code:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?
A. 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.
B. 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.
C. 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.
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.
正解:B
解説: (Pass4Test メンバーにのみ表示されます)

質問 8:
A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from part- file size of 512 MB. Because Parquet is being used instead of Delta Lake, built-in file-sizing features such as Auto-Optimize & Auto-Compaction cannot be used.
Which strategy will yield the best performance without shuffling data?
A. Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.
B. Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.
C. Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.
D. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.
E. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB*
1024*1024/512), and then write to parquet.
正解:A
解説: (Pass4Test メンバーにのみ表示されます)

質問 9:
A Delta Lake table was created with the below query:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

Realizing that the original query had a typographical error, the below code was executed:
ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
Which result will occur after running the second command?
A. A new Delta transaction log Is created for the renamed table.
B. The table reference in the metastore is updated and no data is changed.
C. All related files and metadata are dropped and recreated in a single ACID transaction.
D. The table reference in the metastore is updated and all data files are moved.
E. The table name change is recorded in the Delta transaction log.
正解:B
解説: (Pass4Test 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. A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.
Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

A) Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.
B) The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.
C) Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.
D) Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored.
E) The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.


2. A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

A) Databricks supports Spark SQL and JDBC; all logic can be directly migrated from the source system without refactoring.
B) Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.
C) Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
D) All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
E) Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.


3. A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?

A) Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.
B) Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.
C) Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.
D) Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.


4. Which statement describes Delta Lake optimized writes?

A) An asynchronous job runs after the write completes to detect if files could be further compacted; yes, an OPTIMIZE job is executed toward a default of 1 GB.
B) A shuffle occurs prior to writing to try to group data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.
C) Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.
D) Optimized writes logical partitions instead of directory partitions partition boundaries are only Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from represented in metadata fewer small files are written.


5. A user wants to use DLT expectations to validate that a derived table report contains all records from the source, included in the table validation_copy.
The user attempts and fails to accomplish this by adding an expectation to the report table definition.
Which approach would allow using DLT expectations to validate all expected records are present in this table?

A) Define a temporary table that perform a left outer join on validation_copy and report, and define an expectation that no report key values are null
B) Define a view that performs a left outer join on validation_copy and report, and reference this view in DLT expectations for the report table
C) Define a SQL UDF that performs a left outer join on two tables, and check if this returns null values for report key values in a DLT expectation for the report table.
D) Define a function that performs a left outer join on validation_copy and report and report, and check against the result in a DLT expectation for the report table


質問と回答:

質問 # 1
正解: E
質問 # 2
正解: D
質問 # 3
正解: C
質問 # 4
正解: B
質問 # 5
正解: B

1401 お客様のコメント最新のコメント

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