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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：2025-04-02

問題と解答：全127問

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質問 1：
Which statement describes integration testing?
A. Validates an application use case
B. Validates behavior of individual elements of your application
C. Validates interactions between subsystems of your application
D. Requires manual intervention
E. Requires an automated testing framework
正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 2：
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 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.
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 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
D. 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
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 3：
What is the first of a Databricks Python notebook when viewed in a text editor?
A. %python
B. # Databricks notebook source
C. -- Databricks notebook source
D. # MAGIC %python
E. // Databricks notebook source
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 4：
Which statement describes a key benefit of an end-to-end test?
A. It pinpoint errors in the building blocks of your application.
B. It provides testing coverage for all code paths and branches.
C. It makes it easier to automate your test suite
D. It closely simulates real world usage of your application.
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 5：
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. Data will be marked as deleted but still recoverable with Time Travel.
C. Nothing will occur until a COMMIT command is executed.
D. The table will be removed from the catalog but the data will remain in storage.
E. The table will be removed from the catalog and the data will be deleted.
正解：E
解説: (Topexam メンバーにのみ表示されます)

質問 6：
Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?
A. libraries
B. configure
C. workspace
D. fs
E. jobs
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 7：
A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:

A batch job is attempting to insert new records to the table, including a record where latitude =
45.50 and longitude = 212.67.
Which statement describes the outcome of this batch insert?
A. The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.
B. The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.
C. The write will fail when the violating record is reached; any records previously processed will be recorded to the target table.
D. The write will fail completely because of the constraint violation and no records will be inserted into the target table.
E. The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.
正解：D
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. 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.files.maxPartitionBytes to 512 MB, 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) Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB*
1024*1024/512), and then write to parquet.
E) Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.

2. A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?

A) Databricks has autotuned to a smaller target file size based on the overall size of data in the table
B) Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
C) Databricks has autotuned to a smaller target file size based on the amount of data in each partition
D) Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations

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

4. Which statement describes the default execution mode for Databricks Auto Loader?

A) 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.
B) New files are identified by listing the input directory; the target table is materialized by directory querying all valid files in the source directory.
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) 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.
E) New files are identified by listing the input directory; new files are incrementally and idempotently loaded into the target Delta Lake table.

5. The data science team has created and logged a production model using MLflow. The model accepts a list of column names and returns a new column of type DOUBLE.
The following code correctly imports the production model, loads the customers table containing the customer_id key column into a DataFrame, and defines the feature columns needed for the model.

Which code block will output a DataFrame with the schema "customer_id LONG, predictions DOUBLE"?

A) df.select("customer_id", pandas_udf(model, columns).alias("predictions"))
B) model.predict(df, columns)
C) df.select("customer_id", model(*columns).alias("predictions"))
D) df.apply(model, columns).select("customer_id, predictions")
E) df.map(lambda x:model(x[columns])).select("customer_id, predictions")

質問と回答：

質問 # 1
正解： A

質問 # 2
正解： D

質問 # 3
正解： C

質問 # 4
正解： E

質問 # 5
正解： C

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

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