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Databricks Databricks-Certified-Data-Engineer-Professional 問題集

Databricks-Certified-Data-Engineer-Professional

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:2025-09-14

問題と解答:全127問

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質問 1:
What is a method of installing a Python package scoped at the notebook level to all nodes in the currently active cluster?
A. Run source env/bin/activate in a notebook setup script
B. Install libraries from PyPi using the cluster UI
C. Use &Pip install in a notebook cell
D. Use &sh install in a notebook cell
正解:B
解説: (Topexam メンバーにのみ表示されます)

質問 2:
A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.
Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.
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Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?
A. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.
B. Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af: if this entity exists, Cmd 2 will succeed.
C. Both commands will fail. No new variables, tables, or views will be created.
D. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.
E. Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
正解:D
解説: (Topexam メンバーにのみ表示されます)

質問 3:
A Data engineer wants to run unit's tests using common Python testing frameworks on python functions defined across several Databricks notebooks currently used in production. How can the data engineer run unit tests against function that work with data in production?
A. Define and unit test functions using Files in Repos
B. Run unit tests against non-production data that closely mirrors production
C. Define units test and functions within the same notebook
D. Define and import unit test functions from a separate Databricks notebook
正解:B
解説: (Topexam メンバーにのみ表示されます)

質問 4:
The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:

Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?
A. Define a view against the products_per_order table and define the dashboard against this view.
B. Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.
C. Configure a webhook to execute an incremental read against products_per_order each time the dashboard is refreshed.
D. Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.
E. Populate the dashboard by configuring a nightly batch job to save the required to quickly update the dashboard with each query.
正解:E

質問 5:
The view updates represents an incremental batch of all newly ingested data to be inserted or Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from updated in the customers table.
The following logic is used to process these records.
MERGE INTO customers
USING (
SELECT updates.customer_id as merge_ey, updates .*
FROM updates
UNION ALL
SELECT NULL as merge_key, updates .*
FROM updates JOIN customers
ON updates.customer_id = customers.customer_id
WHERE customers.current = true AND updates.address <> customers.address ) staged_updates ON customers.customer_id = mergekey WHEN MATCHED AND customers. current = true AND customers.address <> staged_updates.address THEN UPDATE SET current = false, end_date = staged_updates.effective_date WHEN NOT MATCHED THEN INSERT (customer_id, address, current, effective_date, end_date) VALUES (staged_updates.customer_id, staged_updates.address, true, staged_updates.effective_date, null) Which statement describes this implementation?
A. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
B. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
C. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
D. The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
正解:D
解説: (Topexam メンバーにのみ表示されます)

質問 6:
A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI.
Upon reviewing their work, the engineer realizes that they are listed as the "Owner" for each job.
They attempt to transfer "Owner" privileges to the "DevOps" group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?
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A. Other than the default "admins" group, only individual users can be granted privileges on jobs.
B. A user can only transfer job ownership to a group if they are also a member of that group.
C. Only workspace administrators can grant "Owner" privileges to a group.
D. Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.
E. The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.
正解:D
解説: (Topexam メンバーにのみ表示されます)

質問 7:
A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on task A.
If tasks A and B complete successfully but task C fails during a scheduled run, which statement describes the resulting state?
A. All logic expressed in the notebook associated with tasks A and B will have been successfully completed; any changes made in task C will be rolled back due to task failure.
B. All logic expressed in the notebook associated with tasks A and B will have been successfully completed; some operations in task C may have completed successfully.
C. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until ail tasks have successfully been completed.
D. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task C failed, all commits will be rolled back automatically.
E. All logic expressed in the notebook associated with task A will have been successfully completed; tasks B and C will not commit any changes because of stage failure.
正解:B
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 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.


2. A junior developer complains that the code in their notebook isn't producing the correct results in the development environment. A shared screenshot reveals that while they're using a notebook versioned with Databricks Repos, they're using a personal branch that contains old logic. The desired branch named dev-2.3.9 is not available from the branch selection dropdown.
Which approach will allow this developer to review the current logic for this notebook?

A) Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9
B) Merge all changes back to the main branch in the remote Git repository and clone the repo again
C) Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch
D) Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.
E) Use Repos to merge the current branch and the dev-2.3.9 branch, then make a pull request to sync with the remote repository


3. A large company seeks to implement a near real-time solution involving hundreds of pipelines with parallel updates of many tables with extremely high volume and high velocity data.
Which of the following solutions would you implement to achieve this requirement?

A) Partition ingestion tables by a small time duration to allow for many data files to be written in parallel.
B) Configure Databricks to save all data to attached SSD volumes instead of object storage, increasing file I/O significantly.
C) Isolate Delta Lake tables in their own storage containers to avoid API limits imposed by cloud vendors.
D) Use Databricks High Concurrency clusters, which leverage optimized cloud storage connections to maximize data throughput.
E) Store all tables in a single database to ensure that the Databricks Catalyst Metastore can load balance overall throughput.


4. The data architect has mandated that all tables in the Lakehouse should be configured as external (also known as "unmanaged") Delta Lake tables.
Which approach will ensure that this requirement is met?

A) When tables are created, make sure that the EXTERNAL keyword is used in the CREATE TABLE statement.
B) When configuring an external data warehouse for all table storage, leverage Databricks for all ELT.
C) When a database is being created, make sure that the LOCATION keyword is used.
D) When the workspace is being configured, make sure that external cloud object storage has been mounted.
E) When data is saved to a table, make sure that a full file path is specified alongside the Delta format.


5. The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs UI. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.
What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

A) Can Run
B) Can Edit
C) Can Read
D) No permissions
E) Can Manage


質問と回答:

質問 # 1
正解: C
質問 # 2
正解: D
質問 # 3
正解: D
質問 # 4
正解: A
質問 # 5
正解: C

Databricks-Certified-Data-Engineer-Professional 関連試験
Databricks-Certified-Data-Engineer-Associate-JPN - Databricks Certified Data Engineer Associate Exam (Databricks-Certified-Data-Engineer-Associate日本語版)
Associate-Developer-Apache-Spark - Databricks Certified Associate Developer for Apache Spark 3.0 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-3.5 - Databricks Certified Associate Developer for Apache Spark 3.5 - Python
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