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

質問 1：
What is the first of a Databricks Python notebook when viewed in a text editor?
A. //Databricks notebook source
B. % Databricks notebook source
C. %python
D. -- Databricks notebook source
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 2：
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. 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.
D. New files are identified by listing the input directory; new files are incrementally and idempotently loaded into the target Delta Lake table.
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 3：
To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries.
The data engineering team has been made aware of new requirements from a customer-facing application, which is the only downstream workload they manage entirely. As a result, an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added.
Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?
A. Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application.
B. Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer-facing application.
C. Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries.
D. Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.
E. Create a new table with the required schema and new fields and use Delta Lake's deep clone functionality to sync up changes committed to one table to the corresponding table.
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 4：
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
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 5：
The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over 20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?
A. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
B. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
C. Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
D. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
E. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 6：
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. Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.
B. Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.
C. Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.
D. Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.
正解：D
解説: (Topexam メンバーにのみ表示されます)