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Databricks Certified Professional Data Engineer試験は、Databricksプラットフォームを使用するデータ専門家にとって貴重な認定です。Databricksを使用したデータエンジニアリングタスクにおける専門知識を示すことで、候補者はキャリアの見通しを向上させ、彼らの組織の成功に貢献することができます。

試験は、Databricksを使用して大規模なデータ処理パイプラインを設計、構築、および維持することに熟練したデータエンジニアが自分の専門知識を証明したい場合に最適です。認定試験はデータエンジニアの特定の役割と責任に合わせて調整されており、データインジェスション、データ変換、データストレージ、およびデータ分析などのトピックをカバーしています。試験に合格することで、候補者はDatabricksを使用して複雑なビッグデータ課題を解決する能力を証明できます。

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Databricks認定プロフェッショナルデータエンジニア試験は、世界中どこからでも受験できるオンライン試験です。試験は時間制限があり、複数の選択肢の問題と、Databricksを使用して特定のタスクを実行する能力を証明するための実践的な課題から構成されています。試験は、Databricksのデータエンジニアリングの概念と技術を徹底的に理解することが必要であり、難易度が高く設計されています。

Databricks Certified Professional Data Engineer Exam 認定 Databricks-Certified-Professional-Data-Engineer 試験問題 (Q181-Q186):

質問 # 181
A data engineer has set up two Jobs that each run nightly. The first Job starts at 12:00 AM, and it usually
completes in about 20 minutes. The second Job depends on the first Job, and it starts at 12:30 AM. Sometimes,
the second Job fails when the first Job does not complete by 12:30 AM.
Which of the following approaches can the data engineer use to avoid this problem?

A. They can set up a retry policy on the first Job to help it run more quickly
B. They can limit the size of the output in the second Job so that it will not fail as easily
C. They can set up the data to stream from the first Job to the second Job
D. They can use cluster pools to help the Jobs run more efficiently
E. They can utilize multiple tasks in a single job with a linear dependency

正解：E

質問 # 182
A data engineer has set up a notebook to automatically process using a Job. The data engineer's manager wants
to version control the schedule due to its complexity.
Which of the following approaches can the data engineer use to obtain a version-controllable con-figuration of
the Job's schedule?

A. They can submit the Job once on an all-purpose cluster
B. They can download the XML description of the Job from the Job's page
C. They can submit the Job once on a Job cluster
D. They can download the JSON description of the Job from the Job's page
E. They can link the Job to notebooks that are a part of a Databricks Repo

正解：D

質問 # 183
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?

A. Only workspace administrators can grant "Owner" privileges to a group.
B. Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.
C. The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.
D. A user can only transfer job ownership to a group if they are also a member of that group.
E. Other than the default "admins" group, only individual users can be granted privileges on jobs.

正解：B

解説：
Explanation
The reason why the junior data engineer cannot transfer "Owner" privileges to the "DevOps" group is that Databricks jobs must have exactly one owner, and the owner must be an individual user, not a group. A job cannot have more than one owner, and a job cannot have a group as an owner. The owner of a job is the user who created the job, or the user who was assigned the ownership by another user. The owner of a job has the highest level of permission on the job, and can grant or revoke permissions to other users or groups. However, the owner cannot transfer the ownership to a group, only to another user. Therefore, the junior data engineer's attempt to transfer "Owner" privileges to the "DevOps" group is not possible. References:
Jobs access control: https://docs.databricks.com/security/access-control/table-acls/index.html Job permissions:
https://docs.databricks.com/security/access-control/table-acls/privileges.html#job-permissions

質問 # 184
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 executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
B. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
C. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
D. 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.
E. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.

正解：A

解説：
https://www.databricks.com/blog/2020/08/31/introducing-the-databricks-web-terminal.html The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use the Databricks Connect feature to run your Python code on a remote Databricks cluster, or you can use the Spark Git Connector to read data from Git repositories as Spark DataFrames.

質問 # 185
A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings.
The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.
The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.
Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?

A. Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
B. By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.
C. Tungsten encoding used by Databricks is optimized for storing string data: newly-added native support for querying JSON strings means that string types are always most efficient.
D. Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.

正解：B

解説：
Delta Lake, built on top of Parquet, enhances query performance through data skipping, which is based on the statistics collected for each file in a table. For tables with a large number of columns, Delta Lake by default collects and stores statistics only for the first 32 columns. Thesestatistics include min/max values and null counts, which are used to optimize query execution by skipping irrelevant data files. When dealing with highly nested JSON structures, understanding this behavior is crucial for schema design, especially when determining which fields should be flattened or prioritized in the table structure to leverage data skipping efficiently for performance optimization.References: Databricks documentation on Delta Lake optimization techniques, including data skipping and statistics collection (https://docs.databricks.com/delta/optimizations
/index.html).

質問 # 186
......

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