効率的な学習計画

実際のところ、長時間の勉強は必要ではありませんが、質の高い効率的な学習が成功するための鍵となる方法です。 複雑な知識を単純化し、学習内容を習得しやすいようにDatabricks-Certified-Professional-Data-Engineerテスト連続をいくつか用意しています。貴重な時間を短縮しながら、より重要な知識を得ることができます。 Databricks Certified Professional Data Engineer Examガイド連続は時間管理とシミュレーションテスト機能を備えています、それはスピードを調整して効率を改善するために警戒を続けるのを助けるために時間管理者をセットアップするのにとても役に立ちます。 私たちのエキスパートチームはDatabricks-Certified-Professional-Data-Engineer認証トレーニングで試験を準備するためにあなたが20-30時間しか必要としないことを高効率のトレーニングプロセスを設計しました。 全体的な20-30時間のトレーニング計画で、Databricks-Certified-Professional-Data-Engineerテスト連続であなたが一日にどのくらいの時間を費やすつもりであるかについて思い出させるために自分自身を思い出させるために小さなやるリストを作ることもできます。

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Databricks Certified Professional Data Engineer Exam認定を取得したいという意欲のある人には、主にサラリーマンが含まれます。 彼らはより高い地位に到達し、さらに、ハンサムな給料を得ることを期待しています、さらに、繁栄した未来。 Databricks-Certified-Professional-Data-Engineerテスト連続を通して、さらなる発展のための高効率的な学習態度を構築するのを助けるようにそのような効率的な研究計画を設計することを期待します。 学習教材は、学生やオフィスワーカー、長年の経験を持つグリーンハンド、またはスタッフメンバーに関係なく、すべての候補者を対象としています。Databricks-Certified-Professional-Data-Engineer認定トレーニングは、あなたにとって絶対に良い選択です。 したがって、試験に合格することができるかどうかを心配する必要はありません。これは、弊社の技術力で成功することを保証するためです。

Databricks Certified Professional Data Engineer 認定 Databricks-Certified-Professional-Data-Engineer 試験問題:

1. 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) Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task C failed, all commits will be rolled back automatically.
D) 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.
E) Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until ail tasks have successfully been completed.

2. An external object storage container has been mounted to the location /mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:

After the database was successfully created and permissions configured, a member of the finance team runs the following code:

If all users on the finance team are members of the finance group, which statement describes how the tx_sales table will be created?

A) A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.
B) A managed table will be created in the DBFS root storage container.
C) A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.
D) An managed table will be created in the storage container mounted to /mnt/finance eda bucket.
E) An external table will be created in the storage container mounted to /mnt/finance eda bucket.

3. A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

A) slidingWindow("event_time", "10 minutes")
B) await("event_time + '10 minutes'")
C) awaitArrival("event_time", "10 minutes")
D) delayWrite("event_time", "10 minutes")
E) withWatermark("event_time", "10 minutes")

4. The data engineer team is configuring environment for development testing, and production before beginning migration on a new data pipeline. The team requires extensive testing on both the code and data resulting from code execution, and the team want to develop and test against similar production data as possible.
A junior data engineer suggests that production data can be mounted to the development testing environments, allowing pre production code to execute against production dat a. Because all users have Admin privileges in the development environment, the junior data engineer has offered to configure permissions and mount this data for the team.
Which statement captures best practices for this situation?

A) Because delta Lake versions all data and supports time travel, it is not possible for user error or malicious actors to permanently delete production data, as such it is generally safe to mount production data anywhere.
B) Because access to production data will always be verified using passthrough credentials it is safe to mount data to any Databricks development environment.
C) In environments where interactive code will be executed, production data should only be accessible with read permissions; creating isolated databases for each environment further reduces risks.
D) All developer, testing and production code and data should exist in a single unified workspace; creating separate environments for testing and development further reduces risks.

5. 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) 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.
B) 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.
C) 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.
D) 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.
E) 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.

質問と回答：

質問 # 1 正解： B

質問 # 2 正解： D

質問 # 3 正解： E

質問 # 4 正解： C

質問 # 5 正解： C