Databricks-Certified-Professional-Data-Engineer試験 & Databricks-Certified-Professional-Data-Engineerダウンロード

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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、DataBricksプラットフォーム上のデータパイプラインの構築と最適化のスキルを検証したいデータ専門家向けに設計されています。この試験は、DataBrickを使用して堅牢でスケーラブルで効率的なデータパイプラインの設計、構築、および維持に必要なデータエンジニアリングの原則、ベストプラクティス、および実用的なスキルを包括的に理解することを目的としています。この認定試験は世界的に認識されており、雇用市場で競争力を提供します。

Databricks Certified Professional Data Engineer になるには、データエンジニアリングに関連する幅広いトピックをカバーする厳しい試験に合格する必要があります。この試験は、Databricks ツールやテクノロジーを使用してデータパイプラインを設計、構築、維持する能力をテストするとともに、データモデリング、データウェアハウジング、データ統合の理解もテストします。

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Databricks Databricks-Certified-Professional-Data-Engineerダウンロード & Databricks-Certified-Professional-Data-Engineerファンデーション

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Databricks Certified Professional Data Engineer（Databricks-Certified-Professional-Data-Engineer）試験は、Databricksを使用してビッグデータパイプラインを開発および管理するデータエンジニアのスキルと専門知識を検証するために設計された認定プログラムです。この試験は、Databricksで作業するデータエンジニア、ETL開発者、およびデータアーキテクトに最適であり、彼らのスキルと熟練度をアピールすることができます。

Databricks Certified Professional Data Engineer Exam 認定 Databricks-Certified-Professional-Data-Engineer 試験問題 (Q121-Q126):

質問 # 121
The data engineer team has been tasked with configured connections to an external database that does not have a supported native connector with Databricks. The external database already has data security configured by group membership. These groups map directly to user group already created in Databricks that represent various teams within the company.
A new login credential has been created for each group in the external database. The Databricks Utilities Secrets module will be used to make these credentials available to Databricks users.
Assuming that all the credentials are configured correctly on the external database and group membership is properly configured on Databricks, which statement describes how teams can be granted the minimum necessary access to using these credentials?

A. No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.
B. "Read" permissions should be set on a secret scope containing only those credentials that will be used by a given team.
C. "Manage" permission should be set on a secret scope containing only those credentials that will be used by a given team.
D. ''Read'' permissions should be set on a secret key mapped to those credentials that will be used by a given team.

正解：B

質問 # 122
A data team's Structured Streaming job is configured to calculate running aggregates for item sales to update a downstream marketing dashboard. The marketing team has introduced a new field to track the number of times this promotion code is used for each item. A junior data engineer suggests updating the existing query as follows: Note that proposed changes are in bold.

Which step must also be completed to put the proposed query into production?

A. Specify a new checkpointlocation
B. Increase the shuffle partitions to account for additional aggregates
C. Run REFRESH TABLE delta, /item_agg'
D. Remove .option (mergeSchema', true') from the streaming write

正解：A

解説：
When introducing a new aggregation or a change in the logic of a Structured Streaming query, it is generally necessary to specify a new checkpoint location. This is because the checkpoint directory contains metadata about the offsets and the state of the aggregations of a streaming query. If the logic of the query changes, such as including a new aggregation field, the state information saved in the current checkpoint would not be compatible with the new logic, potentially leading to incorrect results or failures. Therefore, to accommodate the new field and ensure the streaming job has the correct starting point and state information for aggregations, a new checkpoint location should be specified.
References:
* Databricks documentation on Structured Streaming: https://docs.databricks.com/spark/latest/structured- streaming/index.html
* Databricks documentation on streaming checkpoints: https://docs.databricks.com/spark/latest/structured- streaming/production.html#checkpointing

質問 # 123
A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device.
Bgm_stats incrementally computes user statistics based on BPM measurements from raw_lot.
How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?

A. Set the pipelines, reset, allowed property to false on bpm_stats
B. Set the SkipChangeCommits flag to true raw_lot
C. Set the pipelines, reset, allowed property to false on raw_iot
D. Set the skipChangeCommits flag to true on bpm_stats

正解：C

解説：
In Databricks Lakehouse, to retain manually deleted or updated records in the raw_iot table while recomputing downstream tables when a pipeline update is run, the property pipelines.reset.allowed should be set to false. This property prevents the system from resetting the state of the table, which includes the removal of the history of changes, during a pipeline update. By keeping this property as false, any changes to the raw_iot table, including manual deletes or updates, are retained, and recomputation of downstream tables, such as bpm_stats, can occur with the full history of data changes intact.
Reference:
Databricks documentation on DLT pipelines: https://docs.databricks.com/data-engineering/delta-live-tables/delta-live-tables-overview.html

質問 # 124
Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code.
Which statement describes a main benefit that offset this additional effort?

A. Ensures that all steps interact correctly to achieve the desired end result
B. Yields faster deployment and execution times
C. Troubleshooting is easier since all steps are isolated and tested individually
D. Validates a complete use case of your application
E. Improves the quality of your data

正解：E

質問 # 125
What is the purpose of the bronze layer in a Multi-hop architecture?

A. Contains aggregated data that is to be consumed into Silver
B. Used as a data source for Machine learning applications.
C. Can be used to eliminate duplicate records
D. Provides efficient storage and querying of full unprocessed history of data
E. Perform data quality checks, corrupt data quarantined

正解：D

解説：
Explanation
The answer is Provides efficient storage and querying of full unprocessed history of data Medallion Architecture - Databricks Bronze Layer:
1.Raw copy of ingested data
2.Replaces traditional data lake
3.Provides efficient storage and querying of full, unprocessed history of data
4.No schema is applied at this layer
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.

質問 # 126
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