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Databricks-Certified-Data-Engineer-Associate試験問題は正式に認定されています。私たちDatabricksの目標は、効率的な学習スタイルで、Databricks Certified Data Engineer Associate Exam関連するDatabricks-Certified-Data-Engineer-Associate試験に合格できるようにすることです。 Databricks-Certified-Data-Engineer-Associateトレーニング資料の品質と手頃な価格により、当社の競争力は常に世界のリーダーです。 Databricks-Certified-Data-Engineer-Associate学習教材は、他のトレーニング教材よりも高い合格率を持っているため、完全な結果を得ることができると確信しています。 Databricks-Certified-Data-Engineer-Associate試験問題を使用すると、Fast2test成功が保証されます。

Databricks Databricks-Certified-Data-Engineer-Associate 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Production Pipelines: It focuses on identifying the advantages of using multiple tasks in Jobs, a suitable scenario where predecessor task should be set up, CRON as an opportunity for scheduling opportunity, and how an alert can be sent via email. The topic also discusses setting up a predecessor task in Jobs, reviewing a task's execution history, and debugging a failed task. Lastly, it delves into setting up a retry policy in case of failure and creating an alert in the case of a failed task.
トピック 2	Data Governance: It identifies one of the four areas of data governance, Unity Catalog securables, and the cluster security modes. It also discusses how to create a UC-enabled all-purpose cluster and a DBSQL warehouse. The topic explains how to implement data object access control, create a DBSQL warehouse, and e a UC-enabled all-purpose cluster.
トピック 3	Databricks Lakehouse Platform: This topic covers the relationship between the data lakehouse and the data warehouse, the improvement in data quality, comparing and contrasting silver and gold tables, elements of the Databricks Platform Architecture, and differentiating between all-purpose clusters and jobs clusters. Moreover, it identifies how cluster software is versioned, how clusters can be filtered, how to use multiple languages, how to run one notebook, how notebooks can be shared, Git operations, and limitations in Databricks Notebooks. Lastly, the topic describes how clusters are terminated, how to use multiple languages, and how Databricks Repos enables CI CD workflows.
トピック 4	ELT with Apache Spark: It focuses on extracting data, identifying the prefix, creating a view, duplicating rows, creating a new table, utilizing the dot, parsing JSON, and defining a SQL UDF. Moreover, the topic delves into describing the security model, identifying the location of a function, and identifying the PIVOT.
トピック 5	Incremental Data Processing: In this topic questions about identifying Delta Lake, benefits of ACID transactions, a scenario to use an external table, location of a table, the benefits of Zordering, the kind of files, CTAS as a solution, the impact of ON VIOLATION DROP ROW and ON VIOLATION FAIL UPDATE, and the necessary component to create a new DLT pipeline. Moreover, the topic also discusses directory structure of Delta Lake files, generated column, adding a table comment, and the benefits of the MERGE command.

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Databricks Certified Data Engineer Associate Exam 認定 Databricks-Certified-Data-Engineer-Associate 試験問題 (Q102-Q107):

質問 # 102
In which of the following scenarios should a data engineer use the MERGE INTO command instead of the INSERT INTO command?

A. When the target table is an external table
B. When the target table cannot contain duplicate records
C. When the source table can be deleted
D. When the source is not a Delta table
E. When the location of the data needs to be changed

正解：B

質問 # 103
An organization has implemented a data pipeline in Databricks and needs to ensure it can scale automatically based on varying workloads without manual cluster management. The goal is to meet the company's Service Level Agreements (SLAs), which require high availability and minimal downtime, while Databricks automatically handles resource allocation and optimization.
Which approach fulfills these requirements?

A. Use interactive clusters in Databricks, adjusting cluster sizes manually based on workload demands
B. Use spot instances to allocate resources dynamically while minimizing costs, with potential interruptions
C. Use Serverless compute in Databricks to automatically scale and provision resources with minimal manual intervention
D. Deploy job clusters with fixed configurations, dedicated to specific tasks, without automatic scaling

正解：C

解説：
Databricks documentation recommends serverless compute as the simplest and most reliable compute option when the workload is supported. Serverless compute is designed to automatically provision resources, scale with demand, reduce infrastructure management, and apply platform optimizations without requiring users to configure clusters manually. This directly supports the requirement for automatic scaling, reduced operational overhead, and better alignment with strict SLAs. Databricks also states that serverless compute is always available and scales according to workload, making it a strong fit for organizations seeking high availability and minimal downtime. Fixed-configuration job clusters in option B still require manual sizing decisions and do not meet the "no manual cluster management" requirement. Spot instances in option C may reduce costs but can be interrupted, which makes them a poor choice when reliability is a top requirement.
Interactive clusters in option D are intended more for development and exploration and still need manual management. Based on Databricks guidance, serverless compute is the correct choice.
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質問 # 104
A data engineer needs access to a table new_table, but they do not have the correct permissions. They can ask the table owner for permission, but they do not know who the table owner is.
Which of the following approaches can be used to identify the owner of new_table?

A. There is no way to identify the owner of the table
B. All of these options can be used to identify the owner of the table
C. Review the Permissions tab in the table's page in Data Explorer
D. Review the Owner field in the table's page in Data Explorer
E. Review the Owner field in the table's page in the cloud storage solution

正解：D

質問 # 105
A Delta Live Table pipeline includes two datasets defined using streaming live table. Three datasets are defined against Delta Lake table sources using live table.
The table is configured to run in Production mode using the Continuous Pipeline Mode.
What is the expected outcome after clicking Start to update the pipeline assuming previously unprocessed data exists and all definitions are valid?

A. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
B. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped.
C. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
D. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.

正解：B

解説：
In Delta Live Tables (DLT), when configured to run in Continuous Pipeline Mode, particularly in a production environment, the system is designed to continuously process and update data as it becomes available. This mode keeps the compute resources active to handle ongoing data processing and automatically updates all datasets defined in the pipeline at predefined intervals. Once the pipeline is manually stopped, the compute resources are terminated to conserve resources and reduce costs. This mode is suitable for production environments where datasets need to be kept up-to-date with the latest data.
Reference:
Databricks documentation on Delta Live Tables: Delta Live Tables Guide

質問 # 106
A data architect has determined that a table of the following format is necessary:

Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above format regardless of whether a table already exists with this name?

正解：C

質問 # 107
......

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