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Databricks Certified Data Engineer Associate 認定は、業界で広く認められ、雇用主から高く評価されています。この認定を取得することは、Databricks プラットフォーム上でデータソリューションを構築および維持するために必要なスキルと知識を持っていることを雇用主に示すことを意味します。この認定は、プロフェッショナルがキャリアを進め、収益力を高めるのに役立ちます。

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GAQM Databricks-Certified-Data-Engineer-Associate（DataBricks Certified Data Engineer Associate）認定試験は、データアブリックプラットフォームとデータエンジニアリングにおけるその役割についての候補者の理解を検証する包括的かつ厳密な試験です。この認定は、DataBricksを使用してデータパイプラインの設計、構築、維持に必要な実用的なスキルと知識の評価に焦点を当てています。

Databricks Certified Data Engineer Associate Exam 認定 Databricks-Certified-Data-Engineer-Associate 試験問題 (Q77-Q82):

質問 # 77
What is stored in a Databricks customer's cloud account?

A. Data
B. Notebooks
C. Databricks web application
D. Cluster management metadata

正解：A

解説：
In a Databricks customer's cloud account, the primary elements stored include:
Data: This is the central type of content stored in the customer's cloud account. Data might include various datasets, tables, and files that are used and managed through Databricks platforms.
Notebooks: These are also stored within a customer's cloud account. Notebooks include scripts, notes, and other information necessary for data analysis and processing tasks.
Cluster management metadata is indeed managed through the cloud, but it's primarily handled by Databricks rather than stored directly in the customer's account. The Databricks web application itself is not stored within the customer's cloud account; rather, it's a service provided by Databricks.
Reference:
Databricks documentation: Data in Databricks

質問 # 78
A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to an ELT job. The ELT job has its Databricks SQL query that returns the number of input records containing unexpected NULL values. The data engineer wants their entire team to be notified via a messaging webhook whenever this value reaches 100.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of NULL values reaches 100?

A. They can set up an Alert with a new email alert destination.
B. They can set up an Alert without notifications.
C. They can set up an Alert with a custom template.
D. They can set up an Alert with a new webhook alert destination.
E. They can set up an Alert with one-time notifications.

正解：D

質問 # 79
A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

A. sqlite
B. autoloader
C. org.apache.spark.sql.jdbc
D. DELTA
E. org.apache.spark.sql.sqlite

正解：A

解説：
In the given command, a data engineer is trying to create a table in Databricks using data from an SQLite database. The correct option to fill in the blank is "sqlite" because it specifies the type of database being connected to in a JDBC connection string. The USING clause should be followed by the format of the data, and since we are connecting to an SQLite database, "sqlite" would be appropriate here. Reference:
Create a table using JDBC
JDBC connection string
SQLite JDBC driver

質問 # 80
A data engineer and data analyst are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL. The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables. Which change will need to be made to the pipeline when migrating to Delta Live Tables?

A. The pipeline will need to be written entirely in SQL.
B. The pipeline will need to be written entirely in Python.
C. The pipeline will need to use a batch source in place of a streaming source.
D. The pipeline can have different notebook sources in SQL & Python.

正解：D

解説：
When migrating to Delta Live Tables (DLT), you can use both SQL and Python in the same pipeline. DLT supports multi-language notebooks, allowing data engineers to continue using Python for certain transformations and data analysts to use SQL for their specific requirements.

質問 # 81
A data engineer is reading a very large dataset that contains skewed key values. Some partitions contain significantly more records than others, causing uneven workload distribution across executors. Which Spark issue does this scenario describe?

A. Broadcast failure
B. Lazy evaluation
C. Schema evolution
D. Data skew

正解：D

質問 # 82
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