Databricks-Certified-Data-Engineer-Associate Databricks Certified Data Engineer Associate Exam Practice Test

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Total 109 questions
Question 1

A data engineer has left the organization. The data team needs to transfer ownership of the data engineer's Delta tables to a new data engineer. The new data engineer is the lead engineer on the data team.

Assuming the original data engineer no longer has access, which of the following individuals must be the one to transfer ownership of the Delta tables in Data Explorer?



Answer : C

The workspace administrator is the only individual who can transfer ownership of the Delta tables in Data Explorer, assuming the original data engineer no longer has access. The workspace administrator has the highest level of permissions in the workspace and can manage all resources, users, and groups. The other options are either not possible or not sufficient to perform the ownership transfer. The Databricks account representative is not involved in the workspace management. The transfer is possible and not dependent on the original data engineer. The new lead data engineer may not have the necessary permissions to access or modify the Delta tables, unless granted by the workspace administrator or the original data engineer before leaving.Reference:Workspace access control,Manage Unity Catalog object ownership.


Question 2

A data engineer wants to create a new table containing the names of customers that live in France.

They have written the following command:

A senior data engineer mentions that it is organization policy to include a table property indicating that the new table includes personally identifiable information (PII).

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



Answer : D

In Databricks, when creating a table, you can add a comment to columns or the entire table to provide more information about the data it contains. In this case, since it's organization policy to indicate that the new table includes personally identifiable information (PII), option D is correct. The line of code would be added after defining the table structure and before closing with a semicolon.Reference:Data Engineer Associate Exam Guide,CREATE TABLE USING (Databricks SQL)


Question 3

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 Development mode using the Continuous Pipeline Mode.

Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?



Answer : B

The Continuous Pipeline Mode for Delta Live Tables allows the pipeline to run continuously and process data as it arrives. This mode is suitable for streaming ingest and CDC workloads that require low-latency updates. The Development mode for Delta Live Tables allows the pipeline to run on a dedicated cluster that is not shared with other pipelines. This mode is useful for testing and debugging the pipeline logic before deploying it to production. Therefore, the correct answer is B, because the pipeline will run continuously on a dedicated cluster until it is manually stopped, and the compute resources will be released only after the pipeline is shut down.Reference:Databricks Documentation - Configure pipeline settings for Delta Live Tables,Databricks Documentation - Continuous vs. triggered pipeline execution,Databricks Documentation - Development vs. production mode.


Question 4

A data organization leader is upset about the data analysis team's reports being different from the data engineering team's reports. The leader believes the siloed nature of their organization's data engineering and data analysis architectures is to blame.

Which of the following describes how a data lakehouse could alleviate this issue?



Answer : B

A data lakehouse is a data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data12. By using a data lakehouse, both the data analysis and data engineering teams can access the same data sources and formats, ensuring data consistency and quality across their reports.A data lakehouse also supports schema enforcement and evolution, data validation, and time travel to old table versions, which can help resolve data conflicts and errors1.Reference:1:What is a Data Lakehouse? - Databricks2:What is a data lakehouse? | IBM


Question 5

In which of the following file formats is data from Delta Lake tables primarily stored?



Answer : C

Delta Lake is an open source project that provides ACID transactions, time travel, and other features on top of Apache Parquet, a columnar file format that is widely used for big data analytics. Delta Lake uses versioned Parquet files to store your data in your cloud storage, along with JSON files as transaction logs and checkpoint files to track the changes and ensure data integrity. Delta Lake is compatible with any Apache Hive compatible file format, such as CSV, JSON, or AVRO, but it primarily stores data as Parquet files for better performance and compression.Reference:How to Create Delta Lake tables,5 reasons to choose Delta Lake format (on Databricks),Parquet vs Delta format in Azure Data Lake Gen 2 store,What is Delta Lake? - Azure Databricks,Lakehouse and Delta tables - Microsoft Fabric


Question 6

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



Answer : A

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


Question 7

Which of the following describes a scenario in which a data team will want to utilize cluster pools?



Answer : A

Databricks cluster pools are a set of idle, ready-to-use instances that can reduce cluster start and auto-scaling times. This is useful for scenarios where a data team needs to run an automated report as quickly as possible, without waiting for the cluster to launch or scale up. Cluster pools can also help save costs by reusing idle instances across different clusters and avoiding DBU charges for idle instances in the pool.Reference:Best practices: pools | Databricks on AWS,Best practices: pools - Azure Databricks | Microsoft Learn,Best practices: pools | Databricks on Google Cloud


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Total 109 questions