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

Question 1

A data engineer needs access to a table new_uable, 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 approach can be used to identify the owner of new_table?

AThere is no way to identify the owner of the table

BReview the Owner field in the table's page in the cloud storage solution

CReview the Permissions tab in the table's page in Data Explorer

DReview the Owner field in the table's page in Data Explorer

Answer : D

To find the owner of a table in Databricks, one can utilize the Data Explorer feature. The Data Explorer provides detailed information about various data objects, including tables. By navigating to the specific table's page in Data Explorer, a data engineer can review the Owner field, which identifies the individual or role that owns the table. This information is crucial for obtaining the necessary permissions or for any administrative actions related to the table.

Reference: Databricks documentation on Data Explorer: Using Data Explorer in Databricks

Question 2

A data engineer wants to schedule their Databricks SQL dashboard to refresh every hour, but they only want the associated SQL endpoint to be running when It is necessary. The dashboard has multiple queries on multiple datasets associated with it. The data that feeds the dashboard is automatically processed using a Databricks Job.

Which approach can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

AO They can reduce the cluster size of the SQL endpoint.

BQ They can turn on the Auto Stop feature for the SQL endpoint.

CO They can set up the dashboard's SQL endpoint to be serverless.

D0 They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.

Answer : B

To minimize the total running time of the SQL endpoint used in the refresh schedule of a dashboard in Databricks, the most effective approach is to utilize the Auto Stop feature. This feature allows the SQL endpoint to automatically stop after a period of inactivity, ensuring that it only runs when necessary, such as during the dashboard refresh or when actively queried. This minimizes resource usage and associated costs by ensuring the SQL endpoint is not running idle outside of these operations.

Reference: Databricks documentation on SQL endpoints: SQL Endpoints in Databricks

Question 3

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?

AThe pipeline can have different notebook sources in SQL & Python.

BThe pipeline will need to be written entirely in SQL.

CThe pipeline will need to be written entirely in Python.

DThe pipeline will need to use a batch source in place of a streaming source.

Answer : A

When migrating to Delta Live Tables (DLT) with a data pipeline that involves different programming languages across various data layers, the migration does not require unifying the pipeline into a single language. Delta Live Tables support multi-language pipelines, allowing data engineers and data analysts to work in their preferred languages, such as Python for data engineering tasks (raw, bronze, and silver layers) and SQL for data analytics tasks (gold layer). This capability is particularly beneficial in collaborative settings and leverages the strengths of each language for different stages of data processing.

Reference: Databricks documentation on Delta Live Tables: Delta Live Tables Guide

Question 4

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?

AAll datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.

BAll datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.

CAll datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.

DAll 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.

Answer : D

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

Question 5

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

AData

BCluster management metadata

CDatabricks web application

DNotebooks

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 6

A data engineer is working with two tables. Each of these tables is displayed below in its entirety. The data engineer runs the following query to join these tables together: Which of the following will be returned by the above query?

AOption A

BOption B

COption C

DOption D

EOption E

Answer : A

Option A is the correct answer because it shows the result of an INNER JOIN between the two tables. An INNER JOIN returns only the rows that have matching values in both tables based on the join condition. In this case, the join condition isON a.customer_id = c.customer_id, which means that only the rows that have the same customer ID in both tables will be included in the output. The output will have four columns: customer_id, name, account_id, and overdraft_amt. The output will have four rows, corresponding to the four customers who have accounts in the account table.

Question 7

A data engineer is running code in a Databricks Repo that is cloned from a central Git repository. A colleague of the data engineer informs them that changes have been made and synced to the central Git repository. The data engineer now needs to sync their Databricks Repo to get the changes from the central Git repository.

Which of the following Git operations does the data engineer need to run to accomplish this task?

AMerge

BPush

CPull

DCommit

EClone

Answer : C

: To sync a Databricks Repo with the changes from a central Git repository, the data engineer needs to run the Git pull operation. This operation fetches the latest updates from the remote repository and merges them with the local repository. The data engineer can use the Pull button in the Databricks Repos UI, or use the git pull command in a terminal session. The other options are not relevant for this task, as they either push changes to the remote repository (Push), combine two branches (Merge), save changes to the local repository (Commit), or create a new local repository from a remote one (Clone).Reference:

Run Git operations on Databricks Repos

Git pull

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