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Databricks Databricks-Certified-Professional-Data-Engineer Exam Sample Questions

Question # 1

The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity. Each user should be able to execute workloads against their assigned clusters at any time of the day. Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.

A. "Can Manage" privileges on the required cluster

B. Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster

C. Cluster creation allowed. "Can Attach To" privileges on the required cluster

D. "Can Restart" privileges on the required cluster

E. Cluster creation allowed. "Can Restart" privileges on the required cluster

Answer

D. "Can Restart" privileges on the required cluster

Explanation:

https://learn.microsoft.com/en-us/azure/databricks/security/authauthz/access-control/cluster-acl

https://docs.databricks.com/en/security/auth-authz/access-control/cluster-acl.html

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Question # 2

A data engineer needs to capture pipeline settings from an existing in the workspace, and use them to create and version a JSON file to create a new pipeline. Which command should the data engineer enter in a web terminal configured with the Databricks CLI?

A. Use the get command to capture the settings for the existing pipeline; remove the pipeline_id and rename the pipeline; use this in a create command

B. Stop the existing pipeline; use the returned settings in a reset command

C. Use the alone command to create a copy of an existing pipeline; use the get JSON command to get the pipeline definition; save this to git

D. Use list pipelines to get the specs for all pipelines; get the pipeline spec from the return results parse and use this to create a pipeline

Answer

A. Use the get command to capture the settings for the existing pipeline; remove the pipeline_id and rename the pipeline; use this in a create command

Explanation:

The Databricks CLI provides a way to automate interactions with Databricks services. When dealing with pipelines, you can use the databricks pipelines get -- pipeline-id command to capture the settings of an existing pipeline in JSON format. This JSON can then be modified by removing the pipeline_id to prevent conflicts and renaming the pipeline to create a new pipeline. The modified JSON file can then be used with the databricks pipelines create command to create a new pipeline with those settings.

References:

Databricks Documentation on CLI for Pipelines: Databricks CLI - Pipelines

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Question # 3

A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure. The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic. The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema. Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?

A. Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.

B. Tungsten encoding used by Databricks is optimized for storing string data: newly-added native support for querying JSON strings means that string types are always most efficient.

C. Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.

D. By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.

Answer

D. By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.

Explanation:

Delta Lake, built on top of Parquet, enhances query performance through data skipping, which is based on the statistics collected for each file in a table. For tables with a large number of columns, Delta Lake by default collects and stores statistics only for the first 32 columns. These statistics include min/max values and null counts, which are used to optimize query execution by skipping irrelevant data files. When dealing with highly nested JSON structures, understanding this behavior is crucial for schema design, especially when determining which fields should be flattened or prioritized in the table structure to leverage data skipping efficiently for performance optimization.

References:

Databricks documentation on Delta Lake optimization techniques, including data skipping and statistics collection (https://docs.databricks.com/delta/optimizations/index.html).

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Question # 4

The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema: For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields: Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization. Which solution meets the expectations of the end users while controlling and limiting possible costs?

A. Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.

B. Populate the dashboard by configuring a nightly batch job to save the required to quickly update the dashboard with each query.

C. Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.

D. Define a view against the products_per_order table and define the dashboard against this view.

Answer

D. Define a view against the products_per_order table and define the dashboard against this view.

Explanation:

Given the requirement for daily refresh of data and the need to ensure quick response times for interactive queries while controlling costs, a nightly batch job to precompute and save the required summary metrics is the most suitable approach. By pre-aggregating data during off-peak hours, the dashboard can serve queries quickly without requiring on-the-fly computation, which can be resource-intensive and slow, especially with many users.

This approach also limits the cost by avoiding continuous computation throughout the day and instead leverages a batch process that efficiently computes and stores the necessary data.

The other options (A, C, D) either do not address the cost and performance requirements effectively or are not suitable for the use case of less frequent data refresh and high interactivity.

References:

Databricks Documentation on Batch Processing: Databricks Batch Processing

Data Lakehouse Patterns: Data Lakehouse Best Practices

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Question # 5

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. ‘’Read’’ permissions should be set on a secret key mapped to those credentials that will be used by a given team.

B. No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.

C. “Read” permissions should be set on a secret scope containing only those credentials that will be used by a given team.

D. “Manage” permission should be set on a secret scope containing only those credentials that will be used by a given team.

Answer

C. “Read” permissions should be set on a secret scope containing only those credentials that will be used by a given team.

Explanation:

In Databricks, using the Secrets module allows for secure management of sensitive information such as database credentials. Granting 'Read' permissions on a secret key that maps to database credentials for a specific team ensures that only members of that team can access these credentials. This approach aligns with the principle of least privilege, granting users the minimum level of access required to perform their jobs, thus enhancing security.

References:

Databricks Documentation on Secret Management: Secrets

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