Free Practice Questions for Databricks Databricks-Certified-Professional-Data-Engineer Exam

The best practice for running unit tests on functions that interact with data is to use a dataset that closely mirrors the production data. This approach allows data engineers to validate the logic of their functions without the risk of affecting the actual production data. It's important to have a representative sample of production data to catch edge cases and ensure the functions will work correctly when used in a production environment.

References:

Databricks Documentation on Testing: Testing and Validation of Data and Notebooks

https://www.databricks.com/blog/2020/08/31/introducing-the-databricks-web-terminal.html

The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use the Databricks Connect feature to run your Python code on a remote Databricks cluster, or you can use the Spark Git Connector to read data from Git repositories as Spark DataFrames.

The code creates a view called email_ltv that selects the email and ltv columns from a table called user_ltv, which has the following schema: email STRING, age INT, ltv INT. The code also uses the CASE WHEN expression to replace the email values with the string “REDACTED” if the user is not a member of the marketing group. The user who executes the query is not a member of the marketing group, so they will only see the email and ltv columns, and the email column will contain the string “REDACTED” in each row. Verified References: [Databricks Certified Data Engineer Professional], under “Lakehouse” section; Databricks Documentation, under “CASE expression” section.

Maximum concurrent runs: Set to 1. There must be only one instance of each query concurrently active. Retries: Set to Unlimited. https://docs.databricks.com/en/structured-streaming/query-recovery.html

The code uses the DELETE FROM command to delete records from the users table that match a condition based on a join with another table called delete_requests, which contains all users that have requested deletion. The DELETE FROM command deletes records from a Delta Lake table by creating a new version of the table that does not contain the deleted records. However, this does not guarantee that the records to be deleted are no longer accessible, because Delta Lake supports time travel, which allows querying previous versions of the table using a timestamp or version number. Therefore, files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files from physical storage. Verified References: [Databricks Certified Data Engineer Professional], under “Delta Lake” section; Databricks Documentation, under “Delete from a table” section; Databricks Documentation, under “Remove files no longer referenced by a Delta table” section.

This is the correct answer because the JSON posted to the Databricks REST API endpoint 2.0/jobs/create defines a new job with a name, an existing cluster id, and a notebook task. However, it does not specify any schedule or trigger for the job execution. Therefore, three new jobs with the same name and configuration will be created in the workspace, but none of them will be executed until they are manually triggered or scheduled. Verified References: [Databricks Certified Data Engineer Professional], under “Monitoring & Logging” section; [Databricks Documentation], under “Jobs API - Create” section.

The CHECK constraint is used to ensure that the data inserted into the table meets the specified conditions. In this case, the CHECK constraint is used to ensure that the latitude and longitude values are within the specified range. If the data does not meet the specified conditions, the write operation will fail completely and no records will be inserted into the target table. This is because Delta Lake supports ACID transactions, which means that either all the data is written or none of it is written. Therefore, the batch insert will fail when it encounters a record that violates the constraint, and the target table will not be updated. References:

Constraints: https://docs.delta.io/latest/delta-constraints.html

ACID Transactions: https://docs.delta.io/latest/delta-intro.html#acid-transactions

The daily_store_sales table contains all the information needed to update store_sales_summary. The schema of the table is:

store_id INT, sales_date DATE, total_sales FLOAT

The daily_store_sales table is implemented as a Type 1 table, which means that old values are overwritten by new values and no history is maintained. The total_sales column might be adjusted after manual data auditing, which means that the data in the table may change over time.

The safest approach to generate accurate reports in the store_sales_summary table is to use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update. Structured Streaming is a scalable and fault-tolerant stream processing engine built on Spark SQL. Structured Streaming allows processing data streams as if they were tables or DataFrames, using familiar operations such as select, filter, groupBy, or join. Structured Streaming also supports output modes that specify how to write the results of a streaming query to a sink, such as append, update, or complete. Structured Streaming can handle both streaming and batch data sources in a unified manner.

The change data feed is a feature of Delta Lake that provides structured streaming sources that can subscribe to changes made to a Delta Lake table. The change data feed captures both data changes and schema changes as ordered events that can be processed by downstream applications or services. The change data feed can be configured with different options, such as starting from a specific version or timestamp, filtering by operation type or partition values, or excluding no-op changes.

By using Structured Streaming to subscribe to the change data feed for daily_store_sales, one can capture and process any changes made to the total_sales column due to manual data auditing. By applying these changes to the aggregates in the store_sales_summary table with each update, one can ensure that the reports are always consistent and accurate with the latest data. Verified References: [Databricks Certified Data Engineer Professional], under “Spark Core” section; Databricks Documentation, under “Structured Streaming” section; Databricks Documentation, under “Delta Change Data Feed” section.

Comprehensive and Detailed In-Depth Explanation:

TheDatabricks CLIallows users to manage clusters using command-line commands. The correct command for creating a cluster follows a specific format.

Key Components in the Command:

Command Type:databricks compute create is the correct syntax for creating a new compute resource (cluster).

Runtime Version:'14.3.x-scala2.12' specifies the Databricks runtime to use.

Workers:--num-workers 5 sets the number of worker nodes to 5.

Node Type:--node-type-id i3.xlarge defines the hardware configuration.

Cluster Name:--cluster-name DataEngineer_cluster assigns a recognizable name to the cluster.

Evaluation of Options:

Option A (databricks clusters create ...)

Incorrect:databricks clusters createis not a valid commandin the Databricks CLI v0.205.

The correct CLI command for cluster creation is databricks compute create.

Option B (databricks clusters add ...)

Incorrect:databricks clusters addis not a valid CLI command.

Option C (databricks compute add ...)

Incorrect:databricks compute addis not a valid CLI command.

Option D (databricks compute create ...)

Correct:databricks compute create is the correct command for creating a cluster.

Conclusion:

The correct command to create a cluster with five workers, an i3.xlarge node type, and Databricks runtime 14.3.x-scala2.12 is:

databricks compute create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster

Thus, the correct answer isD.

References:

Databricks CLI Documentation