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Databricks Certified Data Engineer Professional Sample Questions:

1. 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) Configure a webhook to execute an incremental read against products_per_order each time the dashboard is refreshed.
B) Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.
C) Populate the dashboard by configuring a nightly batch job to save the required to quickly update the dashboard with each query.
D) Define a view against the products_per_order table and define the dashboard against this view.
E) Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.

2. Which REST API call can be used to review the notebooks configured to run as tasks in a multi- task job?

A) /jobs/get
B) /jobs/runs/get
C) /jobs/list
D) /jobs/runs/list
E) /jobs/runs/get-output

3. An external object storage container has been mounted to the location /mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:

After the database was successfully created and permissions configured, a member of the finance team runs the following code:

If all users on the finance team are members of the finance group, which statement describes how the tx_sales table will be created?

A) An managed table will be created in the storage container mounted to /mnt/finance_eda_bucket.
B) A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.
C) A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.
D) A managed table will be created in the DBFS root storage container.
E) An external table will be created in the storage container mounted to /mnt/finance eda bucket.

4. The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property "contains_pii" = true.
The following SQL DDL statement is executed to create a new table:
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Which command allows manual confirmation that these three requirements have been met?

A) SHOW TBLPROPERTIES dev.pii test
B) DESCRIBE EXTENDED dev.pii test
C) SHOW TABLES dev
D) DESCRIBE HISTORY dev.pii test
E) DESCRIBE DETAIL dev.pii test

5. 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 to power several production monitoring dashboards and a production model. At present, 45 of the 100 fields are being used in at least one of these applications.
The data engineer is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields.
Which of the following accurately presents information about Delta Lake and Databricks that may impact their decision-making process?

A) Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement.
B) Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.
C) The 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.
D) Human labor in writing code is the largest cost associated with data engineering workloads; as such, automating table declaration logic should be a priority in all migration workloads.
E) Schema inference and evolution on .Databricks ensure that inferred types will always accurately match the data types used by downstream systems.

Solutions:

Question # 1 Answer: C

Question # 2 Answer: A

Question # 3 Answer: A

Question # 4 Answer: B

Question # 5 Answer: A