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Databricks Certified-Data-Engineer-Professional 試験シラバストピック:

Databricks Certified Data Engineer Professional 認定 Certified-Data-Engineer-Professional 試験問題：

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

2. The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalid latitude and longitude values in the activity_details table have been breaking their ability to use other geolocation processes.
A junior engineer has written the following code to add CHECK constraints to the Delta Lake table:

A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.
Which statement explains the cause of this failure?

A) Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.
B) The activity details table already contains records that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table.
C) The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.
D) The activity details table already exists; CHECK constraints can only be added during initial table creation.
E) The current table schema does not contain the field valid coordinates; schema evolution will need to be enabled before altering the table to add a constraint.

3. A data engineer is developing a Lakeflow Declarative Pipeline (LDP) using a Databricks notebook directly connected to their pipeline. After adding new table definitions and transformation logic in their notebook, they want to check for any syntax errors in the pipeline code without actually processing data or running the pipeline. How should the data engineer perform this syntax check?

A) Use the "Validate" option in the notebook to check for syntax errors.
B) Disconnect the notebook from the pipeline and reconnect it to a compute cluster to access code validation features.
C) Switch to a workspace file instead of a notebook to access validation and diagnostics tools.
D) Open the web terminal from the notebook and run a shell command to validate the pipeline code.

4. A platform engineer is creating catalogs and schemas for the development team to use.
The engineer has created an initial catalog, catalog_A, and initial schema, schema_A. The engineer has also granted USE CATALOG, USE SCHEMA, and CREATE TABLE to the development team so that the engineer can begin populating the schema with new tables.
Despite being owner of the catalog and schema, the engineer noticed that they do not have access to the underlying tables in Schema_A.
What explains the engineer's lack of access to the underlying tables?

A) The platform engineer needs to execute a REFRESH statement as the table permissions did not automatically update for owners.
B) Permissions explicitly given by the table creator are the only way the Platform Engineer could access the underlying tables in their schema.
C) The owner of the schema does not automatically have permission to tables within the schema, but can grant them to themselves at any point.
D) Users granted with USE CATALOG can modify the owner's permissions to downstream tables.

5. A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impression led to monitizable clicks.

Which solution would improve the performance?

A)

B)

C)

D)

質問と回答：