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

2. A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?

A) Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.
B) Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.
C) Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.
D) Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.

3. The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?

A) Add a parallel table write to the current production pipeline, updating a new sales table that varies Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from as required from marketing table.
B) Instruct the marketing team to download results as a CSV and email them to the sales organization.
C) Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.
D) Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.
E) Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.

4. Which statement characterizes the general programming model used by Spark Structured Streaming?

A) Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.
B) Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.
C) Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.
D) Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.
E) Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.

5. The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over
20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?

A) %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
B) %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
C) Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
D) %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
E) Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.

質問と回答：