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

1. A data architect has heard about lake's built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability. Which piece of Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from information is critical to this decision?

A) Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.
B) Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.
C) Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires setting multiple fields in a single update.
D) Delta Lake only supports Type 0 tables; once records are inserted to a Delta Lake table, they cannot be modified.
E) Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.

2. 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 Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.
B) 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.
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.

3. A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI.
Upon reviewing their work, the engineer realizes that they are listed as the "Owner" for each job.
They attempt to transfer "Owner" privileges to the "DevOps" group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

A) Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.
B) The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.
C) Other than the default "admins" group, only individual users can be granted privileges on jobs.
D) Only workspace administrators can grant "Owner" privileges to a group.
E) A user can only transfer job ownership to a group if they are also a member of that group.

4. A large company seeks to implement a near real-time solution involving hundreds of pipelines with parallel updates of many tables with extremely high volume and high velocity data.
Which of the following solutions would you implement to achieve this requirement?

A) Partition ingestion tables by a small time duration to allow for many data files to be written in parallel.
B) Configure Databricks to save all data to attached SSD volumes instead of object storage, increasing file I/O significantly.
C) Use Databricks High Concurrency clusters, which leverage optimized cloud storage connections to maximize data throughput.
D) Isolate Delta Lake tables in their own storage containers to avoid API limits imposed by cloud vendors.
E) Store all tables in a single database to ensure that the Databricks Catalyst Metastore can load balance overall throughput.

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

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