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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

1. 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) Use Databricks High Concurrency clusters, which leverage optimized cloud storage connections to maximize data throughput.
B) Configure Databricks to save all data to attached SSD volumes instead of object storage, increasing file I/O significantly.
C) Partition ingestion tables by a small time duration to allow for many data files to be written in parallel.
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.

2. A data engineering team is migrating off its legacy Hadoop platform. As part of the process, they are evaluating storage formats for performance comparison. The legacy platform uses ORC and RCFile formats. After converting a subset of data to Delta Lake, they noticed significantly better query performance. Upon investigation, they discovered that queries reading from Delta tables leveraged a Shuffle Hash Join, whereas queries on legacy formats used Sort Merge Joins. The queries reading Delta Lake data also scanned less data. Which reason could be attributed to the difference in query performance?

A) Delta Lake enables data skipping and file pruning using a vectorized Parquet reader.
B) Shuffle Hash Joins are always more efficient than Sort Merge Joins.
C) The queries against the Delta Lake tables were able to leverage the dynamic file pruning optimization.
D) The queries against the ORC tables leveraged the dynamic data skipping optimization but not the dynamic file pruning optimization.

3. 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 the Delta Cache to persists the products_per_order table in memory to quickly 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) Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.

4. A Delta Lake table in the Lakehouse named customer_parsams is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
Immediately after each update succeeds, the data engineer team would like to determine the difference between the new version and the previous of the table. Given the current implementation, which method can be used?

A) Parse the Spark event logs to identify those rows that were updated, inserted, or deleted.
B) Execute a query to calculate the difference between the new version and the previous version using Delta Lake's built-in versioning and time travel functionality.
C) Parse the Delta Lake transaction log to identify all newly written data files.
D) Execute DESCRIBE HISTORY customer_churn_params to obtain the full operation metrics for the update, including a log of all records that have been added or modified.

5. A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run?

A) Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.
B) Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
C) Tasks B and C will be skipped; task A will not commit any changes because of stage failure.
D) Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
E) Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.

Solutions: