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Databricks Certified Data Engineer Professional Sample Questions:

1. To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries.
The data engineering team has been made aware of new requirements from a customer-facing application, which is the only downstream workload they manage entirely. As a result, an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added.
Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?

A) Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.
B) Create a new table with the required schema and new fields and use Delta Lake's deep clone functionality to sync up changes committed to one table to the corresponding table.
C) Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer-facing application.
D) Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application.
E) Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries.

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

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

3. A developer has successfully configured credential for Databricks Repos and cloned a remote Git repository. Hey don not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace.
Use Response to pull changes from the remote Git repository commit and push changes to a branch that appeared as a changes were pulled.

A) Use Repos to pull changes from the remote Git repository; commit and push changes to a branch that appeared as changes were pulled.
B) Use Repos to merge all differences and make a pull request back to the remote repository.
C) Use repos to merge all difference and make a pull request back to the remote repository.
D) Use Repos to create a new branch commit all changes and push changes to the remote Git repertory.
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
E) Use repos to create a fork of the remote repository commit all changes and make a pull request on the source repository

4. When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

A) Cluster: New Job Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: Unlimited
B) Cluster: Existing All-Purpose Cluster;
Retries: None;
Maximum Concurrent Runs: 1
C) Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
D) Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
E) Cluster: New Job Cluster;
Retries: None;
Maximum Concurrent Runs: 1

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 Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from structure.
The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.
The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.
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, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.
B) Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
C) By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.
D) 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.

Solutions: