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Databricks-Certified-Professional-Data-Engineer Exam Dumps : Databricks Certified Data Engineer Professional Exam

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Databricks Certified Data Engineer Professional Exam Questions and Answers

Question 1

When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM's resources?

Options:

A.

The five Minute Load Average remains consistent/flat

B.

Bytes Received never exceeds 80 million bytes per second

C.

Network I/O never spikes

D.

Total Disk Space remains constant

E.

CPU Utilization is around 75%

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Question 2

A Delta Lake table representing metadata about content posts from users has the following schema:

user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE

This table is partitioned by the date column. A query is run with the following filter:

longitude < 20 & longitude > -20

Which statement describes how data will be filtered?

Options:

A.

Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.

B.

No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.

C.

The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.

D.

Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.

E.

The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.

Question 3

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?

Options:

A.

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.

B.

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

C.

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.

D.

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.

E.

Schema inference and evolution on .Databricks ensure that inferred types will always accurately match the data types used by downstream systems.