Free Databricks-Certified-Data-Engineer-Associate Exam Dumps

No Installation Required, Instantly Prepare for the Databricks-Certified-Data-Engineer-Associate exam and please click the below link to start the Databricks-Certified-Data-Engineer-Associate Exam Simulator with a real Databricks-Certified-Data-Engineer-Associate practice exam questions.
Use directly our on-line Databricks-Certified-Data-Engineer-Associate exam dumps materials and try our Testing Engine to pass the Databricks-Certified-Data-Engineer-Associate which is always updated.

  • Exam Code: Databricks-Certified-Data-Engineer-Associate
  • Exam Title: Databricks Certified Data Engineer Associate Exam
  • Vendor: Databricks
  • Exam Questions: 88
  • Last Updated: November 15th,2024

Question 1

A data engineer has developed a data pipeline to ingest data from a JSON source using Auto Loader, but the engineer has not provided any type inference or schema hints in their pipeline. Upon reviewing the data, the data engineer has noticed that all of the columns in the target table are of the string type despite some of the fields only including float or boolean values.
Which of the following describes why Auto Loader inferred all of the columns to be of the
string type?

Correct Answer:B
JSON data is a text-based format that uses strings to represent all values. When Auto Loader infers the schema of JSON data, it assumes that all values are strings. This is because Auto Loader cannot determine the type of a value based on its string representation. https://docs.databricks.com/en/ingestion/auto-loader/schema.html Forexample, the following JSON string represents a value that is logically a boolean: JSON "true" Use code with caution. Learn more However, Auto Loader would infer that the type of this value is string. This is because Auto Loader cannot determine that the value is a boolean based on its string representation. In order to get Auto Loader to infer the correct types for columns, the data engineer can provide type inference or schema hints. Type inference hints can be used to specify the types of specific columns. Schema hints can be used to provide the entire schema of the data. Therefore, the correct answer is B. JSON data is a text-based format.

Question 2

An engineering manager wants to monitor the performance of a recent project using a Databricks SQL query. For the first week following the project’s release, the manager wants the query results to be updated every minute. However, the manager is concerned that the compute resources used for the query will be left running and cost the organization a lot of money beyond the first week of the project’s release.
Which of the following approaches can the engineering team use to ensure the query does not cost the organization any money beyond the first week of the project’s release?

Correct Answer:E
If a dashboard is configured for automatic updates, it has a Scheduled button at the top, rather than a Schedule button. To stop automatically updating the dashboard and remove its subscriptions:
Click Scheduled.
In the Refresh every drop-down, select Never.
Click Save. The Scheduled button label changes to Schedule. Source:https://learn.microsoft.com/en-us/azure/databricks/sql/user/dashboards/

Question 3

A data engineer is attempting to drop a Spark SQL table my_table and runs the following command:
DROP TABLE IF EXISTS my_table;
After running this command, the engineer notices that the data files and metadata files have been deleted from the file system.
Which of the following describes why all of these files were deleted?

Correct Answer:A
managed tables files and metadata are managed by metastore and will be deleted when the table is dropped . while external tables the metadata is stored in a external location. hence when a external table is dropped you clear off only the metadata and the files (data) remain.

Question 4

Which of the following describes a benefit of creating an external table from Parquet rather than CSV when using a CREATE TABLE AS SELECT statement?

Correct Answer:C
https://www.databricks.com/glossary/what-is- parquet#:~:text=Columnar storage like Apache Parquet,compared to row-oriented databases. Columnar storage like Apache Parquet is designed to bring efficiency compared to row-based files like CSV. When querying, columnar storage you can skip over the non-relevant data very quickly. As a result, aggregation queries are less time-consuming compared to row-oriented databases.

Question 5

A data engineer has a Python variable table_name that they would like to use in a SQL query. They want to construct a Python code block that will run the query using table_name.
They have the following incomplete code block:
(f"SELECT customer_id, spend FROM {table_name}")
Which of the following can be used to fill in the blank to successfully complete the task?

Correct Answer:E