RELATED DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER CERTIFICATIONS, DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER LATEST STUDY QUESTIONS

Related Databricks-Certified-Professional-Data-Engineer Certifications, Databricks-Certified-Professional-Data-Engineer Latest Study Questions

Related Databricks-Certified-Professional-Data-Engineer Certifications, Databricks-Certified-Professional-Data-Engineer Latest Study Questions

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q115-Q120):

NEW QUESTION # 115
A Delta Lake table was created with the below query:
Consider the following query:
DROP TABLE prod.sales_by_store -
If this statement is executed by a workspace admin, which result will occur?

  • A. The table will be removed from the catalog and the data will be deleted.
  • B. The table will be removed from the catalog but the data will remain in storage.
  • C. An error will occur because Delta Lake prevents the deletion of production data.
  • D. Nothing will occur until a COMMIT command is executed.
  • E. Data will be marked as deleted but still recoverable with Time Travel.

Answer: A

Explanation:
When a table is dropped in Delta Lake, the table is removed from the catalog and the data is deleted. This is because Delta Lake is a transactional storage layer that provides ACID guarantees. When a table is dropped, the transaction log is updated to reflect the deletion of the table and the data is deleted from the underlying storage. References:
* https://docs.databricks.com/delta/quick-start.html#drop-a-table
* https://docs.databricks.com/delta/delta-batch.html#drop-table


NEW QUESTION # 116
Delete records from the transactions Delta table where transactionDate is greater than current timestamp?

  • A. DELET FROM transactions where transactionDate GE current_timestamp()
  • B. DELETE FROM transactions where transactionDate > current_timestamp() KEEP_HISTORY
  • C. DELETE FROM transactions where transactionDate > current_timestamp()
  • D. DELETE FROM transactions if transctionDate > current_timestamp()
  • E. DELETE FROM transactions FORMAT DELTA where transactionDate > cur-renct_timestmap()

Answer: C


NEW QUESTION # 117
An engineering manager uses a Databricks SQL query to monitor their team's progress on fixes related to
customer-reported bugs. The manager checks the results of the query every day, but they are manually
rerunning the query each day and waiting for the results.
Which of the following approaches can the manager use to ensure the results of the query are up-dated each
day?

  • A. They can schedule the query to refresh every 1 day from the SQL endpoint's page in Databricks SQL
  • B. They can schedule the query to run every 12 hours from the Jobs UI
  • C. They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL
  • D. They can schedule the query to run every 1 day from the Jobs UI
  • E. They can schedule the query to refresh every 1 day from the query's page in Databricks SQL

Answer: E


NEW QUESTION # 118
Which method is used to solve for coefficients bO, b1, ... bn in your linear regression model:

  • A. Integer programming
  • B. Ordinary Least squares
  • C. Ridge and Lasso
  • D. Apriori Algorithm

Answer: B

Explanation:
Explanation : RY = b0 + b1x1+b2x2+ .... +bnxn
In the linear model, the bi's represent the unknown p parameters. The estimates for these unknown parameters
are chosen so that, on average, the model provides a reasonable estimate of a person's income based on age
and education. In other words, the fitted model should minimize the overall error between the linear model and
the actual observations. Ordinary Least Squares (OLS) is a common technique to estimate the parameters


NEW QUESTION # 119
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. Schema inference and evolution on .Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
  • B. 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.
  • 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. Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.
  • E. 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.

Answer: E

Explanation:
This is the correct answer because it accurately presents information about Delta Lake and Databricks that may impact the decision-making process of a junior data engineer who is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields. Delta Lake and Databricks support schema inference and evolution, which means that they can automatically infer the schema of a table from the source data and allow adding new columns or changing column types without affecting existing queries or pipelines. However, schema inference and evolution may not always be desirable or reliable, especially when dealing with complex or nested data structures or when enforcing data quality and consistency across different systems. Therefore, setting types manually can provide greater assurance of data quality enforcement and avoid potential errors or conflicts due to incompatible or unexpected data types. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Schema inference and partition of streaming DataFrames/Datasets" section.


NEW QUESTION # 120
......

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