Most Recent Databricks-Certified-Professional-Data-Engineer Exam Questions & Answers

Option B correctly fills in the blank to meet the specified requirements. Option B uses the ''cloudFiles.schemaLocation'' option, which is required for the schema detection and evolution functionality of Databricks Auto Loader. Additionally, option B uses the ''mergeSchema'' option, which is required for the schema evolution functionality of Databricks Auto Loader. Finally, option B uses the ''writeStream'' method, which is required for the incremental processing of JSON files as they arrive in a source directory. The other options are incorrect because they either omit the required options, use the wrong method, or use the wrong format.Reference:

Configure schema inference and evolution in Auto Loader: https://docs.databricks.com/en/ingestion/auto-loader/schema.html

Write streaming data: https://docs.databricks.com/spark/latest/structured-streaming/writing-streaming-data.html

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.

Given the CASE statement in the view definition, the result set for a user not in the auditing group would be constrained by the ELSE condition, which filters out records based on age. Therefore, the view will return all columns normally for records with an age greater than 18, as users who are not in the auditing group will not satisfy the is_member('auditing') condition. Records not meeting the age > 18 condition will not be displayed.

Granting a user 'Can Read' permissions on a notebook within Databricks allows them to view the notebook's content without the ability to execute or edit it. This level of permission ensures that the new team member can review the production logic for learning or auditing purposes without the risk of altering the notebook's code or affecting production data and workflows. This approach aligns with best practices for maintaining security and integrity in production environments, where strict access controls are essential to prevent unintended modifications. Reference: Databricks documentation on access control and permissions for notebooks within the workspace (https://docs.databricks.com/security/access-control/workspace-acl.html).