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In order to facilitate near real-time workloads, a data engineer is creating a helper function to leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.
The function is displayed below with a blank:
Which response correctly fills in the blank to meet the specified requirements?
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:
Which is a key benefit of an end-to-end test?
End-to-end testing is a methodology used to test whether the flow of an application, from start to finish, behaves as expected. The key benefit of an end-to-end test is that it closely simulates real-world, user behavior, ensuring that the system as a whole operates correctly.
Software Testing: End-to-End Testing
A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.
Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?
This is the correct answer because it explains which of the following adjustments will get a more accurate measure of how code is likely to perform in production. The adjustment is that calling display() forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results. When developing code in Databricks notebooks, one should be aware of how Spark handles transformations and actions. Transformations are operations that create a new DataFrame or Dataset from an existing one, such as filter, select, or join. Actions are operations that trigger a computation on a DataFrame or Dataset and return a result to the driver program or write it to storage, such as count, show, or save. Calling display() on a DataFrame or Dataset is also an action that triggers a computation and displays the result in a notebook cell. Spark uses lazy evaluation for transformations, which means that they are not executed until an action is called. Spark also uses caching to store intermediate results in memory or disk for faster access in subsequent actions. Therefore, calling display() forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results. To get a more accurate measure of how code is likely to perform in production, one should avoid calling display() too often or clear the cache before running each cell. Verified Reference: [Databricks Certified Data Engineer Professional], under ''Spark Core'' section; Databricks Documentation, under ''Lazy evaluation'' section; Databricks Documentation, under ''Caching'' section.
Which statement regarding stream-static joins and static Delta tables is correct?
This is the correct answer because stream-static joins are supported by Structured Streaming when one of the tables is a static Delta table. A static Delta table is a Delta table that is not updated by any concurrent writes, such as appends or merges, during the execution of a streaming query. In this case, each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch, which means it will reflect any changes made to the static Delta table before the start of each microbatch. Verified Reference: [Databricks Certified Data Engineer Professional], under ''Structured Streaming'' section; Databricks Documentation, under ''Stream and static joins'' section.
A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI. Upon reviewing their work, the engineer realizes that they are listed as the "Owner" for each job. They attempt to transfer "Owner" privileges to the "DevOps" group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?
The reason why the junior data engineer cannot transfer ''Owner'' privileges to the ''DevOps'' group is that Databricks jobs must have exactly one owner, and the owner must be an individual user, not a group. A job cannot have more than one owner, and a job cannot have a group as an owner. The owner of a job is the user who created the job, or the user who was assigned the ownership by another user. The owner of a job has the highest level of permission on the job, and can grant or revoke permissions to other users or groups. However, the owner cannot transfer the ownership to a group, only to another user. Therefore, the junior data engineer's attempt to transfer ''Owner'' privileges to the ''DevOps'' group is not possible.Reference:
Jobs access control: https://docs.databricks.com/security/access-control/table-acls/index.html
