Databricks-Certified-Professional-Data-Engineer Exam Dumps - Databricks Certification Questions and Answers

This is the correct answer because it characterizes the general programming model used by Spark Structured Streaming, which is to treat a live data stream as a table that is being continuously appended. This leads to a new stream processing model that is very similar to a batch processing model, where users can express their streaming computation using the same Dataset/DataFrame API as they would use for static data. The Spark SQL engine will take care of running the streaming query incrementally and continuously and updating the final result as streaming data continues to arrive. Verified References: [Databricks Certified Data Engineer Professional], under “Structured Streaming” section; Databricks Documentation, under “Overview” section.

This is the correct answer because it describes a limitation of Databricks Secrets. Databricks Secrets is a module that provides tools to store sensitive credentials and avoid accidentally displaying them in plain text. Databricks Secrets allows creating secret scopes, which are collections of secrets that can be accessed by users or groups. Databricks Secrets also allows creating and managing secrets using the Databricks CLI or the Databricks REST API. However, a limitation of Databricks Secrets is that the Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials. Therefore, users should still be careful with which credentials are stored in Databricks Secrets and which users have access to using these secrets. Verified References: [Databricks Certified Data Engineer Professional], under “Databricks Workspace” section; Databricks Documentation, under “List secrets” section.

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 References: [Databricks Certified Data Engineer Professional], under “Structured Streaming” section; Databricks Documentation, under “Stream and static joins” section.

The scenario presented involves inconsistent microbatch processing times in a Structured Streaming job during peak hours, with the need to ensure that records are processed within 10 seconds. The trigger once option is the most suitable adjustment to address these challenges:

Understanding Triggering Options:

Fixed Interval Triggering (Current Setup): The current trigger interval of 10 seconds may contribute to the inconsistency during peak times as it doesn't adapt based on the processing time of the microbatches. If a batch takes longer to process, subsequent batches will start piling up, exacerbating the delays.

Trigger Once: This option allows the job to run a single microbatch for processing all available data and then stop. It is useful in scenarios where batch sizes are unpredictable and can vary significantly, which seems to be the case during peak hours in this scenario.

Implementation of Trigger Once:

Setup: Instead of continuously running, the job can be scheduled to run every 10 seconds using a Databricks job. This scheduling effectively acts as a custom trigger interval, ensuring that each execution cycle handles all available data up to that point without overlapping or queuing up additional executions.

Advantages: This approach allows for each batch to complete processing all available data before the next batch starts, ensuring consistency in handling data surges and preventing the system from being overwhelmed.

Rationale Against Other Options:

Option A and E (Decrease Interval): Decreasing the trigger interval to 5 seconds might exacerbate the problem by increasing the frequency of batch starts without ensuring the completion of previous batches, potentially leading to higher overhead and less efficient processing.

Option B (Increase Interval): Increasing the trigger interval to 30 seconds could lead to latency issues, as the data would be processed less frequently, which contradicts the requirement of processing records in less than 10 seconds.

Option C (Modify Partitions): While increasing parallelism through more shuffle partitions can improve performance, it does not address the fundamental issue of batch scheduling and could still lead to inconsistency during peak loads.

Conclusion:

By using the trigger once option and scheduling the job every 10 seconds, you ensure that each microbatch has sufficient time to process all available data thoroughly before the next cycle begins, aligning with the need to handle peak loads more predictably and efficiently.

References

Structured Streaming Programming Guide - Triggering

Databricks Jobs Scheduling

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.

 This is the correct answer because Delta Lake uses a transaction log to store metadata about each table, including min and max statistics for each column in each data file. The Delta engine can use this information to quickly identify which files to load based on a filter condition, without scanning the entire table or the file footers. This is called data skipping and it can improve query performance significantly. Verified References: [Databricks Certified Data Engineer Professional], under “Delta Lake” section; [Databricks Documentation], under “Optimizations - Data Skipping” section.

In the Transaction log, Delta Lake captures statistics for each data file of the table. These statistics indicate per file:

- Total number of records

- Minimum value in each column of the first 32 columns of the table

- Maximum value in each column of the first 32 columns of the table

- Null value counts for in each column of the first 32 columns of the table

When a query with a selective filter is executed against the table, the query optimizer uses these statistics to generate the query result. it leverages them to identify data files that may contain records matching the conditional filter.

For the SELECT query in the question, The transaction log is scanned for min and max statistics for the price column

Comprehensive and Detailed Explanation From Exact Extract:

Exact extract: “run_id: The canonical identifier of a run.”

https://learn.microsoft.com/en-us/azure/databricks/delta/vacuum

In Databricks Repos, when a user does not have privileges to make changes directly to the main branch of a cloned remote Git repository, the recommended approach is to create a new branch within the Databricks workspace. The developer can then make changes in this new branch, commit those changes, and push the new branch to the remote Git repository. This workflow allows for isolated development without affecting the main branch, enabling the developer to propose changes via a pull request from the new branch to the main branch in the remote repository. This method adheres to common Git collaboration workflows, fostering code review and collaboration while ensuring the integrity of the main branch.