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NEW QUESTION # 64
Which statement describes Delta Lake Auto Compaction?
- A. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.
- B. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.
- C. Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.
- D. Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from - E. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.
Answer: E
Explanation:
This is the correct answer because it describes the behavior of Delta Lake Auto Compaction, which is a feature that automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones. Auto Compaction runs as an asynchronous job after a write to a table has succeeded and checks if files within a partition can be further compacted. If yes, it runs an optimize job with a default target file size of 128 MB. Auto Compaction only compacts files that have not been compacted previously.
NEW QUESTION # 65
A Structured Streaming job deployed to production has been resulting in higher than expected cloud storage costs. At present, during normal execution, each microbatch of data is processed in less than 3s; at least 12 times per minute, a microbatch is processed that contains 0 records. The streaming write was configured using the default trigger settings. The production job is currently scheduled alongside many other Databricks jobs in a workspace with instance pools provisioned to reduce start-up time for jobs with batch execution.
Holding all other variables constant and assuming records need to be processed in less than 10 minutes, which adjustment will meet the requirement?
- A. Increase the number of shuffle partitions to maximize parallelism, since the trigger interval cannot be modified without modifying the checkpoint directory.
- B. Use the trigger once option and configure a Databricks job to execute the query every 10 minutes; this approach minimizes costs for both compute and storage.
- C. Set the trigger interval to 3 seconds; the default trigger interval is consuming too many records per batch, resulting in spill to disk that can increase volume costs.
- D. Set the trigger interval to 10 minutes; each batch calls APIs in the source storage account, so decreasing trigger frequency to maximum allowable threshold should minimize this cost.
- E. Set the trigger interval to 500 milliseconds; setting a small but non-zero trigger interval ensures that the source is not queried too frequently.
Answer: D
NEW QUESTION # 66
Which REST API call can be used to review the notebooks configured to run as tasks in a multi- task job?
- A. /jobs/runs/get
- B. /jobs/get
- C. /jobs/runs/get-output
- D. /jobs/runs/list
- E. /jobs/list
Answer: B
Explanation:
https://docs.databricks.com/api/workspace/jobs/getresponses/settings/tasks/notebook_task/noteb ook_path
NEW QUESTION # 67
The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. The recent_sensor_recordings table contains an identifying sensor_id alongside the timestamp and temperature for the most recent 5 minutes of recordings.
The below query is used to create the alert:
The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger when mean (temperature) > 120. Notifications are triggered to be sent at most Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from every 1 minute.
If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?
- A. The total average temperature across all sensors exceeded 120 on three consecutive executions of the query
- B. The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query
- C. The source query failed to update properly for three consecutive minutes and then restarted
- D. The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query
- E. The recent_sensor_recordingstable was unresponsive for three consecutive runs of the query
Answer: D
Explanation:
This is the correct answer because the query is using a GROUP BY clause on the sensor_id column, which means it will calculate the mean temperature for each sensor separately. The alert will trigger when the mean temperature for any sensor is greater than 120, which means at least one sensor had an average temperature above 120 for three consecutive minutes. The alert will stop when the mean temperature for all sensors drops below 120.
NEW QUESTION # 68
A developer has successfully configured credential for Databricks Repos and cloned a remote Git repository. Hey don not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace.
Use Response to pull changes from the remote Git repository commit and push changes to a branch that appeared as a changes were pulled.
- A. Use Repos to merge all differences and make a pull request back to the remote repository.
- B. Use Repos to pull changes from the remote Git repository; commit and push changes to a branch that appeared as changes were pulled.
- C. Use repos to create a fork of the remote repository commit all changes and make a pull request on the source repository
- D. Use repos to merge all difference and make a pull request back to the remote repository.
- E. Use Repos to create a new branch commit all changes and push changes to the remote Git repertory.
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Answer: E
Explanation:
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.
NEW QUESTION # 69
A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company's data is stored in regional cloud storage in the United States.
The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed.
Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?
- A. Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from made based upon what is most convenient for the workspace administrator.
- B. Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.
- C. Databricks notebooks send all executable code from the user's browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.
- D. Databricks leverages user workstations as the driver during interactive development; as such, users should always use a workspace deployed in a region they are physically near.
- E. Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored.
Answer: E
Explanation:
This is the correct answer because it accurately informs this decision. The decision is about where the Databricks workspace used by the contractors should be deployed. The contractors are based in India, while all the company's data is stored in regional cloud storage in the United States. When choosing a region for deploying a Databricks workspace, one of the important factors to consider is the proximity to the data sources and sinks. Cross-region reads and writes can incur significant costs and latency due to network bandwidth and data transfer fees.
Therefore, whenever possible, compute should be deployed in the same region the data is stored to optimize performance and reduce costs.
NEW QUESTION # 70
Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code.
Which statement describes a main benefit that offset this additional effort?
- A. Improves the quality of your data
- B. Ensures that all steps interact correctly to achieve the desired end result
- C. Yields faster deployment and execution times
- D. Validates a complete use case of your application
- E. Troubleshooting is easier since all steps are isolated and tested individually
Answer: E
Explanation:
Unit tests are small, isolated tests that are used to check specific parts of the code, such as functions or classes.
NEW QUESTION # 71
A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor.
When evaluating the Ganglia Metrics for this cluster, which indicator would signal a bottleneck caused by code executing on the driver?
- A. Bytes Received never exceeds 80 million bytes per second
- B. The five Minute Load Average remains consistent/flat
- C. Network I/O never spikes
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from - D. Total Disk Space remains constant
- E. Overall cluster CPU utilization is around 25%
Answer: E
Explanation:
This is the correct answer because it indicates a bottleneck caused by code executing on the driver. A bottleneck is a situation where the performance or capacity of a system is limited by a single component or resource. A bottleneck can cause slow execution, high latency, or low throughput. A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor. When evaluating the Ganglia Metrics for this cluster, one can look for indicators that show how the cluster resources are being utilized, such as CPU, memory, disk, or network. If the overall cluster CPU utilization is around 25%, it means that only one out of the four nodes (driver + 3 executors) is using its full CPU capacity, while the other three nodes are idle or underutilized. This suggests that the code executing on the driver is taking too long or consuming too much CPU resources, preventing the executors from receiving tasks or data to process. This can happen when the code has driver-side operations that are not parallelized or distributed, such as collecting large amounts of data to the driver, performing complex calculations on the driver, or using non-Spark libraries on the driver.
NEW QUESTION # 72
The data engineer team is configuring environment for development testing, and production before beginning migration on a new data pipeline. The team requires extensive testing on both the code and data resulting from code execution, and the team want to develop and test against similar production data as possible.
A junior data engineer suggests that production data can be mounted to the development testing environments, allowing pre production code to execute against production data. Because all users have Admin privileges in the development environment, the junior data engineer has offered to configure permissions and mount this data for the team.
Which statement captures best practices for this situation?
- A. In environments where interactive code will be executed, production data should only be accessible with read permissions; creating isolated databases for each environment further reduces risks.
- B. Because delta Lake versions all data and supports time travel, it is not possible for user error or malicious actors to permanently delete production data, as such it is generally safe to mount production data anywhere.
- C. All developer, testing and production code and data should exist in a single unified workspace; creating separate environments for testing and development further reduces risks.
- D. Because access to production data will always be verified using passthrough credentials it is safe to mount data to any Databricks development environment.
Answer: A
Explanation:
The best practice in such scenarios is to ensure that production data is handled securely and with proper access controls. By granting only read access to production data in development and testing environments, it mitigates the risk of unintended data modification. Additionally, maintaining isolated databases for different environments helps to avoid accidental impacts on production data and systems.
NEW QUESTION # 73
A data engineer needs to capture pipeline settings from an existing in the workspace, and use them to create and version a JSON file to create a new pipeline. Which command should the data engineer enter in a web terminal configured with the Databricks CLI?
- A. Use the alone command to create a copy of an existing pipeline; use the get JSON command to get the pipeline definition; save this to git
- B. Stop the existing pipeline; use the returned settings in a reset command
- C. Use the get command to capture the settings for the existing pipeline; remove the pipeline_id and rename the pipeline; use this in a create command
- D. Use list pipelines to get the specs for all pipelines; get the pipeline spec from the return results parse and use this to create a pipeline
Answer: C
Explanation:
The Databricks CLI provides a way to automate interactions with Databricks services. When dealing with pipelines, you can use the databricks pipelines get --pipeline-id command to capture the settings of an existing pipeline in JSON format. This JSON can then be modified by removing the pipeline_id to prevent conflicts and renaming the pipeline to create a new pipeline. The modified JSON file can then be used with the databricks pipelines create command to create a new pipeline with those settings.
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NEW QUESTION # 74
Which statement describes the default execution mode for Databricks Auto Loader?
- A. Webhook trigger Databricks job to run anytime new data arrives in a source directory; new data automatically merged into target tables using rules inferred from the data.
- B. Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; the target table is materialized by directly querying all valid files in the source directory.
- C. New files are identified by listing the input directory; new files are incrementally and idempotently loaded into the target Delta Lake table.
- D. Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; new files are incrementally and impotently into the target Delta Lake table.
- E. New files are identified by listing the input directory; the target table is materialized by directory querying all valid files in the source directory.
Answer: C
Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from Explanation:
Databricks Auto Loader simplifies and automates the process of loading data into Delta Lake.
The default execution mode of the Auto Loader identifies new files by listing the input directory. It incrementally and idempotently loads these new files into the target Delta Lake table. This approach ensures that files are not missed and are processed exactly once, avoiding data duplication. The other options describe different mechanisms or integrations that are not part of the default behavior of the Auto Loader.
NEW QUESTION # 75
The data engineer team has been tasked with configured connections to an external database that does not have a supported native connector with Databricks. The external database already has data security configured by group membership. These groups map directly to user group already created in Databricks that represent various teams within the company. A new login credential has been created for each group in the external database. The Databricks Utilities Secrets module will be used to make these credentials available to Databricks users. Assuming that all the credentials are configured correctly on the external database and group membership is properly configured on Databricks, which statement describes how teams can be granted the minimum necessary access to using these credentials?
- A. "Read" permissions should be set on a secret scope containing only those credentials that will be used by a given team.
- B. "Read'' permissions should be set on a secret key mapped to those credentials that will be used by a given team.
- C. "Manage" permission should be set on a secret scope containing only those credentials that will be used by a given team.
- D. No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.
Answer: A
Explanation:
In Databricks, using the Secrets module allows for secure management of sensitive information such as database credentials. Granting 'Read' permissions on a secret key that maps to database credentials for a specific team ensures that only members of that team can access Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from these credentials. This approach aligns with the principle of least privilege, granting users the minimum level of access required to perform their jobs, thus enhancing security.
NEW QUESTION # 76
Which statement regarding spark configuration on the Databricks platform is true?
- A. Spark configuration set within an notebook will affect all SparkSession attached to the same interactive cluster
- B. Spark configuration properties can only be set for an interactive cluster by creating a global init script.
- C. The Databricks REST API can be used to modify the Spark configuration properties for an interactive cluster without interrupting jobs.
- D. Spark configuration properties set for an interactive cluster with the Clusters UI will impact all notebooks attached to that cluster.
- E. When the same spar configuration property is set for an interactive to the same interactive cluster.
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Answer: D
Explanation:
When Spark configuration properties are set for an interactive cluster using the Clusters UI in Databricks, those configurations are applied at the cluster level. This means that all notebooks attached to that cluster will inherit and be affected by these configurations. This approach ensures consistency across all executions within that cluster, as the Spark configuration properties dictate aspects such as memory allocation, number of executors, and other vital execution parameters. This centralized configuration management helps maintain standardized execution environments across different notebooks, aiding in debugging and performance optimization.
NEW QUESTION # 77
The data architect has decided that once data has been ingested from external sources into the Databricks Lakehouse, table access controls will be leveraged to manage permissions for all production tables and views.
The following logic was executed to grant privileges for interactive queries on a production database to the core engineering group.
GRANT USAGE ON DATABASE prod TO eng;
GRANT SELECT ON DATABASE prod TO eng;
Assuming these are the only privileges that have been granted to the eng group and that these users are not workspace administrators, which statement describes their privileges?
- A. Group members are able to query and modify all tables and views in the prod database, but cannot create new tables or views.
- B. Group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database.
- C. Group members are able to list all tables in the prod database but are not able to see the results of any queries on those tables.
- D. Group members have full permissions on the prod database and can also assign permissions to other users or groups.
- E. Group members are able to create, query, and modify all tables and views in the prod database, but cannot define custom functions.
Answer: B
Explanation:
The GRANT USAGE ON DATABASE prod TO eng command grants the eng group the permission to use the prod database, which means they can list and access the tables and views in the database. The GRANT SELECT ON DATABASE prod TO eng command grants the eng group the permission to select data from the tables and views in the prod database, which means they can query the data using SQL or DataFrame API. However, these commands do not grant the eng group any other permissions, such as creating, modifying, or deleting tables and views, or defining custom functions. Therefore, the eng group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database.
NEW QUESTION # 78
Two of the most common data locations on Databricks are the DBFS root storage and external object storage mounted with dbutils.fs.mount().
Which of the following statements is correct?
- A. The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.
- B. Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.
- C. By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.
- D. DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems.
- E. The DBFS root stores files in ephemeral block volumes attached to the driver, while mounted directories will always persist saved data to external storage between sessions.
Answer: D
Explanation:
DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems. DBFS is not a physical file system, but a layer over the object storage that provides a unified view of data across different data sources. By Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from default, the DBFS root is accessible to all users in the workspace, and the access to mounted data sources depends on the permissions of the storage account or container. Mounted storage volumes do not need to have full public read and write permissions, but they do require a valid connection string or access key to be provided when mounting. Both the DBFS root and mounted storage can be accessed when using %sh in a Databricks notebook, as long as the cluster has FUSE enabled. The DBFS root does not store files in ephemeral block volumes attached to the driver, but in the object storage associated with the workspace. Mounted directories will persist saved data to external storage between sessions, unless they are unmounted or deleted.
NEW QUESTION # 79
The data engineering team maintains a table of aggregate statistics through batch nightly updates. This includes total sales for the previous day alongside totals and averages for a variety of time periods including the 7 previous days, year-to-date, and quarter-to-date. This table is named store_saies_summary and the schema is as follows:
The table daily_store_sales contains all the information needed to update store_sales_summary.
The schema for this table is:
store_id INT, sales_date DATE, total_sales FLOAT
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from If daily_store_sales is implemented as a Type 1 table and the total_sales column might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in the store_sales_summary table?
- A. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
- B. Implement the appropriate aggregate logic as a Structured Streaming read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
- C. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and overwrite the store_sales_summary table with each Update.
- D. Use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update.
- E. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and append new rows nightly to the store_sales_summary table.
Answer: C
NEW QUESTION # 80
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