Free Databricks Databricks-Certified-Professional-Data-Engineer Practice Exam with Questions & Answers | Set: 2

Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.

Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.

Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.

Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.

Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.

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

Stop the existing pipeline; use the returned settings in a reset command

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

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

No; Delta Lake manages streaming checkpoints in the transaction log.

Yes; both of the streams can share a single checkpoint directory.

No; only one stream can write to a Delta Lake table.

Yes; Delta Lake supports infinite concurrent writers.

No; each of the streams needs to have its own checkpoint directory.

Improves the quality of your data

Validates a complete use case of your application

Troubleshooting is easier since all steps are isolated and tested individually

Yields faster deployment and execution times

Ensures that all steps interact correctly to achieve the desired end result

A shuffle occurs prior to writing to try to group data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.

Optimized writes logical partitions instead of directory partitions partition boundaries are only represented in metadata fewer small files are written.

An asynchronous job runs after the write completes to detect if files could be further compacted; yes, an OPTIMIZE job is executed toward a default of 1 GB.

Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.

The job_id is returned in this field.

The job_id and number of times the job has been are concatenated and returned.

The number of times the job definition has been run in the workspace.

The globally unique ID of the newly triggered run.

Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.

Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.

The checkpoint directory will be used to track state information for the unique keys present in the join.

Stream-static joins cannot use static Delta tables because of consistency issues.

The checkpoint directory will be used to track updates to the static Delta table.

Set the skipChangeCommits flag to true on bpm_stats

Set the SkipChangeCommits flag to true raw_lot

Set the pipelines, reset, allowed property to false on bpm_stats

Set the pipelines, reset, allowed property to false on raw_iot

All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.

All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.

Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.

All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.

The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.

Use Auto Loader to subscribe to new files in the account history directory; configure a Structured Streaminq trigger once job to batch update newly detected files into the account current table.

Overwrite the account current table with each batch using the results of a query against the account history table grouping by user id and filtering for the max value of last updated.

Filter records in account history using the last updated field and the most recent hour processed, as well as the max last iogin by user id write a merge statement to update or insert the most recent value for each user id.

Use Delta Lake version history to get the difference between the latest version of account history and one version prior, then write these records to account current.

Filter records in account history using the last updated field and the most recent hour processed, making sure to deduplicate on username; write a merge statement to update or insert the

most recent value for each username.