Databricks-Certified-Data-Engineer-Professional 無料問題集「Databricks Certified Data Engineer Professional」

（A）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.

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

（C）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.

（D）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.

（E）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.

（A）When the workspace is being configured, make sure that external cloud object storage has been mounted.

（B）Whenever a table is being created, make sure that the location keyword is used.

（C）When tables are created, make sure that the external keyword is used in the create table statement.

（D）Whenever a database is being created, make sure that the location keyword is used Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

（E）When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.

（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）Spark configuration properties set for an interactive cluster with the Clusters UI will impact all notebooks attached to that cluster.

（D）When the same spar configuration property is set for an interactive to the same interactive cluster.
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

（E）The Databricks REST API can be used to modify the Spark configuration properties for an interactive cluster without interrupting jobs.

（A）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.

（B）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.

（C）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.

（D）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.

（E）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.

（A）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.

（B）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.

（C）The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.

（D）Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.

（E）By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.

（A）Skew caused by more data being assigned to a subset of spark-partitions.

（B）Spill resulting from attached volume storage being too small.

（C）Credential validation errors while pulling data from an external system.

（D）Task queueing resulting from improper thread pool assignment.

（E）Network latency due to some cluster nodes being in different regions from the source data

（A）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.

（B）Optimized writes logical partitions instead of directory partitions partition boundaries are only Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from represented in metadata fewer small files are written.

（C）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.

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

（A）They are updated.

（B）They are inserted.

（C）They are deleted.

（D）They are merged.

（E）They are ignored.

（A）Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.

（B）Add a parallel table write to the current production pipeline, updating a new sales table that varies Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from as required from marketing table.

（C）Instruct the marketing team to download results as a CSV and email them to the sales organization.

（D）Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.

（E）Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.

（A）Configure a job that executes every time new data lands in a given directory.

（B）Schedule a Structured Streaming job with a trigger interval of 60 minutes.

（C）Schedule a jo to execute the pipeline once and hour on a dedicated interactive cluster.

（D）Schedule a job to execute the pipeline once hour on a new job cluster.