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

（A）Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.

（B）Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.

（C）Populate the dashboard by configuring a nightly batch job to save the required to quickly update the dashboard with each query.

（D）Define a view against the products_per_order table and define the dashboard against this view.

（A）New files are identified by listing the input directory; new files are incrementally and idempotently loaded into the target Delta Lake table.

（B）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.

（C）New files are identified by listing the input directory; the target table is materialized by directory querying all valid files in the source directory.

（D）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.

（A）Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB* 1024*1024
/512), and then write to parquet.

（B）Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.

（C）Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.

（D）Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.

（E）Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.

（A）It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3

（B）It is retained for 90 days or until the run-id is re-used through custom run configuration

（C）It is retained until you export or delete job run logs

（D）t is retained for 60 days, during which you can export notebook run results to HTML

（E）It is retained for 60 days, after which logs are archived

（A）Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.

（B）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.

（C）Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.

（D）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.

（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 1 GB.

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

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

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

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

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

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

（B）When a database is being created, make sure that the LOCATION keyword is used.

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

（D）When tables are created, make sure that the EXTERNAL keyword is used in the CREATE TABLE statement.

（E）When data is saved to a table, make sure that a full file path is specified alongside the Delta format.

（A）Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.

（B）Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.

（C）Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.

（D）Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.

（E）Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.

（A）The data files compacted by vacuum are not tracked by the cloned metadata; running refresh on the cloned table will pull in recent changes.

（B）The metadata created by the clone operation is referencing data files that were purged as invalid by the vacuum command

（C）Running vacuum automatically invalidates any shallow clones of a table; deep clone should always be used when a cloned table will be repeatedly queried.

（D）Because Type 1 changes overwrite existing records, Delta Lake cannot guarantee data consistency for cloned tables.

（A）No permissions

（B）Can Read

（C）Can Manage

（D）Can Edit

（E）Can Run