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

（A）Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.

（B）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.

（C）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.

（D）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.

（E）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.

（A）Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root.

（B）Isolating tables in separate databases based on data quality tiers allows for easy permissions management through database ACLs and allows physical separation of default storage locations for managed tables.

（C）Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.

（D）Storinq all production tables in a single database provides a unified view of all data assets available throughout the Lakehouse, simplifying discoverability by granting all users view privileges on this database.

（E）Because all tables must live in the same storage containers used for the database they're created in, organizations should be prepared to create between dozens and thousands of databases depending on their data isolation requirements.

（A）Databricks supports Spark SQL and JDBC; all logic can be directly migrated from the source system without refactoring.

（B）Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.

（C）All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.

（D）Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.

（E）Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.

（A）Cluster creation allowed. "Can Attach To" privileges on the required cluster

（B）"Can Restart" privileges on the required cluster

（C）Cluster creation allowed. "Can Restart" privileges on the required cluster

（D）"Can Manage" privileges on the required cluster

（E）Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster

（A）The table will be removed from the catalog and the data will be deleted.

（B）An error will occur because Delta Lake prevents the deletion of production data.

（C）Nothing will occur until a COMMIT command is executed.

（D）Data will be marked as deleted but still recoverable with Time Travel.

（E）The table will be removed from the catalog but the data will remain in storage.

（A）import sys
date = sys.argv[1]

（B）dbutils.widgets.text("date", "null")
date = dbutils.widgets.get("date")

（C）date = spark.conf.get("date")

（D）date = dbutils.notebooks.getParam("date")

（E）input_dict = input()
date= input_dict["date"]

（A）Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.

（B）Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement.

（C）The Tungsten encoding used by Databricks is optimized for storing string data; newly-added native support for querying JSON strings means that string types are always most efficient.

（D）Human labor in writing code is the largest cost associated with data engineering workloads; as such, automating table declaration logic should be a priority in all migration workloads.

（E）Schema inference and evolution on .Databricks ensure that inferred types will always accurately match the data types used by downstream systems.

（A）Yes; both of the streams can share a single checkpoint directory.
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

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

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

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

（E）Yes; Delta Lake supports infinite concurrent writers.

（A）No permissions

（B）Can Read

（C）Can Manage

（D）Can Edit

（E）Can Run

（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