Databricks Databricks-Certified-Data-Engineer-Professional real exam prep : Databricks Certified Data Engineer Professional Exam

A platform team is creating a standardized template for Databricks Asset Bundles to support CI/CD. The template must specify defaults for artifacts, workspace root paths, and a run identity, while allowing a "dev" target to be the default and override specific paths. How should the team use databricks.yml to satisfy these requirements?

• A. Use deployment, builds, context, identity, and environments; set dev as default environment and override paths under builds.
• B. Use project, packages, environment, identity, and stages; set dev as default stage and override workspace under environment.
• C. Use bundle, artifacts, workspace, run_as, and targets at the top level; set one target with default:true and override workspace paths or artifacts under that target.
• D. Use roots, modules, profiles, actor, and targets; where profiles contain workspace and artifacts defaults and actor sets run identity.

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 made based upon what is most convenient for the workspace administrator.
• 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. 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.
• D. Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.
• 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.

A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schema. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams.
The proposed directory structure is displayed below:

Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?

• A. No; each of the streams needs to have its own checkpoint directory.
• B. Yes; both of the streams can share a single checkpoint directory.
• C. No; Delta Lake manages streaming checkpoints in the transaction log.
• D. Yes; Delta Lake supports infinite concurrent writers.
• E. No; only one stream can write to a Delta Lake table.

A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the auditing group executes the following query:
SELECT * FROM user_ltv_no_minors
Which statement describes the results returned by this query?

• A. All values for the age column will be returned as null values, all other columns will be returned with the values in user_ltv.
• B. All age values less than 18 will be returned as null values all other columns will be returned with the values in user_ltv.
• C. All records from all columns will be displayed with the values in user_ltv.
• D. All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.
• E. All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.

A job runs four independent tasks (X, Y, Z, W) in parallel to process regional sales data. The Data Engineering team recently updated its cluster policy to ban cost-prohibitive instance types. Task Y now fails due to the newly enforced cluster policy restricting the use of a specific instance type.
A data engineer needs to resolve the failure quickly without disrupting the other tasks. How should the data engineer resolve the failure of tasks?

• A. Manually create a new cluster for Task Y, update the job configuration, and trigger a full re-run.
• B. Delete the failed run, disable the cluster policy, and re-execute all tasks.
• C. Edit the global cluster policy to allow the restricted instance type, then re-run the entire job.
• D. Use "Repair run", override the cluster configuration for Task Y to use a permitted instance type, and let Databricks re-run only Task Y.