The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs UI. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.
What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

• A.Can Manage
• B.Can Run
• C.No permissions
• D.Can Read
• E.Can Edit

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A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create.

Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?

• A.Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.
• B.The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.
• C.Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.
• D.One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.
• E.The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.

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A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?

• A.Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.
• B.Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.
• C.Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.
• D.Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.

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A data engineer is building a streaming data pipeline to ingest JSON files from cloud storage into a Delta Lake table. The pipeline must process files incrementally, handle schema evolution automatically, ensure exactly-once processing, and minimize manual infrastructure management.
How should the data engineer fulfill these requirements?

• A.Use Lakeflow Spark Declarative Pipelines with a static DataFrame read, merge schema with spark.conf.set ("spark.databricks.delta.schema.autoMerge.enabled", "true")
• B.Use Auto Loader in batch mode with a daily job to overwrite the Delta table.
• C.Use Lakeflow Spart Declarative Pipelines with Auto Loader and enabling schema inference with
  "cloudFiles.schemaEvolutionMode"= "addNewColumns"
• D.Use traditional Spark Structured Streaming with Auto Loader, manually configuring checkpoints location and enabling schema inference with "mergeSchema"= "true"

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A security analytics pipeline must enrich billions of raw connection logs with geolocation data.
The join hinges on finding which IPv4 range each event's address falls into.
Table 1: network_events ( 5 billion rows)
event_id ip_int
42 3232235777
Table 2: ip_ranges ( 2 million rows)
start_ip_int end_ip_int country
3232235520 3232236031 US
The query is currently very slow:
SELECT n.event_id, n.ip_int, r.country
FROM network_events n
JOIN ip_ranges r
ON n.ip_int BETWEEN r.start_ip_int AND r.end_ip_int;
Which change will most dramatically accelerate the query while preserving its logic?

• A.Increase spark.sql.shuffle.partitions from 200 to 10000.
• B.Add a range-join hint /*+ RANGE_JOIN(r, 65536) */.
• C.Force a sort-merge join with /*+ MERGE(r) */.
• D.Add a broadcast hint: /*+ BROADCAST(r) */ for ip_ranges.

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