Databricks Certified Data Engineer Professional : Databricks-Certified-Data-Engineer-Professional

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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

1. 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 Auto Loader in batch mode with a daily job to overwrite the Delta table.
B) Use Lakeflow Spark Declarative Pipelines with a static DataFrame read, merge schema with spark.conf.set ("spark.databricks.delta.schema.autoMerge.enabled", "true")
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"

2. To identify the top users consuming compute resources, a data engineering team needs to monitor usage within their Databricks workspace for better resource utilization and cost control.
The team decided to use Databricks system tables, available under the System catalog in Unity Catalog, to gain detailed visibility into workspace activity. Which SQL query should the team run from the System catalog to achieve this?

A) SELECT identity_metadata.run_as AS user_email,
SUM(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email
ORDER BY total_dbus DESC
LIMIT 10
B) SELECT sku_name,
identity_metadata.created_by AS user_email,
COUNT(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10
C) SELECT sku_name,
usage_metadata.run_name AS user_email,
SUM(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10
D) SELECT sku_name,
identity_metadata.created_by AS user_email,
SUM(usage_quantity * usage_unit) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10

3. 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 roots, modules, profiles, actor, and targets; where profiles contain workspace and artifacts defaults and actor sets run identity.
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 project, packages, environment, identity, and stages; set dev as default stage and override workspace under environment.

4. A data engineer is running a groupBy aggregation on a massive user activity log grouped by user_id. A few users have millions of records, causing task skew and long runtimes. Which technique will fix the skew in this aggregation?

A) Use reduceByKey instead of groupBy to avoid shuffles.
B) Increase the Spark driver memory and retry.
C) Use salting by adding a random prefix to skewed keys before aggregation, then aggregate again after removing the prefix.
D) Filter out the skewed users before the aggregation.

5. A query is taking too long to run. After investigating the Spark UI, the data engineer discovered a significant amount of disk spill. The compute instance being used has a core-to-memory ratio of
1:2. What are the two steps the data engineer should take to minimize spillage? (Choose two.)

A) Choose a compute instance with a higher core-to-memory ratio.
B) Choose a compute instance with more disk space.
C) Choose a compute instance with more network bandwidth.
D) Reduce spark.sql.files.maxPartitionBytes.
E) Increase spark.sql.files.maxPartitionBytes.

Solutions: