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

Section	Weight	Objectives
Topic 1: Data Modeling and Storage	20%	- File Formats - Storage Optimization - Data Modeling
Topic 2: Data Quality and Governance	12%	- Data Lineage - Governance - Data Quality
Topic 3: Monitoring and Troubleshooting	16%	- Troubleshooting - Monitoring - Performance Optimization
Topic 4: Databricks Lakehouse Platform	24%	- Data Management - Lakehouse Architecture - Unity Catalog - Delta Lake
Topic 5: Data Processing	28%	- ETL Pipelines - Spark SQL - Structured Streaming - Data Transformation

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q184-Q189):

NEW QUESTION # 184
What is the first line of a Databricks Python notebook when viewed in a text editor?

A. # Databricks notebook source
B. # MAGIC %python
C. // Databricks notebook source
D. %python
E. -- Databricks notebook source

Answer: A

Explanation:
https://docs.databricks.com/en/notebooks/notebook-export-import.html#import-a-file-and-convert- it-to-a-notebook

NEW QUESTION # 185
A data engineer wants to create a cluster using the Databricks CLI for a big ETL pipeline. The cluster should have five workers, one driver of type i3.xlarge, and should use the '14.3.x- scala2.12' runtime. Which command should the data engineer use?

A. databricks clusters add 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster
B. databricks clusters create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster- name DataEngineer_cluster
C. databricks compute create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster- name Data Engineer_cluster
D. databricks compute add 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster

Answer: B

Explanation:
The correct Databricks CLI command to create a new cluster is databricks clusters create. You specify the runtime with --spark-version (here '14.3.x-scala2.12'), the number of workers with -- num-workers, the node type with --node-type-id, and the cluster name with --cluster-name. This command properly initializes the cluster with the desired configuration.

NEW QUESTION # 186
A data organization has adopted Delta Sharing to securely distribute curated datasets from a Unity Catalog-enabled workspace. The data engineering team shares large Delta tables internally via Databricks-to-Databricks and externally via Open Sharing for aggregated reports. While testing, they encounter challenges related to access control, data update visibility, and shareable object types. What is a limitation of the Delta Sharing protocol or implementation when used with Databricks-to-Databricks or Open Sharing?

A. Delta Sharing does not support Unity Catalog-enabled tables; only legacy Hive Metastore tables are shareable.
B. With Databricks-to-Databricks sharing, Unity Catalog recipients must re-ingest data manually using COPY INTO or REST APIs.
C. Delta Sharing (both Databricks-to-Databricks and Open Sharing) allows recipients to modify the source data if they have select privileges.
D. With Open Sharing, recipients cannot access Volumes, Models, or notebooks -- only static Delta tables are supported.

Answer: D

Explanation:
According to Databricks' documentation, Open Sharing allows secure sharing of Delta tables to any recipient via a REST-based protocol without requiring a Databricks account. However, the Open Sharing protocol is limited to static Delta tables--it does not support sharing of Unity Catalog objects like Volumes, Machine Learning models, or notebooks. Only Databricks-to- Databricks sharing supports dynamic data sharing with update visibility and streaming reads.
Thus, the inability to share non-table objects in Open Sharing represents a known limitation of the protocol. Option A accurately reflects this constraint as described in Delta Sharing design principles and documentation.

NEW QUESTION # 187
All records from an Apache Kafka producer are being ingested into a single Delta Lake table with the following schema:
key BINARY, value BINARY, topic STRING, partition LONG, offset LONG, timestamp LONG There are 5 unique topics being ingested. Only the "registration" topic contains Personal Identifiable Information (PII). The company wishes to restrict access to PII. The company also wishes to only retain records containing PII in this table for 14 days after initial ingestion.
However, for non-PII information, it would like to retain these records indefinitely.
Which of the following solutions meets the requirements?

A. Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.
B. Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.
C. All data should be deleted biweekly; Delta Lake's time travel functionality should be leveraged to maintain a history of non-PII information.
D. Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.
E. Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.

Answer: D

Explanation:
By default partitionning by a column will create a separate folder for each subset data linked to the partition.

NEW QUESTION # 188
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 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.
B. Use deployment, builds, context, identity, and environments; set dev as default environment and override paths under builds.
C. Use roots, modules, profiles, actor, and targets; where profiles contain workspace and artifacts defaults and actor sets run identity.
D. Use project, packages, environment, identity, and stages; set dev as default stage and override workspace under environment.

Answer: A

Explanation:
In Databricks Asset Bundles, the databricks.yml file defines all top-level configuration keys, including bundle, artifacts, workspace, run_as, and targets. The targets section defines specific deployment contexts (for example, dev, test, prod). Setting default: true for a target marks it as the default environment. Overrides for workspace paths and artifact configurations can be defined inside each target while keeping defaults at the top level.

NEW QUESTION # 189
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