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

Section	Weight	Objectives
Developing Code for Data Processing using Python and SQL	22%	- Write efficient and maintainable code - Implement complex data processing logic - Use Databricks-specific libraries and APIs
Cost & Performance Optimisation	13%	- Optimize compute and storage resources - Improve query and pipeline performance - Apply cost management best practices
Ensuring Data Security and Compliance	10%	- Ensure data privacy and compliance - Secure data at rest and in transit - Implement access control and permissions
Monitoring and Alerting	10%	- Track data lineage and metrics - Set up alerts and notifications - Monitor pipeline performance and health
Data Modelling	6%	- Implement dimensional and relational models - Optimize table design and partitioning - Design Medallion Architecture
Data Sharing and Federation	5%	- Use Delta Sharing for secure data sharing - Manage cross-platform data access - Implement Lakehouse Federation
Data Transformation, Cleansing, and Quality	10%	- Apply data cleansing and validation rules - Enforce data quality standards - Implement schema evolution and management
Data Ingestion & Acquisition	7%	- Ingest data from diverse sources - Handle incremental and batch data loads - Use Auto Loader and structured streaming
Data Governance	7%	- Use Unity Catalog for governance - Manage data assets and metadata - Enforce data policies and standards
Debugging and Deploying	10%	- Implement CI/CD and DevOps practices - Troubleshoot and debug pipelines - Deploy using Asset Bundles, CLI, and APIs

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q95-Q100):

NEW QUESTION # 95
A data engineer is evaluating tools to build a production-grade data pipeline. The team must process change data from cloud object storage, filter out or isolate invalid records, and ensure the timely delivery of clean data to downstream consumers. The team is small, under tight deadlines, and wants to minimize operational overhead while keeping pipelines auditable and maintainable.
Which approach should the data engineer implement?

A. Ingest data directly into Delta tables via Spark jobs, apply data quality filters using UDFs, and use LDP for creating Materialized Views.
B. Use a hybrid approach: Ingest with Auto Loader into Bronze tables, then process using SQL queries in Databricks Workflows to generate cleaned Silver and Gold tables on a schedule.
C. Implement ingestion using Auto Loader with Structured Streaming, and manage invalid data handling and table updates using checkpointing and merge logic.
D. Use LDP to build declarative pipelines with Streaming Tables and Materialized Views, leveraging built-in support for data expectations and incremental processing.

Answer: D

Explanation:
LDP provides a declarative framework for building production-grade pipelines with minimal operational overhead. Streaming Tables and Materialized Views handle incremental processing automatically, while built-in data expectations allow invalid records to be filtered or isolated in a consistent and auditable way. This approach is well suited for small teams under tight deadlines, as it simplifies maintenance, improves reliability, and ensures timely delivery of clean data to downstream consumers.

NEW QUESTION # 96
What is a method of installing a Python package scoped at the notebook level to all nodes in the currently active cluster?

A. Use &sh install in a notebook cell
B. Run source env/bin/activate in a notebook setup script
C. Install libraries from PyPi using the cluster UI
D. Use &Pip install in a notebook cell

Answer: C

Explanation:
Installing a Python package scoped at the notebook level to all nodes in the currently active cluster in Databricks can be achieved by using the Libraries tab in the cluster UI. This interface allows you to install libraries across all nodes in the cluster. While the %pip command in a notebook cell would only affect the driver node, using the cluster UI ensures that the package is installed on all nodes.

NEW QUESTION # 97
The data engineer team has been tasked with configured connections to an external database that does not have a supported native connector with Databricks. The external database already has data security configured by group membership. These groups map directly to user group already created in Databricks that represent various teams within the company. A new login credential has been created for each group in the external database. The Databricks Utilities Secrets module will be used to make these credentials available to Databricks users. Assuming that all the credentials are configured correctly on the external database and group membership is properly configured on Databricks, which statement describes how teams can be granted the minimum necessary access to using these credentials?

A. "Manage" permission should be set on a secret scope containing only those credentials that will be used by a given team.
B. "Read'' permissions should be set on a secret key mapped to those credentials that will be used by a given team.
C. No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.
D. "Read" permissions should be set on a secret scope containing only those credentials that will be used by a given team.

Answer: D

Explanation:
In Databricks, using the Secrets module allows for secure management of sensitive information such as database credentials. Granting 'Read' permissions on a secret key that maps to database credentials for a specific team ensures that only members of that team can access these credentials. This approach aligns with the principle of least privilege, granting users the minimum level of access required to perform their jobs, thus enhancing security.

NEW QUESTION # 98
The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity.
Each user should be able to execute workloads against their assigned clusters at any time of the day.
Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.

A. "Can Manage" privileges on the required cluster
B. Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster
C. Cluster creation allowed. "Can Attach To" privileges on the required cluster
D. Cluster creation allowed. "Can Restart" privileges on the required cluster
E. "Can Restart" privileges on the required cluster

Answer: E

Explanation:
https://learn.microsoft.com/en-us/azure/databricks/security/auth-authz/access-control/cluster-acl
https://docs.databricks.com/en/security/auth-authz/access-control/cluster-acl.html

NEW QUESTION # 99
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 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
B. 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
C. 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
D. 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

Answer: B

Explanation:
The system.billing.usage table in the Unity Catalog System schema provides detailed usage metrics for each workload in the workspace. The field identity_metadata.run_as identifies the user or service principal under which the job or query executed. Summing usage_quantity provides total DBU (Databricks Unit) consumption per user. According to Databricks documentation, this table is the authoritative source for monitoring workspace cost drivers, showing compute SKU, user, and DBU consumption over time. Grouping by identity_metadata.run_as and summing usage_quantity produces the correct aggregation to determine top users. Other queries use non- existent or incorrect fields (created_by, run_name, or multiplied usage quantities), which do not reflect actual billing metrics.

NEW QUESTION # 100
......

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