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

Section	Objectives
Cost & Performance Optimization	- Optimize cost and performance 1. Apply Change Data Feed to address streaming table limitations and improve latency 2. Use query profiling to identify bottlenecks such as inefficient joins and data shuffling 3. Understand how and why Unity Catalog managed tables reduce operational overhead and maintenance burden 4. Understand Delta optimization techniques such as deletion vectors and liquid clustering 5. Understand Databricks query optimization techniques for large datasets, including data skipping and file pruning
Monitoring and Alerting	- Alerting 1. Use the Workflows UI and Jobs API to configure notifications for job status and performance issues 2. Use SQL Alerts to monitor data quality - Monitoring 1. Use Query Profile and Spark UI to monitor workloads 2. Use Lakeflow Declarative Pipelines event logs to monitor pipelines 3. Use Databricks REST APIs and Databricks CLI to monitor jobs and pipelines 4. Use system tables for observability of resource utilization, cost, auditing, and workloads
Data Ingestion & Acquisition	- Design and implement data ingestion pipelines 1. Ingest formats including Delta Lake, Parquet, ORC, AVRO, JSON, CSV, XML, text, and binary data from sources such as message buses and cloud storage 2. Create an append-only data pipeline capable of handling both batch and streaming data using Delta
Data Governance	- Govern enterprise data 1. Demonstrate understanding of the Unity Catalog permission inheritance model 2. Create and add descriptions and metadata to enterprise data to improve discoverability
Data Sharing and Federation	- Share and federate data 1. Use Delta Sharing to share live data from the Lakehouse with any computing platform 2. Demonstrate secure Delta Sharing between Databricks deployments using Databricks-to-Databricks sharing or with external platforms using the open sharing protocol 3. Configure Lakehouse Federation with appropriate governance across supported source systems
Ensuring Data Security and Compliance	- Ensuring Compliance 1. Develop data purging solutions that comply with data retention policies 2. Implement compliant batch and streaming pipelines that detect and mask PII - Applying Data Security Mechanisms 1. Use ACLs to secure workspace objects and enforce the principle of least privilege 2. Use row filters and column masks to protect sensitive table data 3. Apply anonymization and pseudonymization methods including hashing, tokenization, suppression, and generalization
Data Modeling	- Design and optimize data models 1. Simplify data layout decisions and optimize query performance using liquid clustering 2. Identify the benefits of liquid clustering over partitioning and Z-Ordering 3. Design dimensional models for analytical workloads with efficient querying and aggregation 4. Design and implement scalable data models using Delta Lake to manage large datasets
Developing Code for Data Processing using Python and SQL	- Building and Testing an ETL Pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark 1. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI 2. Create pipeline components using control flow operators such as if/else and foreach 3. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools 4. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader 5. Explain the advantages and disadvantages of streaming tables compared to materialized views 6. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines 7. Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines 8. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior - Using Python and Tools for Development 1. Manage and troubleshoot external third-party library installations and dependencies, including PyPI packages, local wheels, and source archives 2. Develop User-Defined Functions using Pandas/Python UDF 3. Design and implement a scalable Python project structure optimized for Databricks Asset Bundles, enabling modular development, deployment automation, and CI/CD integration
Debugging and Deploying	- Deploying CI/CD 1. Build and deploy Databricks resources using Databricks Asset Bundles 2. Configure and integrate Git-based CI/CD workflows using Databricks Git folders for notebook and code deployment - Debugging and Troubleshooting 1. Analyze errors and remediate failed job runs using job repairs and parameter overrides 2. Identify diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors 3. Use Lakeflow Declarative Pipelines event logs and Spark UI to debug Lakeflow Declarative Pipelines and Spark pipelines
Data Transformation, Cleansing, and Quality	- Transform and validate data 1. Write efficient Spark SQL and PySpark code for advanced transformations including window functions, joins, and aggregations 2. Develop a quarantining process for bad data with Lakeflow Declarative Pipelines or Auto Loader in classic jobs

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Databricks Certified Data Engineer Professional Sample Questions (Q45-Q50):

NEW QUESTION # 45
A data architect is designing a Databricks solution to efficiently process data for different business requirements. In which scenario should a data engineer use a materialized view compared to a streaming table?

A. Ingesting data from Apache Kafka topics with sub-second processing requirements for immediate alerting.
B. Implementing a CDC (Change Data Capture) pipeline that needs to detect and respond to database changes within seconds.
C. Precomputing complex aggregations and joins from multiple large tables to accelerate BI dashboard performance.
D. Processing high-volume, continuous clickstream data from a website to monitor user behavior in real-time.

Answer: C

Explanation:
Materialized views in Databricks are optimized for precomputing and caching results of complex SQL queries, joins, and aggregations. They store query outputs physically and automatically refresh on a schedule or incremental change basis, drastically improving BI dashboard performance and reducing compute costs.
Conversely, streaming tables are designed for real-time data ingestion and processing, enabling event-driven analytics and low-latency use cases.
Databricks documentation explicitly recommends materialized views for analytical workloads with periodic updates and streaming tables for continuously updating sources. Therefore, the correct choice is C, where complex aggregations from large tables benefit most from materialized precomputation for fast reporting.

NEW QUESTION # 46
Each configuration below is identical to the extent that each cluster has 400 GB total of RAM 160 total cores and only one Executor per VM.
Given an extremely long-running job for which completion must be guaranteed, which cluster configuration will be able to guarantee completion of the job in light of one or more VM failures?

A. - Total VMs: 1
- 400 GB per Executor
- 160 Cores/Executor
B. - Total VMs: 16
- 25 GB per Executor
- 10 Cores / Executor
C. - Total VMs: 4
- 100 GB per Executor
- 40 Cores / Executor
D. - Total VMs: 8
- 50 GB per Executor
- 20 Cores / Executor
E. - Total VMs: 2
- 200 GB per Executor
- 80 Cores / Executor

Answer: B

NEW QUESTION # 47
The following table consists of items found in user carts within an e-commerce website.

The following MERGE statement is used to update this table using an updates view, with schema evolution enabled on this table.

How would the following update be handled?

A. The new nested field is added to the target schema, and files underlying existing records are updated to include NULL values for the new field.
B. The new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.
C. The update throws an error because changes to existing columns in the target schema are not supported.
D. The update is moved to separate ''restored'' column because it is missing a column expected in the target schema.

Answer: A

Explanation:
With schema evolution enabled in Databricks Delta tables, when a new field is added to a record through a MERGE operation, Databricks automatically modifies the table schema to include the new field. In existing records where this new field is not present, Databricks will insert NULL values for that field. This ensures that the schema remains consistent across all records in the table, with the new field being present in every record, even if it is NULL for records that did not originally include it.

NEW QUESTION # 48
Although the Databricks Utilities Secrets module provides tools to store sensitive credentials and avoid accidentally displaying them in plain text users should still be careful with which credentials are stored here and which users have access to using these secrets.
Which statement describes a limitation of Databricks Secrets?

A. Secrets are stored in an administrators-only table within the Hive Metastore; database administrators have permission to query this table by default.
B. Iterating through a stored secret and printing each character will display secret contents in plain text.
C. The Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials.
D. Account administrators can see all secrets in plain text by logging on to the Databricks Accounts console.
E. Because the SHA256 hash is used to obfuscate stored secrets, reversing this hash will display the value in plain text.

Answer: C

Explanation:
This is the correct answer because it describes a limitation of Databricks Secrets. Databricks Secrets is a module that provides tools to store sensitive credentials and avoid accidentally displaying them in plain text. Databricks Secrets allows creating secret scopes, which are collections of secrets that can be accessed by users or groups. Databricks Secrets also allows creating and managing secrets using the Databricks CLI or the Databricks REST API. However, a limitation of Databricks Secrets is that the Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials. Therefore, users should still be careful with which credentials are stored in Databricks Secrets and which users have access to using these secrets.

NEW QUESTION # 49
The security team is exploring whether or not the Databricks secrets module can be leveraged for connecting to an external database.
After testing the code with all Python variables being defined with strings, they upload the password to the secrets module and configure the correct permissions for the currently active user. They then modify their code to the following (leaving all other variables unchanged).

Which statement describes what will happen when the above code is executed?

A. The connection to the external table will fail; the string "redacted" will be printed.
B. The connection to the external table will succeed; the string "redacted" will be printed.
C. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the encoded password will be saved to DBFS.
D. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the password will be printed in plain text.
E. The connection to the external table will succeed; the string value of password will be printed in plain text.

Answer: B

Explanation:
This is the correct answer because the code is using the dbutils.secrets.get method to retrieve the password from the secrets module and store it in a variable. The secrets module allows users to securely store and access sensitive information such as passwords, tokens, or API keys. The connection to the external table will succeed because the password variable will contain the actual password value. However, when printing the password variable, the string "redacted" will be displayed instead of the plain text password, as a security measure to prevent exposing sensitive information in notebooks.

NEW QUESTION # 50
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