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

Section	Objectives
Ensuring Data Security and Compliance	- Applying Data Security Mechanisms 1. Use row filters and column masks to protect sensitive table data 2. Use ACLs to secure workspace objects and enforce the principle of least privilege 3. Apply anonymization and pseudonymization methods including hashing, tokenization, suppression, and generalization - 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
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
Debugging and Deploying	- 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 - 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
Cost & Performance Optimization	- Optimize cost and performance 1. Use query profiling to identify bottlenecks such as inefficient joins and data shuffling 2. Apply Change Data Feed to address streaming table limitations and improve latency 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
Developing Code for Data Processing using Python and SQL	- Building and Testing an ETL Pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark 1. Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines 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. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines 5. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI 6. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader 7. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior 8. Explain the advantages and disadvantages of streaming tables compared to materialized views - Using Python and Tools for Development 1. Design and implement a scalable Python project structure optimized for Databricks Asset Bundles, enabling modular development, deployment automation, and CI/CD integration 2. Manage and troubleshoot external third-party library installations and dependencies, including PyPI packages, local wheels, and source archives 3. Develop User-Defined Functions using Pandas/Python UDF
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. Demonstrate secure Delta Sharing between Databricks deployments using Databricks-to-Databricks sharing or with external platforms using the open sharing protocol 2. Configure Lakehouse Federation with appropriate governance across supported source systems 3. Use Delta Sharing to share live data from the Lakehouse with any computing platform
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
Monitoring and Alerting	- 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 - Alerting 1. Use SQL Alerts to monitor data quality 2. Use the Workflows UI and Jobs API to configure notifications for job status and performance issues
Data Modeling	- Design and optimize data models 1. Identify the benefits of liquid clustering over partitioning and Z-Ordering 2. Design and implement scalable data models using Delta Lake to manage large datasets 3. Simplify data layout decisions and optimize query performance using liquid clustering 4. Design dimensional models for analytical workloads with efficient querying and aggregation

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Most Probable Real Exam Questions in Certified-Data-Engineer-Professional Databricks Certified Data Engineer Professional PDF Dumps Format

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Databricks Certified Data Engineer Professional Sample Questions (Q104-Q109):

NEW QUESTION # 104
Which REST API call can be used to review the notebooks configured to run as tasks in a multi- task job?

A. /jobs/get
B. /jobs/runs/get-output
C. /jobs/list
D. /jobs/runs/list
E. /jobs/runs/get

Answer: A

Explanation:
https://docs.databricks.com/api/workspace/jobs/getresponses/settings/tasks/notebook_task/noteb ook_path

NEW QUESTION # 105
A data engineer wants to ingest a large collection of image files (JPEG and PNG) from cloud object storage into a Unity Catalog-managed table for analysis and visualization. Which two configurations and practices are recommended to incrementally ingest these images into the table? (Choose two.)

A. Use Auto Loader and set cloudFiles.format to "IMAGE".
B. Use Auto Loader and set cloudFiles.format to "TEXT".
C. Use Auto Loader and set cloudFiles.format to "BINARYFILE".
D. Move files to a volume and read with SQL editor.
E. Use the pathGlobFilter option to select only image files (e.g., "*.jpg,*.png").

Answer: C,E

Explanation:
Databricks Auto Loader supports ingestion of binary file formats using the cloudFiles.format option. For ingesting JPEG or PNG image files, the correct setting is "BINARYFILE", which loads the raw binary content and file metadata into a DataFrame. Additionally, when processing files from object storage, it is best practice to apply pathGlobFilter to limit ingestion to specific file types and reduce unnecessary scanning of non-image files. Options like "IMAGE" or "TEXT" are invalid, and using volumes with SQL editors does not provide incremental ingestion. Therefore, combining Auto Loader with cloudFiles.format="BINARYFILE" and pathGlobFilter ensures scalable, incremental ingestion of image data into Unity Catalog tables.

NEW QUESTION # 106
When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

A. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
B. Cluster: New Job Cluster;
Retries: None;
Maximum Concurrent Runs: 1
C. Cluster: New Job Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: Unlimited
D. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
E. Cluster: Existing All-Purpose Cluster;
Retries: None;
Maximum Concurrent Runs: 1

Answer: D

Explanation:
The configuration that automatically recovers from query failures and keeps costs low is to use a new job cluster, set retries to unlimited, and set maximum concurrent runs to 1. This configuration has the following advantages:
A new job cluster is a cluster that is created and terminated for each job run. This means that the cluster resources are only used when the job is running, and no idle costs are incurred. This also ensures that the cluster is always in a clean state and has the latest configuration and libraries for the job.
Setting retries to unlimited means that the job will automatically restart the query in case of any failure, such as network issues, node failures, or transient errors. This improves the reliability and availability of the streaming job, and avoids data loss or inconsistency. Setting maximum concurrent runs to 1 means that only one instance of the job can run at a time. This prevents multiple queries from competing for the same resources or writing to the same output location, which can cause performance degradation or data corruption. Therefore, this configuration is the best practice for scheduling Structured Streaming jobs for production, as it ensures that the job is resilient, efficient, and consistent.

NEW QUESTION # 107
The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:

Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?

A. Define a view against the products_per_order table and define the dashboard against this view.
B. Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.
C. Populate the dashboard by configuring a nightly batch job to save the required values as a table overwritten with each update.
D. Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.
E. Configure a webhook to execute an incremental read against products_per_order each time the dashboard is refreshed.

Answer: C

NEW QUESTION # 108
A data engineer is optimizing a managed Delta table that suffers from data skew and frequently changing query filter columns. The engineer wants to avoid costly data rewrites when query patterns evolve. The table size is under 1 TB. How should the data engineer meet this requirement?

A. Apply Z-ordering, since it allows flexible reorganization of data layout without rewriting existing files and adapts easily to new filter columns.
B. Use Hive-style partitioning, as it provides efficient data skipping and is easy to change partition columns at any time.
C. Enable liquid clustering, as it efficiently handles data skew, allows clustering keys to be changed without rewriting existing data, and adapts to evolving query patterns.
D. Combine partitioning and Z-ordering to maximize flexibility and minimize maintenance as query patterns change.

Answer: C

Explanation:
Liquid clustering is designed for managed tables under 1TB with evolving query patterns. It efficiently addresses data skew, continuously optimizes data layout, and allows clustering keys to be changed without requiring full data rewrites, making it well suited for frequently changing filter columns while minimizing maintenance overhead.

NEW QUESTION # 109
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