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

Section	Objectives
Debugging and Deploying	- Debugging and Troubleshooting 1. Use Spark UI, cluster logs, system tables, and query profiles for diagnostics 2. Analyze errors and remediate failed job runs 3. Use Lakeflow Spark Declarative Pipelines event logs and Spark UI for debugging - Deploying CI/CD 1. Build and deploy Databricks resources using Databricks Asset Bundles 2. Integrate Git-based CI/CD workflows using Databricks Git Folders
Monitoring and Alerting	- Monitoring 1. Use system tables for resource, cost, audit, and workload monitoring 2. Use Databricks REST APIs and CLI for monitoring jobs and pipelines 3. Use Lakeflow Spark Declarative Pipelines event logs for monitoring 4. Use Query Profiler and Spark UI to monitor workloads - Alerting 1. Configure Lakeflow Jobs notifications for job status and performance issues 2. Use SQL Alerts for data quality monitoring
Cost & Performance Optimisation	- Cost Optimization 1. Understand how Unity Catalog managed tables reduce operational overhead - Delta Optimization 1. Use Change Data Feed to address streaming table limitations and improve latency 2. Apply data skipping and file pruning techniques 3. Understand deletion vectors and liquid clustering - Query Performance 1. Use Query Profile to identify performance bottlenecks 2. Identify inefficient joins and excessive data shuffling
Data Modelling	- Dimensional Modelling 1. Design dimensional models for analytical workloads - Scalable Data Models 1. Optimize data layout using Liquid Clustering 2. Understand Liquid Clustering versus partitioning and Z-Ordering 3. Design and implement scalable data models using Delta Lake
Ensuring Data Security and Compliance	- Compliance 1. Implement pipelines that detect and mask personally identifiable information 2. Develop data purging solutions according to data retention policies - Data Security 1. Use ACLs to secure workspace objects and enforce least privilege 2. Apply anonymization and pseudonymization techniques 3. Use row filters and column masks for sensitive data
Data Ingestion & Acquisition	- Design and implement data ingestion pipelines 1. Ingest Delta Lake, Parquet, ORC, Avro, JSON, CSV, XML, Text, and Binary data 2. Ingest data from message buses and cloud storage 3. Build append-only pipelines for batch and streaming data using Delta
Data Transformation, Cleansing, and Quality	- Advanced Data Transformation 1. Apply window functions, joins, and aggregations to large datasets 2. Write efficient Spark SQL and PySpark transformations - Data Quality 1. Develop data quarantining processes for invalid data 2. Apply data quality controls using Lakeflow Spark Declarative Pipelines or Auto Loader
Developing Code for Data Processing using Python and SQL	- Using Python and Tools for Development 1. Design and implement scalable Python project structures optimized for Databricks Asset Bundles 2. Manage and troubleshoot third-party library installations and dependencies 3. Develop User-Defined Functions using Pandas/Python UDFs - Building and Testing ETL Pipelines 1. Use APPLY CHANGES APIs for change data capture 2. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader 3. Develop unit and integration tests for data processing code 4. Create and automate ETL workloads using Jobs through UI, APIs, and CLI 5. Compare streaming tables and materialized views 6. Configure environments, dependencies, memory, and retry behavior 7. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines 8. Use control flow operators in pipeline components
Data Sharing and Federation	- Delta Sharing 1. Configure Databricks-to-Databricks Sharing 2. Configure sharing with external platforms using the open sharing protocol 3. Share live Lakehouse data with external computing platforms - Lakehouse Federation 1. Configure Lakehouse Federation with appropriate governance
Data Governance	- Metadata and Discoverability 1. Create and maintain descriptions and metadata for enterprise data - Unity Catalog Permissions 1. Understand the Unity Catalog permission inheritance model

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Databricks Certified Data Engineer Professional Sample Questions (Q156-Q161):

NEW QUESTION # 156
An external object storage container has been mounted to the location /mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:

After the database was successfully created and permissions configured, a member of the finance team runs the following code:

If all users on the finance team are members of the finance group, which statement describes how the tx_sales table will be created?

A. A managed table will be created in the DBFS root storage container.
B. A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.
C. An managed table will be created in the storage container mounted to /mnt/finance_eda_bucket.
D. An external table will be created in the storage container mounted to /mnt/finance eda bucket.
E. A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.

Answer: C

Explanation:
https://docs.databricks.com/en/data-governance/unity-catalog/create-schemas.html#language-SQL

NEW QUESTION # 157
A developer has successfully configured their credentials for Databricks Repos and cloned a remote Git repository. They do not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace. Which approach allows this user to share their code updates without the risk of overwriting the work of their teammates?

A. Use Repos to merge all differences and make a pull request back to the remote repository.
B. Use repos to merge all difference and make a pull request back to the remote repository.
C. Use Repos to pull changes from the remote Git repository; commit and push changes to a branch that appeared as changes were pulled.
D. Use Repos to create a new branch commit all changes and push changes to the remote Git repertory.
E. Use repos to create a fork of the remote repository commit all changes and make a pull request on the source repository

Answer: D

Explanation:
In Databricks Repos, when a user does not have privileges to make changes directly to the main branch of a cloned remote Git repository, the recommended approach is to create a new branch within the Databricks workspace. The developer can then make changes in this new branch, commit those changes, and push the new branch to the remote Git repository. This workflow allows for isolated development without affecting the main branch, enabling the developer to propose changes via a pull request from the new branch to the main branch in the remote repository. This method adheres to common Git collaboration workflows, fostering code review and collaboration while ensuring the integrity of the main branch.

NEW QUESTION # 158
A data engineer is configuring a pipeline that will potentially see late-arriving, duplicate records.
In addition to de-duplicating records within the batch, which of the following approaches allows the data engineer to deduplicate data against previously processed records as it is inserted into a Delta table?

A. Set the configuration delta.deduplicate = true.
B. VACUUM the Delta table after each batch completes.
C. Rely on Delta Lake schema enforcement to prevent duplicate records.
D. Perform a full outer join on a unique key and overwrite existing data.
E. Perform an insert-only merge with a matching condition on a unique key.

Answer: E

Explanation:
To deduplicate data against previously processed records as it is inserted into a Delta table, you can use the merge operation with an insert-only clause. This allows you to insert new records that do not match any existing records based on a unique key, while ignoring duplicate records that match existing records. For example, you can use the following syntax:
MERGE INTO target_table USING source_table ON target_table.unique_key = source_table.unique_key WHEN NOT MATCHED THEN INSERT * This will insert only the records from the source table that have a unique key that is not present in the target table, and skip the records that have a matching key. This way, you can avoid inserting duplicate records into the Delta table.

NEW QUESTION # 159
A transactions table has been liquid clustered on the columns product_id, user_id, and event_date. Which operation lacks support for cluster on write?

A. spark.writestream.format('delta').mode('append')
B. CTAS and RTAS statements
C. spark.write.format('delta').mode('append')
D. INSERT INTO operations

Answer: A

Explanation:
Delta Lake's Liquid Clustering is an advanced feature that improves query performance by dynamically clustering data without requiring costly compaction steps like traditional Z-ordering.
When performing writes to a Liquid Clustered table, some write operations automatically maintain clustering, while others do not.

NEW QUESTION # 160
A data engineer is building a customer data pipeline in Lakeflow Spark Declarative Pipelines. The source is a cloud-based event stream with limited retention containing inserts, updates, and deletes for customer records. These changes are being applied using the AUTO CDC INTO syntax to maintain an SCD Type 1 table as the target table, customer_dim. How should the data engineer build a downstream job that streams from the customer_dim table to only act on updates and delete events, processing data incrementally?

A. When stored as SCD 1, the target of AUTO CDC INTO includes updates and deletes. Streaming from customer_dim can fail due to these operations. Instead, build another stream from the original source.
B. Use ignoreChanges flag while streaming from customer_dim to avoid breaking the pipeline during updates and deletes.
C. Read change data feed from customer_dim table and apply filters to incrementally act on the change events.
D. Streaming from customer_dim table would only be possible in the case of SCD 2 retention.

Answer: C

Explanation:
Reading the change data feed from the customer_dim table enables downstream processing to react specifically to update and delete events while operating incrementally. Change data feed exposes row-level change types and versions, making it the correct mechanism for streaming only the relevant changes from an SCD Type 1 table maintained with AUTO CDC INTO.

NEW QUESTION # 161
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