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

Section	Objectives
Topic 1: Monitoring and Alerting	- Monitoring 1. Use Databricks REST APIs and CLI for monitoring jobs and pipelines 2. Use Query Profiler and Spark UI to monitor workloads 3. Use Lakeflow Spark Declarative Pipelines event logs for monitoring 4. Use system tables for resource, cost, audit, and workload monitoring - Alerting 1. Configure Lakeflow Jobs notifications for job status and performance issues 2. Use SQL Alerts for data quality monitoring
Topic 2: Data Ingestion & Acquisition	- Design and implement data ingestion pipelines 1. Ingest Delta Lake, Parquet, ORC, Avro, JSON, CSV, XML, Text, and Binary data 2. Build append-only pipelines for batch and streaming data using Delta 3. Ingest data from message buses and cloud storage
Topic 3: 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
Topic 4: Developing Code for Data Processing using Python and SQL	- Using Python and Tools for Development 1. Manage and troubleshoot third-party library installations and dependencies 2. Design and implement scalable Python project structures optimized for Databricks Asset Bundles 3. Develop User-Defined Functions using Pandas/Python UDFs - Building and Testing ETL Pipelines 1. Compare streaming tables and materialized views 2. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader 3. Configure environments, dependencies, memory, and retry behavior 4. Use control flow operators in pipeline components 5. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines 6. Create and automate ETL workloads using Jobs through UI, APIs, and CLI 7. Develop unit and integration tests for data processing code 8. Use APPLY CHANGES APIs for change data capture
Topic 5: Cost & Performance Optimisation	- Query Performance 1. Use Query Profile to identify performance bottlenecks 2. Identify inefficient joins and excessive data shuffling - Delta Optimization 1. Apply data skipping and file pruning techniques 2. Understand deletion vectors and liquid clustering 3. Use Change Data Feed to address streaming table limitations and improve latency - Cost Optimization 1. Understand how Unity Catalog managed tables reduce operational overhead
Topic 6: Data Transformation, Cleansing, and Quality	- Advanced Data Transformation 1. Write efficient Spark SQL and PySpark transformations 2. Apply window functions, joins, and aggregations to large datasets - Data Quality 1. Apply data quality controls using Lakeflow Spark Declarative Pipelines or Auto Loader 2. Develop data quarantining processes for invalid data
Topic 7: Data Modelling	- Dimensional Modelling 1. Design dimensional models for analytical workloads - Scalable Data Models 1. Understand Liquid Clustering versus partitioning and Z-Ordering 2. Optimize data layout using Liquid Clustering 3. Design and implement scalable data models using Delta Lake
Topic 8: Ensuring Data Security and Compliance	- Compliance 1. Develop data purging solutions according to data retention policies 2. Implement pipelines that detect and mask personally identifiable information - Data Security 1. Use row filters and column masks for sensitive data 2. Use ACLs to secure workspace objects and enforce least privilege 3. Apply anonymization and pseudonymization techniques
Topic 9: Debugging and Deploying	- Debugging and Troubleshooting 1. Use Lakeflow Spark Declarative Pipelines event logs and Spark UI for debugging 2. Use Spark UI, cluster logs, system tables, and query profiles for diagnostics 3. Analyze errors and remediate failed job runs - Deploying CI/CD 1. Integrate Git-based CI/CD workflows using Databricks Git Folders 2. Build and deploy Databricks resources using Databricks Asset Bundles
Topic 10: 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

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Databricks Certified Data Engineer Professional Sample Questions (Q107-Q112):

NEW QUESTION # 107
A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on task A.
If tasks A and B complete successfully but task C fails during a scheduled run, which statement describes the resulting state?

A. All logic expressed in the notebook associated with tasks A and B will have been successfully completed; some operations in task C may have completed successfully.
B. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task C failed, all commits will be rolled back automatically.
C. All logic expressed in the notebook associated with task A will have been successfully completed; tasks B and C will not commit any changes because of stage failure.
D. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until ail tasks have successfully been completed.
E. All logic expressed in the notebook associated with tasks A and B will have been successfully completed; any changes made in task C will be rolled back due to task failure.

Answer: A

Explanation:
The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV.
The resulting state after running the second command is that an external table will be created in the storage container mounted to /mnt/finance_eda_bucket with the new name prod.sales_by_store. The command will not change any data or move any files in the storage container; it will only update the table reference in the metastore and create a new Delta transaction log for the renamed table.

NEW QUESTION # 108
A data team is working to optimize an existing large, fast-growing table 'orders' with high cardinality columns, which experiences significant data skew and requires frequent concurrent writes. The team notice that the columns 'user_id', 'event_timestamp' and 'product_id' are heavily used in analytical queries and filters, although those keys may be subject to change in the future due to different business requirements. Which partitioning strategy should the team choose to optimize the table for immediate data skipping, incremental management over time, and flexibility?

A. Z-order the table with OPTIMIZE orders ZORDER BY (user_id, product_id, event_timestamp)
B. Use z-order after partitiing the table: OPTIMIZE orders ZORDER BY (user_id, product_id) WHERE event_timestamp = current date () - 1 DAY
C. Cluster the table with: ALTER TABLE orders CLUSTER BY user_id, product_id, event_timestamp
D. Partition the table with: ALTER TABLE orders PARTITION BY user_id, product_id, event_timestamp

Answer: A

Explanation:
Z-ordering optimizes data skipping for selective queries on high-cardinality columns without physically repartitioning the table, making it flexible if query patterns change. Using OPTIMIZE ...
ZORDER BY (user_id, product_id, event_timestamp) improves query performance for filters and joins while allowing incremental writes, avoiding the data skew and maintenance overhead that explicit partitioning or clustering could introduce.

NEW QUESTION # 109
A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the marketing group executes the following query:
SELECT * FROM email_ltv
Which statement describes the results returned by this query?

A. Three columns will be returned, but one column will be named "redacted" and contain only null values.
B. Only the email and itv columns will be returned; the email column will contain all null values.
C. The email and ltv columns will be returned with the values in user itv.
D. Only the email and ltv columns will be returned; the email column will contain the string
"REDACTED" in each row.
E. The email, age. and ltv columns will be returned with the values in user ltv.

Answer: D

Explanation:
The code creates a view called email_ltv that selects the email and ltv columns from a table called user_ltv, which has the following schema: email STRING, age INT, ltv INT. The code also uses the CASE WHEN expression to replace the email values with the string "REDACTED" if the user is not a member of the marketing group. The user who executes the query is not a member of the marketing group, so they will only see the email and ltv columns, and the email column will contain the string "REDACTED" in each row.

NEW QUESTION # 110
Which distribution does Databricks support for installing custom Python code packages?

A. sbt
B. CRAM
C. CRAN
D. jars
E. nom
F. Wheels

Answer: E

Explanation:
https://learn.microsoft.com/en-us/azure/databricks/workflows/jobs/how-to/use-python-wheels-in-workflows

NEW QUESTION # 111
A nightly batch job is configured to ingest all data files from a cloud object storage container where records are stored in a nested directory structure YYYY/MM/DD. The data for each date represents all records that were processed by the source system on that date, noting that some records may be delayed as they await moderator approval. Each entry represents a user review of a product and has the following schema:
user_id STRING, review_id BIGINT, product_id BIGINT, review_timestamp TIMESTAMP, review_text STRING The ingestion job is configured to append all data for the previous date to a target table reviews_raw with an identical schema to the source system. The next step in the pipeline is a batch write to propagate all new records inserted into reviews_raw to a table where data is fully deduplicated, validated, and enriched.
Which solution minimizes the compute costs to propagate this batch of data?

A. Perform a batch read on the reviews_raw table and perform an insert-only merge using the natural composite key user_id, review_id, product_id, review_timestamp.
B. Use Delta Lake version history to get the difference between the latest version of reviews_raw and one version prior, then write these records to the next table.
C. Reprocess all records in reviews_raw and overwrite the next table in the pipeline.
D. Filter all records in the reviews_raw table based on the review_timestamp; batch append those records produced in the last 48 hours.
E. Configure a Structured Streaming read against the reviews_raw table using the trigger once execution mode to process new records as a batch job.

Answer: E

Explanation:
https://www.databricks.com/blog/2017/05/22/running-streaming-jobs-day-10x-cost-savings.html

NEW QUESTION # 112
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