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

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

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Databricks Certified Data Engineer Professional Sample Questions (Q137-Q142):

NEW QUESTION # 137
A data engineer us ingesting JSON files from cloud object storage using Databricks Auto Loader.
The source folder may occasionally receive large files of data, which risks overwhelming the stream. To ensure predictable micro-batch sizes, the team wants to throttle ingestion based on the volume of data scanned at 1 GB, regardless of the number of files. Which Auto Loader configuration should the data engineer used to achieve this?

A. Configure cloudFiles.maxBytesPerTrigger with 1 GB to place a limit.
B. Configure cloudFiles.maxFilesPerTrigger and estimate the average file size to approximate a size-based throttle of 1 GB.
C. Configure cloudFiles.maxSizePerTrigger with 1 GB to place a limit.
D. Configure cloudFiles.maxPartitionBytes with 1GB to limit data in each partition.

Answer: A

Explanation:
cloudFiles.maxBytesPerTrigger limits the total volume of data scanned in each micro-batch based on size rather than file count. Setting it to 1 GB ensures predictable ingestion throughput even when large files arrive, preventing any single trigger from overwhelming the streaming job.

NEW QUESTION # 138
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. Filter all records in the reviews_raw table based on the review_timestamp; batch append those records produced in the last 48 hours.
B. Configure a Structured Streaming read against the reviews_raw table using the trigger once execution mode to process new records as a batch job.
C. Reprocess all records in reviews_raw and overwrite the next table in the pipeline.
D. 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.
E. 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.

Answer: B

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

NEW QUESTION # 139
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. No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.
B. "Read'' permissions should be set on a secret key mapped to those credentials that will be used by a given team.
C. "Read" permissions should be set on a secret scope containing only those credentials that will be used by a given team.
D. "Manage" permission should be set on a secret scope containing only those credentials that will be used by a given team.

Answer: C

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 # 140
Which statement describes the correct use of pyspark.sql.functions.broadcast?

A. It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.
B. It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.
C. It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.
D. It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.
E. It marks a column as small enough to store in memory on all executors, allowing a broadcast join.

Answer: D

Explanation:
https://spark.apache.org/docs/3.1.3/api/python/reference/api/pyspark.sql.functions.broadcast.html The broadcast function in PySpark is used in the context of joins. When you mark a DataFrame with broadcast, Spark tries to send this DataFrame to all worker nodes so that it can be joined with another DataFrame without shuffling the larger DataFrame across the nodes. This is particularly beneficial when the DataFrame is small enough to fit into the memory of each node. It helps to optimize the join process by reducing the amount of data that needs to be shuffled across the cluster, which can be a very expensive operation in terms of computation and time.
The pyspark.sql.functions.broadcast function in PySpark is used to hint to Spark that a DataFrame is small enough to be broadcast to all worker nodes in the cluster. When this hint is applied, Spark can perform a broadcast join, where the smaller DataFrame is sent to each executor only once and joined with the larger DataFrame on each executor. This can significantly reduce the amount of data shuffled across the network and can improve the performance of the join operation. In a broadcast join, the entire smaller DataFrame is sent to each executor, not just a specific column or a cached version on attached storage. This function is particularly useful when one of the DataFrames in a join operation is much smaller than the other, and can fit comfortably in the memory of each executor node.

NEW QUESTION # 141
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 auditing group executes the following query:
SELECT * FROM user_ltv_no_minors
Which statement describes the results returned by this query?

A. All records from all columns will be displayed with the values in user_ltv.
B. All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.
C. All values for the age column will be returned as null values, all other columns will be returned with the values in user_ltv.
D. All age values less than 18 will be returned as null values all other columns will be returned with the values in user_ltv.
E. All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.

Answer: E

Explanation:
Given the CASE statement in the view definition, the result set for a user not in the auditing group would be constrained by the ELSE condition, which filters out records based on age. Therefore, the view will return all columns normally for records with an age greater than 18, as users who are not in the auditing group will not satisfy the is_member('auditing') condition. Records not meeting the age > 18 condition will not be displayed.

NEW QUESTION # 142
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