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

Section	Weight	Objectives
Data Modeling	~10%	- Apply dimensional modeling techniques - Design scalable Delta Lake schemas and clustering
Cost and Performance Optimization	~13%	- Leverage system tables and observability tools - Optimize queries, clusters, and storage
Streaming Workloads and Change Data Capture	~11%	- Apply AUTO CDC APIs and exactly-once semantics - Implement reliable streaming pipelines
Security and Governance	~10%	- Manage Unity Catalog permissions and ACLs - Implement row-level security, column masking, and compliance
Data Sharing and Federation	~8%	- Configure Delta Sharing and Lakehouse Federation
Monitoring, Logging, and Troubleshooting	~8%	- Diagnose common pipeline and job failures - Use Spark UI, Query Profiler, and system tables
Developing Code for Data Processing using Python and SQL	~22%	- Manage dependencies, libraries, and UDFs - Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader - Implement scalable Python/SQL code and project structures
Data Transformation, Cleansing, and Quality	~12%	- Enforce data quality and quarantine bad data - Apply advanced Spark transformations
CI/CD, Testing, and Deployment	~6%	- Implement testing and deployment pipelines - Deploy with Declarative Automation Bundles, CLI, and REST API

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Databricks Certified Data Engineer Professional Sample Questions (Q129-Q134):

NEW QUESTION # 129
A Delta Lake table representing metadata about content posts from users has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT,
latitude FLOAT, post_time TIMESTAMP, date DATE
This table is partitioned by the date column. A query is run with the following filter:
longitude < 20 & longitude > -20
Which statement describes how data will be filtered?

A. Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.
B. The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.
C. No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.
D. The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.
E. Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.

Answer: E

Explanation:
This is the correct answer because it describes how data will be filtered when a query is run with the following filter: longitude < 20 & longitude > -20. The query is run on a Delta Lake table that has the following schema: user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE. This table is partitioned by the date column.
When a query is run on a partitioned Delta Lake table, Delta Lake uses statistics in the Delta Log to identify data files that might include records in the filtered range. The statistics include information such as min and max values for each column in each data file. By using these statistics, Delta Lake can skip reading data files that do not match the filter condition, which can improve query performance and reduce I/O costs.

NEW QUESTION # 130
The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. The recent_sensor_recordings table contains an identifying sensor_id alongside the timestamp and temperature for the most recent 5 minutes of recordings.
The below query is used to create the alert:

The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger when mean (temperature) > 120. Notifications are triggered to be sent at most every 1 minute.
If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?

A. The source query failed to update properly for three consecutive minutes and then restarted
B. The recent_sensor_recordingstable was unresponsive for three consecutive runs of the query
C. The total average temperature across all sensors exceeded 120 on three consecutive executions of the query
D. The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query
E. The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query

Answer: D

Explanation:
This is the correct answer because the query is using a GROUP BY clause on the sensor_id column, which means it will calculate the mean temperature for each sensor separately. The alert will trigger when the mean temperature for any sensor is greater than 120, which means at least one sensor had an average temperature above 120 for three consecutive minutes. The alert will stop when the mean temperature for all sensors drops below 120.

NEW QUESTION # 131
A data engineer needs to productionize a new Spark application written by teammate. This application has numerous external dependencies, including libraries, and requires custom environment variables and Spark configuration parameters to be set. Which two methods will help the data engineer accomplish the task? (Choose two.)

A. Use compute policies to set system properties, environment variables, and Spark configuration parameters.
B. Add libraries to compute policies
C. Create init scripts on DBFS.
D. Use secrets in init scripts to store configuration data
E. Install libraries on DBFS

Answer: A,C

Explanation:
Compute policies allow centrally defining and enforcing Spark configuration parameters, system properties, and environment variables required by the application, ensuring consistent production settings. Init scripts enable installing external dependencies and performing custom environment setup at cluster startup, making them essential for productionizing Spark applications with complex dependency and configuration requirements.

NEW QUESTION # 132
Which statement describes the default execution mode for Databricks Auto Loader?

A. New files are identified by listing the input directory; new files are incrementally and idempotently loaded into the target Delta Lake table.
B. Webhook trigger Databricks job to run anytime new data arrives in a source directory; new data automatically merged into target tables using rules inferred from the data.
C. New files are identified by listing the input directory; the target table is materialized by directory querying all valid files in the source directory.
D. Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; new files are incrementally and impotently into the target Delta Lake table.
E. Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; the target table is materialized by directly querying all valid files in the source directory.

Answer: A

Explanation:
Databricks Auto Loader simplifies and automates the process of loading data into Delta Lake.
The default execution mode of the Auto Loader identifies new files by listing the input directory. It incrementally and idempotently loads these new files into the target Delta Lake table. This approach ensures that files are not missed and are processed exactly once, avoiding data duplication. The other options describe different mechanisms or integrations that are not part of the default behavior of the Auto Loader.

NEW QUESTION # 133
An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For analytical purposes, only the most recent value for each record needs to be recorded in the target Delta Lake table in the Lakehouse. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

A. Deduplicate records in each batch by pk_id and overwrite the target table.
B. Iterate through an ordered set of changes to the table, applying each in turn to create the current state of the table, (insert, update, delete), timestamp of change, and the values.
C. Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.
D. Use MERGE INTO to insert, update, or delete the most recent entry for each pk_id into a table, then propagate all changes throughout the system.

Answer: C

NEW QUESTION # 134
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