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

Section	Weight	Objectives
Topic 1: CI/CD, Testing, and Deployment	~6%	- Implement testing and deployment pipelines - Deploy with Declarative Automation Bundles, CLI, and REST API
Topic 2: Developing Code for Data Processing using Python and SQL	~22%	- Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader - Implement scalable Python/SQL code and project structures - Manage dependencies, libraries, and UDFs
Topic 3: Data Modeling	~10%	- Design scalable Delta Lake schemas and clustering - Apply dimensional modeling techniques
Topic 4: Streaming Workloads and Change Data Capture	~11%	- Implement reliable streaming pipelines - Apply AUTO CDC APIs and exactly-once semantics
Topic 5: Data Transformation, Cleansing, and Quality	~12%	- Apply advanced Spark transformations - Enforce data quality and quarantine bad data
Topic 6: Cost and Performance Optimization	~13%	- Leverage system tables and observability tools - Optimize queries, clusters, and storage
Topic 7: Data Sharing and Federation	~8%	- Configure Delta Sharing and Lakehouse Federation
Topic 8: Security and Governance	~10%	- Manage Unity Catalog permissions and ACLs - Implement row-level security, column masking, and compliance
Topic 9: Monitoring, Logging, and Troubleshooting	~8%	- Diagnose common pipeline and job failures - Use Spark UI, Query Profiler, and system tables

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Databricks Certified Data Engineer Professional Sample Questions (Q173-Q178):

NEW QUESTION # 173
Each configuration below is identical to the extent that each cluster has 400 GB total of RAM 160 total cores and only one Executor per VM.
Given an extremely long-running job for which completion must be guaranteed, which cluster configuration will be able to guarantee completion of the job in light of one or more VM failures?

A. - Total VMs: 16
- 25 GB per Executor
- 10 Cores / Executor
B. - Total VMs: 4
- 100 GB per Executor
- 40 Cores / Executor
C. - Total VMs: 1
- 400 GB per Executor
- 160 Cores/Executor
D. - Total VMs: 8
- 50 GB per Executor
- 20 Cores / Executor
E. - Total VMs: 2
- 200 GB per Executor
- 80 Cores / Executor

Answer: A

NEW QUESTION # 174
A data engineering workspace was automatically enabled for Unity Catalog, creating a workspace catalog. New team members report they can create tables in the default schema but cannot access table in other schemas within the same workspace catalog. Why are the new team members unable to access tables in other schemas?

A. Workspace catalog permissions are not subject to inheritance rules.
B. Workspace users receive USE CATALOG and specific privileges on default schema only.
C. New users only receive CREATE TABLE privileges on the default schema.
D. Tables in other schemas require additional BROWSEprivileges that new users don't receive automatically

Answer: B

Explanation:
When a workspace catalog is automatically created, new users are granted USE CATALOG and limited privileges on the default schema only. Access to other schemas requires explicit grants, so users cannot see or query tables in those schemas without additional permissions.

NEW QUESTION # 175
The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?

A. When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.
B. Whenever a table is being created, make sure that the location keyword is used.
C. Whenever a database is being created, make sure that the location keyword is used
D. When tables are created, make sure that the external keyword is used in the create table statement.
E. When the workspace is being configured, make sure that external cloud object storage has been mounted.

Answer: B

Explanation:
This is the correct answer because it ensures that this requirement is met. The requirement is that all tables in the Lakehouse should be configured as external Delta Lake tables. An external table 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 by using the location keyword to specify the path to an existing directory in a cloud storage system, such as DBFS or S3. By creating external tables, the data engineering team can avoid losing data if they drop or overwrite the table, as well as leverage existing data without moving or copying it.

NEW QUESTION # 176
A data engineer is building a streaming data pipeline to ingest JSON files from cloud storage into a Delta Lake table. The pipeline must process files incrementally, handle schema evolution automatically, ensure exactly-once processing, and minimize manual infrastructure management.
How should the data engineer fulfill these requirements?

A. Use Lakeflow Spart Declarative Pipelines with Auto Loader and enabling schema inference with
"cloudFiles.schemaEvolutionMode"= "addNewColumns"
B. Use Lakeflow Spark Declarative Pipelines with a static DataFrame read, merge schema with spark.conf.set ("spark.databricks.delta.schema.autoMerge.enabled", "true")
C. Use traditional Spark Structured Streaming with Auto Loader, manually configuring checkpoints location and enabling schema inference with "mergeSchema"= "true"
D. Use Auto Loader in batch mode with a daily job to overwrite the Delta table.

Answer: A

Explanation:
Lakeflow Spark Declarative Pipelines combined with Auto Loader provide fully managed incremental file ingestion with exactly-once guarantees and minimal operational overhead.
Enabling schema inference and evolution allows new columns in incoming JSON files to be incorporated automatically, satisfying the requirements for streaming ingestion, schema evolution, and reduced manual infrastructure management.

NEW QUESTION # 177
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 average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query
B. The total average temperature across all sensors exceeded 120 on three consecutive executions of the query
C. The recent_sensor_recordingstable was unresponsive for three consecutive runs of the query
D. The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query
E. The source query failed to update properly for three consecutive minutes and then restarted

Answer: A

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 # 178
......

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