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

Section	Weight	Objectives
Topic 1: Data Modelling	6%	- Schema design and management - Delta Lake table design - Medallion Architecture implementation
Topic 2: Data Governance	7%	- Data lineage and metadata tracking - Unity Catalog management - Policy enforcement
Topic 3: Monitoring and Alerting	10%	- Pipeline observability and logging - Performance and health monitoring - Setting up alerts and notifications
Topic 4: Data Sharing and Federation	5%	- Cross-workspace and cross-cloud access - Unity Catalog data sharing
Topic 5: Debugging and Deploying	10%	- Troubleshooting pipelines and errors - Deployment using bundles, CLI, and APIs - CI/CD and DevOps practices
Topic 6: Data Transformation, Cleansing, and Quality	10%	- Handling missing or inconsistent data - Standardization and normalization - Data validation and quality checks
Topic 7: Cost & Performance Optimisation	13%	- Storage optimization (partitioning, Z-order, indexing) - Query optimization and caching - Cluster configuration and scaling
Topic 8: Ensuring Data Security and Compliance	10%	- Data encryption and masking - Compliance standards implementation - Access control and permissions
Topic 9: Data Ingestion & Acquisition	7%	- Auto Loader and streaming ingestion - Connecting to diverse data sources - Schema inference and evolution
Topic 10: Developing Code for Data Processing using Python and SQL	22%	- Data transformation and aggregation - Integration with Databricks APIs and tools - Batch and incremental processing logic

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q43-Q48):

NEW QUESTION # 43
Why does AUTO LOADER require schema location?

A. Schema location is used to identify the schema of target table
B. Schema location is used to store schema inferred by AUTO LOADER
C. Schema location is used to store user provided schema
D. Schema location is used to identify the schema of target table and source table
E. AUTO LOADER does not require schema location, because its supports Schema evolution

Answer: B

Explanation:
Explanation
The answer is, Schema location is used to store schema inferred by AUTO LOADER, so the next time AUTO LOADER runs faster as does not need to infer the schema every single time by trying to use the last known schema.
Auto Loader samples the first 50 GB or 1000 files that it discovers, whichever limit is crossed first. To avoid incurring this inference cost at every stream start up, and to be able to provide a stable schema across stream restarts, you must set the option cloudFiles.schemaLocation. Auto Loader creates a hidden directory _schemas at this location to track schema changes to the input data over time.
The below link contains detailed documentation on different options
Auto Loader options | Databricks on AWS

NEW QUESTION # 44
What steps need to be taken to set up a DELTA LIVE PIPELINE as a job using the workspace UI?

A. Select Workflows UI and Delta live tables tab, under task type select Delta live tables pipeline and select the pipeline JSON file
B. Select Workflows UI and Delta live tables tab, under task type select Delta live tables pipeline and select the notebook
C. Use Pipeline creation UI, select a new pipeline and job cluster
D. DELTA LIVE TABLES do not support job cluster

Answer: B

Explanation:
Explanation
The answer is,
Select Workflows UI and Delta live tables tab, under task type select Delta live tables pipeline and select the notebook.
Create a pipeline
To create a new pipeline using the Delta Live Tables notebook:
1.Click Workflows in the sidebar, click the Delta Live Tables tab, and click Create Pipeline.
2.Give the pipeline a name and click to select a notebook.
3.Optionally enter a storage location for output data from the pipeline. The system uses a de-fault location if you leave Storage Location empty.
4.Select Triggered for Pipeline Mode.
5.Click Create.
The system displays the Pipeline Details page after you click Create. You can also access your pipeline by clicking the pipeline name in the Delta Live Tables tab.

NEW QUESTION # 45
The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame named preds with the schema "customer_id LONG, predictions DOUBLE, date DATE".

The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?

A. preds.write.format("delta").save("/preds/churn_preds")
B. preds.write.mode("append").saveAsTable("churn_preds")
C.
D.
E.

Answer: B

NEW QUESTION # 46
Delete records from the transactions Delta table where transactionDate is greater than current timestamp?

A. DELETE FROM transactions where transactionDate > current_timestamp() KEEP_HISTORY
B. DELET FROM transactions where transactionDate GE current_timestamp()
C. DELETE FROM transactions FORMAT DELTA where transactionDate > cur-renct_timestmap()
D. DELETE FROM transactions where transactionDate > current_timestamp()
E. DELETE FROM transactions if transctionDate > current_timestamp()

Answer: D

NEW QUESTION # 47
Which of the following SQL commands are used to append rows to an existing delta table?

A. COPY DELTA INTO table_name
B. UPDATE table_name
C. APPEND INTO table_name
D. APPEND INTO DELTA table_name
E. INSERT INTO table_name

Answer: E

Explanation:
Explanation
The answer is INSERT INTO table_name
Insert adds rows to an existing table, this is very similar to add rows a traditional Database or Da-tawarehouse.

NEW QUESTION # 48
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