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Databricks-Certified-Professional-Data-Engineer測試引擎, Databricks-Certified-Professional-Data-Engineer認證考試, Databricks-Certified-Professional-Data-Engineer參考資料, Databricks-Certified-Professional-Data-Engineer熱門考古題, Databricks-Certified-Professional-Data-Engineer證照資訊

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

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

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最新的 Databricks Certification Databricks-Certified-Professional-Data-Engineer 免費考試真題 (Q211-Q216):

問題 #211
Which of the following statements can successfully read the notebook widget and pass the python variable to a SQL statement in a Python notebook cell?

  • A. 1.order_date = dbutils.widgets.get("widget_order_date")
    2.
    3.spark.sql(f"SELECT * FROM sales WHERE orderDate = '{order_date}' ")
    (Correct)
  • B. 1.order_date = dbutils.widgets.get("widget_order_date")
    2.
    3.spark.sql(f"SELECT * FROM sales WHERE orderDate = 'order_date' ")
  • C. 1.order_date = dbutils.widgets.get("widget_order_date")
    2.
    3.spark.sql(f"SELECT * FROM sales WHERE orderDate = 'f{order_date }'")
  • D. 1.order_date = dbutils.widgets.get("widget_order_date")
    2.
    3.spark.sql("SELECT * FROM sales WHERE orderDate = order_date")
  • E. 1.order_date = dbutils.widgets.get("widget_order_date")
    2.
    3.spark.sql(f"SELECT * FROM sales WHERE orderDate = '${order_date }' ")

答案：A


問題 #212
A data engineer has created a new cluster using shared access mode with default configurations. The data engineer needs to allow the development team access to view the driver logs if needed.
What are the minimal cluster permissions that allow the development team to accomplish this?

  • A. CAN MANAGE
  • B. CAN ATTACH TO
  • C. CAN VIEW
  • D. CAN RESTART

答案：C

解題說明：
Databricks provides different permission levels to control access to clusters. The correct minimal permission required for viewing driver logs is CAN VIEW.
Databricks Cluster Permission Levels:
CAN ATTACH TO:
Allows users to attach notebooks to a cluster but does not allow them to view logs.
Not sufficient for viewing driver logs.
CAN MANAGE:
Grants full control over the cluster, including starting, stopping, and editing configurations.
Too broad for this requirement.
CAN VIEW (Correct Answer):
Allows users to view cluster details, logs, and status but not modify any configurations.
Minimal required permission for viewing logs.
CAN RESTART:
Grants permission to restart the cluster, but does not include log access.
Not sufficient for viewing logs.
Conclusion:
The minimal permission needed to allow the development team to view driver logs is CAN VIEW.
Reference:
Databricks Cluster Permissions Documentation


問題 #213
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 namedpredswith 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.
  • B.
  • C.
  • D. preds.write.mode("append").saveAsTable("churn_preds")
  • E. preds.write.format("delta").save("/preds/churn_preds")

答案：D


問題 #214
You are currently working on reloading customer_sales tables using the below query
1. INSERT OVERWRITE customer_sales
2. SELECT * FROM customers c
3. INNER JOIN sales_monthly s on s.customer_id = c.customer_id
After you ran the above command, the Marketing team quickly wanted to review the old data that was in the table. How does INSERT OVERWRITE impact the data in the customer_sales table if you want to see the previous version of the data prior to running the above statement?

  • A. Appends the data to the current version, you can time travel to previous versions
  • B. Overwrites the current version of the data but clears all historical versions of the data, so you can not time travel to previous versions.
  • C. Overwrites the data in the table, all historical versions of the data, you can not time travel to previous versions
  • D. By default, overwrites the data and schema, you cannot perform time travel
  • E. Overwrites the data in the table but preserves all historical versions of the data, you can time travel to previous versions

答案：E

解題說明：
Explanation
The answer is, INSERT OVERWRITE Overwrites the current version of the data but preserves all historical versions of the data, you can time travel to previous versions.
1.INSERT OVERWRITE customer_sales
2.SELECT * FROM customers c
3.INNER JOIN sales s on s.customer_id = c.customer_id
Let's just assume that this is the second time you are running the above statement, you can still query the prior version of the data using time travel, and any DML/DDL except DROP TABLE creates new PARQUET files so you can still access the previous versions of data.
SQL Syntax for Time travel
SELECT * FROM table_name as of [version number]
with customer_sales example
SELECT * FROM customer_sales as of 1 -- previous version
SELECT * FROM customer_sales as of 2 -- current version
You see all historical changes on the table using DESCRIBE HISTORY table_name Note: the main difference between INSERT OVERWRITE and CREATE OR REPLACE TABLE(CRAS) is that CRAS can modify the schema of the table, i.e it can add new columns or change data types of existing columns. By default INSERT OVERWRITE only overwrites the data.
INSERT OVERWRITE can also be used to update the schema when
spark.databricks.delta.schema.autoMerge.enabled is set true if this option is not enabled and if there is a schema mismatch command INSERT OVERWRITEwill fail.
Any DML/DDL operation(except DROP TABLE) on the Delta table preserves the historical ver-sion of the data.


問題 #215
A Delta Lake table was created with the below query:

Consider the following query:
DROP TABLE prod.sales_by_store -
If this statement is executed by a workspace admin, which result will occur?

  • A. The table will be removed from the catalog but the data will remain in storage.
  • B. The table will be removed from the catalog and the data will be deleted.
  • C. Data will be marked as deleted but still recoverable with Time Travel.
  • D. Nothing will occur until a COMMIT command is executed.
  • E. An error will occur because Delta Lake prevents the deletion of production data.

答案：B

解題說明：
When a table is dropped in Delta Lake, the table is removed from the catalog and the data is deleted. This is because Delta Lake is a transactional storage layer that provides ACID guarantees. When a table is dropped, the transaction log is updated to reflect the deletion of the table and the data is deleted from the underlying storage. References:
* https://docs.databricks.com/delta/quick-start.html#drop-a-table
* https://docs.databricks.com/delta/delta-batch.html#drop-table


問題 #216
......

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