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

Databricks Certified Data Engineer Professional Sample Questions:

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

D)

E)

2. The data engineer is using Spark's MEMORY_ONLY storage level. Which indicators should the data engineer look for in the spark UI's Storage tab to signal that a cached table is not performing optimally?

A) Size on Disk is> 0
B) The RDD Block Name included the '' annotation signaling failure to cache
C) On Heap Memory Usage is within 75% of off Heap Memory usage
D) The number of Cached Partitions> the number of Spark Partitions
E) Size on Disk is < Size in Memory

3. A data engineer is using Lakeflow Declarative Pipelines Expectations feature to track the data quality of their incoming sensor data. Periodically, sensors send bad readings that are out of range, and they are currently flagging those rows with a warning and writing them to the silver table along with the good data. They've been given a new requirement ?the bad rows need to be quarantined in a separate quarantine table and no longer included in the silver table.
This is the existing code for their silver table:
@dlt.table
@dlt.expect("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
What code will satisfy the requirements?

A) @dlt.table
@dlt.expect("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
B) @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading < 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
C) @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect_or_drop("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
D) @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")

4. A data engineer is implementing liquid clustering on a Delta Lale table and needs to understand how it affects data management operations. The table will be updated frequently with new data.
The table is an external table and not managed by Unity Catalog. How does liquid clustering in Delta Lake handle new data that is inserted after the initial table creation?

A) New data remains unclustered until the next OPTIMIZE operation.
B) New data is written to a staging area and clustered during scheduled maintenance.
C) New data is automatically clustered during write operations.
D) New data is rejected if it doesn't match the clustering pattern.

5. A data company uses Databricks Unity Catalog and has multiple enterprise data sources, including PostgreSQL, Snowflake, and SQL Server. The central data platform team wants to configure Lakehouse Federation so analysts can query external tables directly in Databricks using Databricks SQL, without duplicating data. Which steps are necessary to configure Lakehouse Federation in a secure and governed manner?

A) Create external locations and storage credentials to connect to each database, then register foreign tables in Unity Catalog.
B) Mirror the external datasets into Delta Lake using Auto Loader, and govern them using Data Lineage and System Tables.
C) Use Partner Connect to create linked datasets, and apply table ACLs at the source system to govern access through Databricks.
D) Configure connections and foreign catalog in Unity Catalog, then grant access to foreign catalogs, schemas, and tables using Unity Catalog permissions.

Solutions: