Databricks Databricks-Certified-Data-Engineer-Professional Valid Test Preparation, Databricks-Certified-Data-Engineer-Professional Testdump

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

Section	Weight	Objectives
Topic 1: Databricks Lakehouse Platform	24%	- Delta Lake - Data Management - Lakehouse Architecture - Unity Catalog
Topic 2: Data Modeling and Storage	20%	- Data Modeling - Storage Optimization - File Formats
Topic 3: Data Quality and Governance	12%	- Governance - Data Lineage - Data Quality
Topic 4: Monitoring and Troubleshooting	16%	- Troubleshooting - Performance Optimization - Monitoring
Topic 5: Data Processing	28%	- Spark SQL - Structured Streaming - ETL Pipelines - Data Transformation

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q245-Q250):

NEW QUESTION # 245
The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property "contains_pii" = true.
The following SQL DDL statement is executed to create a new table:
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Which command allows manual confirmation that these three requirements have been met?

A. SHOW TABLES dev
B. SHOW TBLPROPERTIES dev.pii test
C. DESCRIBE DETAIL dev.pii test
D. DESCRIBE HISTORY dev.pii test
E. DESCRIBE EXTENDED dev.pii test

Answer: E

Explanation:
This is the correct answer because it allows manual confirmation that these three requirements have been met. The requirements are that all tables containing Personal Identifiable Information (PII) must be clearly annotated, which includes adding column comments, table comments, and setting the custom table property "contains_pii" = true. The DESCRIBE EXTENDED command is used to display detailed information about a table, such as its schema, location, properties, and comments. By using this command on the dev.pii_test table, one can verify that the table has been created with the correct column comments, table comment, and custom table property as specified in the SQL DDL statement.

NEW QUESTION # 246
What statement is true regarding the retention of job run history?

A. It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3
B. It is retained for 60 days, after which logs are archived
C. It is retained for 90 days or until the run-id is re-used through custom run configuration
D. It is retained until you export or delete job run logs
E. It is retained for 60 days, during which you can export notebook run results to HTML

Answer: E

Explanation:
https://docs.databricks.com/en/workflows/jobs/monitor-job-runs.html

NEW QUESTION # 247
A platform engineer needs to report the resource consumption, categorized by SKU tier, across all workspaces. The engineer decides to use the system.billing.usage system table to create a query. Which SQL query will accurately return the daily usage by product?

Answer: D

Explanation:
This query correctly aggregates usage at a daily granularity by truncating the usage start timestamp to the day and summing the usage quantity, which represents DBUs. Grouping by both the derived daily value and the SKU name ensures usage is accurately categorized by product tier across all workspaces.

NEW QUESTION # 248
A data engineer is creating a data ingestion pipeline to understand where customers are taking their rented bicycles during use. The engineer noticed that, over time, data being transmitted from the bicycle sensors fail to include key details like latitude and longitude. Downstream analysts need both the clean records and the quarantined records available for separate processing.
The data engineer already has this code:
import dlt
from pyspark.sql.functions import expr
rules = {
"valid_lat": "(lat IS NOT NULL)",
"valid_long": "(long IS NOT NULL)"
}
quarantine_rules = "NOT({})".format(" AND ".join(rules.values()))
@dlt.view
def raw_trips_data():
return spark.readStream.table("ride_and_go.telemetry.trips")
How should the data engineer meet the requirements to capture good and bad data?

A. @dlt.view
@dlt.expect_or_drop("lat_long_present", "(lat IS NOT NULL AND long IS NOT NULL)") def trips_data_quarantine():
return spark.readStream.table("ride_and_go.telemetry.trips")
B. @dlt.table(partition_cols=["is_quarantined", ])
@dlt.expect_all(rules)
def trips_data_quarantine():
return (
spark.readStream.table("raw_trips_data")
.withColumn("is_quarantined", expr(quarantine_rules))
)
C. @dlt.table(name="trips_data_quarantine")
def trips_data_quarantine():
return (
spark.readStream.table("raw_trips_data")
.filter(expr(quarantine_rules))
)
D. @dlt.table
@dlt.expect_all_or_drop(rules)
def trips_data_quarantine():
return spark.readStream.table("raw_trips_data")

Answer: C

Explanation:
The requirement is that both valid (good) and invalid (bad) records must be captured and available separately for downstream processing. Invalid records should not simply be dropped; they must be quarantined in a dedicated table.
In Databricks Lakeflow Declarative Pipelines (DLT), this is achieved by creating separate output tables:
One table for valid records (Silver table) that pass the expectations.
Another quarantine table that explicitly captures records failing the expectations.
Option A correctly implements this by:
Declaring a DLT table trips_data_quarantine.
Using .filter(expr(quarantine_rules)) to isolate invalid records (records where latitude or longitude is NULL).
This ensures analysts can query both good records (from the main Silver pipeline table) and bad records (from the quarantine table).

NEW QUESTION # 249
A healthcare analytics team is implementing a dimensional model in Delta Lake for patient care analysis. They have a date dimension table and are evaluating design options to ensure it supports a wide range of time-based analyses. Which design approach for the date dimension will support efficient time-based querying and aggregation?

A. Pre-calculate attributes like fiscal_period, quarter, month_name, day_of_week, and holiday.
B. Create separate dimension tables for different calendar systems (fiscal, academic, etc.)
C. Store the date as string in ISO format (YYYY-MM-DD) for readability.
D. Store only the date value and calculate all time attributes in queries.

Answer: A

Explanation:
Pre-calculating commonly used time attributes in the date dimension enables efficient filtering, grouping, and aggregation without requiring repeated computations at query time. This design supports a wide range of time-based analyses and improves query performance and usability in dimensional models.

NEW QUESTION # 250
......

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