Databricks Certified Data Engineer Professional : Databricks-Certified-Data-Engineer-Professional Exam Questions

A Delta Lake table representing metadata about content posts from users has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT,
latitude FLOAT, post_time TIMESTAMP, date DATE
This table is partitioned by the date column. A query is run with the following filter:
longitude < 20 & longitude > -20
Which statement describes how data will be filtered?

• A. No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.
• B. The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.
• C. Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.
• D. The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.
• E. Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.

A data engineering team uses Databricks Lakehouse Monitoring to track the percent_null metric for a critical column in their Delta table.
The profile metrics table (prod_catalog.prod_schema.customer_data_profile_metrics) stores hourly percent_null values.
The team wants to:
Trigger an alert when the daily average of percent_null exceeds 5% for
three consecutive days.
Ensure that notifications are not spammed during sustained issues.

• A. SELECT SUM(CASE WHEN percent_null > 5 THEN 1 ELSE 0 END) AS violation_days FROM prod_catalog.prod_schema.customer_data_profile_metrics WHERE window.end >= CURRENT_TIMESTAMP - INTERVAL '3' DAY Alert Condition: violation_days >= 3 Notification Frequency: Just once
• B. WITH daily_avg AS (
  SELECT DATE_TRUNC('DAY', window.end) AS day,
  AVG(percent_null) AS avg_null
  FROM prod_catalog.prod_schema.customer_data_profile_metrics
  GROUP BY DATE_TRUNC('DAY', window.end)
  )
  SELECT day, avg_null
  FROM daily_avg
  ORDER BY day DESC
  LIMIT 3
  Alert Condition: ALL avg_null > 5 for the latest 3 rows
  Notification Frequency: Just once
• C. SELECT percent_null
  FROM prod_catalog.prod_schema.customer_data_profile_metrics
  WHERE window.end >= CURRENT_TIMESTAMP - INTERVAL '1' DAY
  Alert Condition: percent_null > 5
  Notification Frequency: At most every 24 hours
• D. SELECT AVG(percent_null) AS daily_avg
  FROM prod_catalog.prod_schema.customer_data_profile_metrics
  WHERE window.end >= CURRENT_TIMESTAMP - INTERVAL '3' DAY
  Alert Condition: daily_avg > 5
  Notification Frequency: Each time alert is evaluated

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

A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?

• A. Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.
• B. Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.
• C. Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.
• D. Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.

A data architect is designing a Databricks solution to efficiently process data for different business requirements. In which scenario should a data engineer use a materialized view compared to a streaming table?

• A. Processing high-volume, continuous clickstream data from a website to monitor user behavior in real-time.
• B. Implementing a CDC (Change Data Capture) pipeline that needs to detect and respond to database changes within seconds.
• C. Ingesting data from Apache Kafka topics with sub-second processing requirements for immediate alerting.
• D. Precomputing complex aggregations and joins from multiple large tables to accelerate BI dashboard performance.