An external object storage container has been mounted to the location /mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:

After the database was successfully created and permissions configured, a member of the finance team runs the following code:

If all users on the finance team are members of the finance group, which statement describes how the tx_sales table will be created?

• A. A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.
• B. An external table will be created in the storage container mounted to /mnt/finance eda bucket.
• C. An managed table will be created in the storage container mounted to /mnt/finance_eda_bucket.
• D. A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.
• E. A managed table will be created in the DBFS root storage container.

A company wants to implement Lakehouse Federation across multiple data sources but is concerned about data consistency and ensuring that all teams access the same authoritative version of their data. Which statement is applicable for Lakehouse Federations to maintain data consistency?

• A. Federation provides read-only access that reflects the current state of source systems.
• B. Federation implements change data capture (CDC) from all sources.
• C. A separate data synchronization service must be deployed.
• D. Federation creates local copies that must be manually refreshed.

A data engineering team needs to create a SQL Alert that monitors data quality across multiple columns in their customer table. They want to trigger an alert when both the percentage of customers with missing email addresses exceeds 15% AND the percentage of customers with invalid phone number formats exceeds 10%. Which SQL query pattern is appropriate for implementing this multi-column alert condition?

• A. SELECT email_null_pct, phone_invalid_pct FROM (SELECT (COUNT(CASE WHEN email IS NULL THEN 1 END) *
  100.0/COUNT (*)) as email_null_pct, (COUNT(CASE WHEN phone NOT RLIKE 'ˆ[0-9-+()\\s]+$' THEN 1 END)*
  100.0/COUNT (*)) as phone_invalid_pct FROM customers)
• B. SELECT email, phone FROM customers WHERE email IS NULL AND phone NOT RLIKE 'ˆ[0-9-
  +()\\s]+$'
• C. SELECT CASE WHEN email_null_pct >15 AND phone_invalid_pct> 10 THEN 1 ELSE 0 END FROM (SELECT (COUNT (CASE WHEN email IS NULL THEN 1 END) * 100.0 / COUNT (*)) as phone_invalid_pct FROM customers) metrics
• D. SELECT COUNT (*) FROM customers WHERE email IS NULL OR phone_format_invalid = true

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 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
• 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 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
• D. 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

A data engineering team is implementing an append-only data pipeline using Delta Lake, and wants to ensure that data is never modified or deleted once written. Which Delta Lake feature should the data engineer enable to prevent modifications to existing data?

• A. Delta APPEND_ONLY
• B. Delta VACUUM
• C. Delta Time Travel
• D. Delta OPTIMIZE