Prepare with Databricks Certified-Data-Engineer-Professional exam torrent, pass for sure

When a new Databricks project starts, the central IP team provisions the required infrastructure using Terraform and a Service Principal. This includes creating a Databricks workspace, a Unity Catalog linked to an External Location, and a Databricks group containing all project team members. Project teams must store all assets - e.g., tables and volumes, as Managed assets in Unity Catalog. This model hides infrastructure complexity while giving teams autonomy within their catalog. They can create and manage schemas, tables, volumes, and related objects but cannot rename, delete, or change catalog permissions, those remain under IT's control. Which rights should the project group be granted to enable this model?

• A. The group needs to have ALL PRIVILEGES and the MANAGE on the catalog.
• B. The group needs to have ALL PRIVILEGES on the catalog.
• C. The group should be made OWNER of the catalog.
• D. The group needs to have USE CATALOG and USE SCHEMA on the catalog.

A data team is working to optimize an existing large, fast-growing table 'orders' with high cardinality columns, which experiences significant data skew and requires frequent concurrent writes. The team notice that the columns 'user_id', 'event_timestamp' and 'product_id' are heavily used in analytical queries and filters, although those keys may be subject to change in the future due to different business requirements. Which partitioning strategy should the team choose to optimize the table for immediate data skipping, incremental management over time, and flexibility?

• A. Cluster the table with: ALTER TABLE orders CLUSTER BY user_id, product_id, event_timestamp
• B. Use z-order after partitiing the table: OPTIMIZE orders ZORDER BY (user_id, product_id) WHERE event_timestamp = current date () - 1 DAY
• C. Partition the table with: ALTER TABLE orders PARTITION BY user_id, product_id, event_timestamp
• D. Z-order the table with OPTIMIZE orders ZORDER BY (user_id, product_id, event_timestamp)

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. 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
• B. 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
• 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 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

A data engineer is developing a Lakeflow Declarative Pipeline (LDP) using a Databricks notebook directly connected to their pipeline. After adding new table definitions and transformation logic in their notebook, they want to check for any syntax errors in the pipeline code without actually processing data or running the pipeline. How should the data engineer perform this syntax check?

• A. Disconnect the notebook from the pipeline and reconnect it to a compute cluster to access code validation features.
• B. Use the "Validate" option in the notebook to check for syntax errors.
• C. Switch to a workspace file instead of a notebook to access validation and diagnostics tools.
• D. Open the web terminal from the notebook and run a shell command to validate the pipeline code.

A data engineer manages a production Lakeflow Declarative Pipeline that processes customer transaction data. The pipeline includes several data quality expectations such as transaction_amount > 0 and customer_id IS NOT NULL. These expectations are defined using the EXPECT clause in SQL.
The engineer aims to monitor the pipeline's data quality by analyzing the number of records that passed or failed each expectation during the latest pipeline update. The Lakeflow Declarative Pipelines event logs are stored in a Delta table named event_log_table.
For the most recent pipeline update, determine a programmatically appropriate approach to extract information like the name of each expectation, associated dataset, count of records that passed the expectation, and count of records that failed the expectation.
Which method retrieves the desired data quality metrics from the Lakeflow Declarative Pipelines event log?

• A. Use the Lakeflow Declarative Pipelines UI to navigate to the specific pipeline, select the dataset, and view the Data Quality tab to manually retrieve the expectation metrics.
• B. Access the event_log_table, filter for events where event_type = 'flow_progress', and parse details.flow_progress.data_quality.expectations field to extract the required metrics.
• C. Access the event_log_table, filter for events where event_type = 'expectation_result', and extract the expectation metrics from the details field.
• D. Query the event_log_table for events with event_type = 'data_quality' and directly select the passed_records and failed_records fields.