Databricks Certified-Data-Engineer-Professional valid study dumps : Databricks Certified Data Engineer Professional

An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

• A. Ingest all log information into a bronze table; use merge into to insert, update, or delete the most recent entry for each pk_id into a silver table to recreate the current table state.
• B. Iterate through an ordered set of changes to the table, applying each in turn; rely on Delta Lake's versioning ability to create an audit log.
• C. Create a separate history table for each pk_id resolve the current state of the table by running a union all filtering the history tables for the most recent state.
• D. Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.
• E. Use merge into to insert, update, or delete the most recent entry for each pk_id into a bronze table, then propagate all changes throughout the system.

A data pipeline uses Structured Streaming to ingest data from kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka_generated timesamp, key, and value. Three months after the pipeline is deployed the data engineering team has noticed some latency issued during certain times of the day.
A senior data engineer updates the Delta Table's schema and ingestion logic to include the current timestamp (as recoded by Apache Spark) as well the Kafka topic and partition. The team plans to use the additional metadata fields to diagnose the transient processing delays.
Which limitation will the team face while diagnosing this problem?

• A. New fields cannot be added to a production Delta table.
• B. Spark cannot capture the topic partition fields from the kafka source.
• C. Updating the table schema requires a default value provided for each file added.
• D. New fields will not be computed for historic records.
• E. Updating the table schema will invalidate the Delta transaction log metadata.

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

A Delta Lake table representing metadata about content from user has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE Based on the above schema, which column is a good candidate for partitioning the Delta Table?

• A. latitude
• B. Post_id
• C. User_id
• D. Date
• E. Post_time

A transactions table has been liquid clustered on the columns product_id, user_id, and event_date. Which operation lacks support for cluster on write?

• A. INSERT INTO operations
• B. spark.write.format('delta').mode('append')
• C. CTAS and RTAS statements
• D. spark.writestream.format('delta').mode('append')