Pay attention on Pass-king Certified-Data-Engineer-Professional Practice Materials, clear exam 100% for sure

The data engineer is using Spark's MEMORY_ONLY storage level. Which indicators should the data engineer look for in the spark UI's Storage tab to signal that a cached table is not performing optimally?

• A. The RDD Block Name included the '' annotation signaling failure to cache
• B. The number of Cached Partitions> the number of Spark Partitions
• C. Size on Disk is < Size in Memory
• D. Size on Disk is> 0
• E. On Heap Memory Usage is within 75% of off Heap Memory usage

The data engineering team is migrating an enterprise system with thousands of tables and views into the Lakehouse. They plan to implement the target architecture using a series of bronze, silver, and gold tables. Bronze tables will almost exclusively be used by production data engineering workloads, while silver tables will be used to support both data engineering and machine learning workloads. Gold tables will largely serve business intelligence and reporting purposes. While personal identifying information (PII) exists in all tiers of data, pseudonymization and anonymization rules are in place for all data at the silver and gold levels.
The organization is interested in reducing security concerns while maximizing the ability to collaborate across diverse teams.
Which statement exemplifies best practices for implementing this system?

• A. Because all tables must live in the same storage containers used for the database they're created in, organizations should be prepared to create between dozens and thousands of databases depending on their data isolation requirements.
• B. Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.
• C. Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root.
• D. Storinq all production tables in a single database provides a unified view of all data assets available throughout the Lakehouse, simplifying discoverability by granting all users view privileges on this database.
• E. Isolating tables in separate databases based on data quality tiers allows for easy permissions management through database ACLs and allows physical separation of default storage locations for managed tables.

A data engineer is optimizing a managed Delta table that suffers from data skew and frequently changing query filter columns. The engineer wants to avoid costly data rewrites when query patterns evolve. The table size is under 1 TB. How should the data engineer meet this requirement?

• A. Combine partitioning and Z-ordering to maximize flexibility and minimize maintenance as query patterns change.
• B. Use Hive-style partitioning, as it provides efficient data skipping and is easy to change partition columns at any time.
• C. Apply Z-ordering, since it allows flexible reorganization of data layout without rewriting existing files and adapts easily to new filter columns.
• D. Enable liquid clustering, as it efficiently handles data skew, allows clustering keys to be changed without rewriting existing data, and adapts to evolving query patterns.

Which statement describes Delta Lake Auto Compaction?

• A. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.
• B. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.
• C. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.
• D. Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
• E. Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.

A data engineer is troubleshooting a slow-running Delta Lake query on Databricks SQL involves complex joins and large datasets. They need to identify whether the root cause is related to poor data skipping, inefficient join strategies, or excessive data shuffling. Which approach should identify the specific bottlenecks using native Databricks tools?

• A. Check the query's execution time in the Jobs UI and correlate it with cluster resource utilization metrics.
• B. Analyze the Top Operators panel in the Query Profile to identify high-cost operations like BroadcastNestedLoopJoin
• C. Enable the EXPLAIN command to review the parsed logical plan and manually estimate shuffle sizes.
• D. Use the LIMIT clause to run a subset of the query and compare execution times with the full dataset.