Databricks Databricks-Certified-Professional-Data-Engineer Exam With Confidence Using Practice Dumps

Databricks documentation specifies that Unity Catalog managed tables are the preferred choice for secure, low-maintenance Delta Lake architectures. Managed tables provide full lifecycle management, including metadata, file storage, and access control integration with Unity Catalog. Fine-grained permissions can be enforced at the column and row level through built-in Unity Catalog governance.

Additionally, Predictive Optimization (Auto Optimize + Auto Compaction) automatically manages file sizes, metadata pruning, and layout optimization, eliminating the need for manual maintenance such as scheduling OPTIMIZE or VACUUM.

External tables (A) require manual path management, and Hive Metastore tables (D) do not support Unity Catalog access policies. Therefore, creating a managed Unity Catalog table with predictive optimization provides both the security and performance benefits needed, making B the correct solution.

In Databricks Asset Bundles, the databricks.yml file defines all top-level configuration keys, including bundle, artifacts, workspace, run_as, and targets. The targets section defines specific deployment contexts (for example, dev, test, prod). Setting default: true for a target marks it as the default environment. Overrides for workspace paths and artifact configurations can be defined inside each target while keeping defaults at the top level.

Reference Source: Databricks Asset Bundle Configuration Guide – “Structure of databricks.yml and target overrides.”

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QUESTION NO: 31

A data engineer inherits a Delta table with historical partitions by country that are badly skewed. Queries often filter by high-cardinality customer_id and vary across dimensions over time. The engineer wants a strategy that avoids a disruptive full rewrite, reduces sensitivity to skewed partitions, and sustains strong query performance as access patterns evolve.

Which two actions should the data engineer take? (Choose 2)

A. Keep existing partitions and rely on bin-packing OPTIMIZE only; ZORDER and clustering are unnecessary for multi-dimensional filters.

B. Periodically run OPTIMIZE table_name.

C. Disable data skipping statistics to avoid maintenance overhead; rely on adaptive query execution instead.

D. Depend solely on optimized writes; Databricks will automatically replace partitioning with clustering over time.

E. Switch from static partitioning to liquid clustering and select initial clustering keys that reflect common filters such as customer_id.

Answer: B, E

Liquid Clustering replaces traditional partitioning and ZORDER optimization by automatically organizing data according to clustering keys. It supports evolving clustering strategies without requiring a full table rewrite. To maintain cluster balance and improve performance, the OPTIMIZE command should be run periodically. OPTIMIZE groups data files by clustering keys and helps reduce small file overhead.

Reference Source: Databricks Delta Lake Guide – “Use Liquid Clustering for Tables” and “OPTIMIZE Command for File Compaction and Data Layout.”

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QUESTION NO: 39

A data engineer needs to provide access to a group named manufacturing-team. The team needs privileges to create tables in the quality schema.

Which set of SQL commands will grant a group named manufacturing-team to create tables in a schema named production with the parent catalog named manufacturing with the least privileges?

A. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE CATALOG ON CATALOG manufacturing TO manufacturing-team;

B. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;

C. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;

D. GRANT USE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;

Answer: C

To create a table within a schema, a principal must have CREATE TABLE on the schema, USE SCHEMA on that schema, and USE CATALOG on the parent catalog. This combination ensures the group has just enough privileges to create objects in that schema without excessive permissions like CREATE SCHEMA or CREATE CATALOG.

Reference Source: Databricks Unity Catalog Privilege Model – “Privileges Required to Create a Table.”

The query joins billions of rows (network_events) with millions of rows (ip_ranges) using a range predicate (BETWEEN). Unlike equality joins (=), range joins are not efficiently handled by broadcast or sort-merge joins because:

Broadcast Join (D): Effective for small tables but only for equality joins. Since this query uses a range condition , broadcast will not reduce the complexity of scanning billions of records across non-equality conditions.

Sort-Merge Join (C): Works for ordered joins but is inefficient on range conditions. Sorting billions of records adds excessive overhead and will not resolve the bottleneck.

Increasing Shuffle Partitions (A): Only spreads out shuffle work but does not address the fundamental inefficiency of range-based lookups at scale.

Range Joins in Spark (RANGE_JOIN hint):

Databricks provides range join optimizations specifically for conditions such as BETWEEN. By applying a RANGE_JOIN hint , Spark can build optimized data structures (such as interval indexes or partition pruning strategies) that map billions of input rows to ranges much faster. This avoids brute-force scans and unnecessary shuffle costs.

Thus, Option B is the correct solution because:

It leverages range-join optimization , which is purpose-built for queries joining massive event logs to smaller lookup tables with IP ranges.

This ensures Spark can evaluate billions of rows against millions of ranges with optimized matching logic, drastically improving query performance while preserving correctness.