Databricks-DE-Assoc Multiple Select — 10 Questions

Question 1

A data engineer is designing a secure architecture using the Databricks Intelligence Platform. Which TWO of the following statements accurately describe the role and capabilities of Unity Catalog within this platform? (Choose TWO)

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Unity Catalog provides centralized access control for tables, views, volumes, and AI models across multiple workspaces.. Unity Catalog is the fine-grained governance solution for data and AI across the Databricks platform, centralizing access control for tables, volumes, and models. It operates across multiple workspaces in a metastore, ensuring consistent security policies and audit logging regardless of which workspace or compute resource executes the user queries.

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Unity Catalog replaces the underlying cloud provider IAM roles for all compute resources executing queries.

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Unity Catalog automatically handles the physical partitioning of Delta tables for optimal query performance.

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Unity Catalog requires all data to be migrated into proprietary binary formats before governance policies can be applied.

Question 2

When configuring a Databricks Job, which TWO factors most directly influence the choice between a 'Job Cluster' and an 'All-Purpose Cluster'?

Accepted Answer

The need for interactive debugging and notebook testing.. Choosing the right cluster type is vital for balancing cost and performance. Job Clusters are ephemeral, optimized for production tasks, and significantly cheaper, whereas All-Purpose Clusters are persistent and intended for interactive development. Understanding the cost implications and lifecycle management of these clusters is fundamental for any Databricks engineer responsible for managing operational budgets and ensuring efficient resource utilization across various environments.

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The requirement for high availability during query execution.

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The number of users concurrently using the cluster.

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The type of data source (e.g., S3 vs. ADLS).

Question 3

Which TWO of the following capabilities are native to the Databricks Unity Catalog?

Accepted Answer

Row and column-level security. Unity Catalog centralizes governance by providing a unified interface for managing permissions across workspaces. It enables fine-grained access control down to the row and column level and provides a centralized audit log. These capabilities are vital for organizations needing to manage data security at scale, ensuring that data engineers and scientists have consistent access control policies across the entire Lakehouse regardless of the specific compute resource or workspace utilized.

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Auto-scaling of compute clusters

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Automated ML model hyperparameter tuning

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Direct hardware driver installation

Question 4

A data engineer is configuring Unity Catalog row-level security on a table `prod.finance.transactions`. They want to ensure that users in the `us_team` group can only see rows where `region = 'US'`, while users in the `eu_team` group can only see rows where `region = 'EU'`. They create a row filter function `prod.security.region_filter`. Which TWO statements accurately describe how to implement and manage this row-level security? (Choose two.)

Accepted Answer

The row filter function must accept the column(s) as parameters and return a BOOLEAN value indicating whether the row should be visible.. Row-level security in Unity Catalog is implemented by attaching a row filter function to a table using ALTER TABLE ... SET ROW FILTER. The function receives column values and returns a boolean, often using identity functions to filter based on group membership. Both managed and external tables support row filters, and bypassing them requires the BYPASS ROW FILTER privilege.

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Row filters can only be applied to external tables, not managed tables, because managed tables do not support fine-grained access control.

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The row filter function must be defined in the same schema as the table it protects.

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Users with the MODIFY privilege on the table automatically bypass the row filter and see all rows.

Question 5

A team is transitioning to the Databricks Intelligence Platform. Which TWO actions are required to successfully register a table in Unity Catalog using the three-level namespace?

Accepted Answer

Grant the 'USE CATALOG' privilege on the target catalog.. The three-level namespace (catalog.schema.table) is a fundamental feature of Unity Catalog. To register a table, an engineer must have sufficient privileges in the specific catalog and schema, and the table must be created using the correct fully qualified path. Understanding this structure is crucial because it enables unified data discovery and governance across different environments, preventing namespace collisions and allowing for standardized cross-database operations in the platform.

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Configure a local Hive Metastore on the cluster.

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Manually update the cluster's Spark configuration to point to the metastore URL.

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Set the default database to 'default' before executing the CREATE statement.

Question 6

A data engineer is working on a Bronze-to-Silver transformation. Which THREE of the following practices are recommended to optimize performance and data quality during this stage?

Accepted Answer

Apply data quality checks to filter out or quarantine malformed records before writing to Silver.. Effective Bronze-to-Silver transformations are the foundation of a robust Lakehouse. By combining data validation, efficient partitioning, and incremental processing, engineers can minimize resource consumption while ensuring the data is reliable. These practices ensure the pipeline remains performant as volume scales and that the downstream Gold tables contain clean, query-optimized data, which is critical for business intelligence and data science use cases.

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Always use 'SELECT *' to ensure the entire schema is captured in the Silver table.

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Perform heavy joins and aggregations directly on the Bronze table.

Question 7

A Databricks job is failing due to 'Out of Memory' (OOM) errors during a join operation on two large datasets. Which TWO actions could help mitigate this issue?

Accepted Answer

Increase the cluster's worker node instance type with more memory.. OOM errors during joins often occur because the driver or worker nodes lack enough memory to handle the shuffle or broadcast operations. Increasing the cluster memory allows for larger data partitions to reside in memory, while adjusting the broadcast join threshold prevents the optimizer from attempting to broadcast datasets that exceed available node capacity. These configurations are essential for stabilizing large-scale ETL pipelines that process high-volume data.

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Decrease the number of partitions in the Spark cluster.

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Enable dynamic allocation for the cluster.

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Use the cache() method on every DataFrame in the pipeline.

Question 8

A data engineering team must implement dynamic row-level filtering on a customer analytics table in Unity Catalog so that regional analysts only view records corresponding to their assigned territory. Which TWO steps are required to achieve this using Unity Catalog features? (Choose 2)

Accepted Answer

Create a SQL user-defined function (UDF) that returns a boolean expression evaluating user identity and territory.. Implementing row-level security in Unity Catalog requires creating a SQL user-defined function that evaluates the current user's identity or group membership against a territory mapping table, and then applying that function to the target table using an ALTER TABLE command.

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Modify the underlying cloud storage bucket IAM policy to restrict read access based on user session tags.

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Create separate physical views for each regional analyst group and grant SELECT privileges exclusively on those views.

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Configure cluster-level Spark configurations to automatically inject WHERE clauses into user queries.

Question 9

Which TWO of the following statements accurately describe the functionality of Unity Catalog within the Databricks Intelligence Platform?

Accepted Answer

It provides centralized access control, auditing, and lineage for all data and AI assets.. Unity Catalog provides a unified governance layer for data and AI assets across a Databricks account. It simplifies security by providing a single place to define access control policies, and it enables cross-workspace data sharing. Understanding this is essential for engineers responsible for managing multi-workspace environments, as it replaces the older, workspace-specific Hive Metastore with a centralized, cloud-agnostic governance model for better auditability and compliance.

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It requires a separate Hive Metastore for each individual Databricks workspace.

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It is an execution engine used to run heavy machine learning training jobs.

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It only supports files stored in Amazon S3 buckets.

Question 10

Which THREE of the following are primary components of the Databricks Lakehouse architecture?

Accepted Answer

Delta Lake. The Databricks Lakehouse architecture combines the best of data warehouses and data lakes by using Delta Lake for reliable storage, Spark for performant compute, and Unity Catalog for centralized governance. These components work together to provide a unified platform that supports diverse workloads, including batch processing, streaming, data science, and business intelligence, ensuring that data is governed, performant, and reliable for all organizational stakeholders.

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Direct Hardware Access

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Proprietary File System Format