Databricks-DE-Assoc Practice Test 7 — 10 Questions

Question 1

Refer to the exhibit. An engineer applies these configurations to a cluster. What is the primary benefit of enabling the Databricks IO Cache for a workload that involves repeatedly reading the same Delta tables?

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

Accepted Answer

It speeds up repeated reads by caching data on local SSDs.. The Databricks IO Cache (also known as the disk cache) accelerates data reads by caching remote data on the local SSDs of the worker nodes. For workloads that frequently query the same tables, this eliminates the latency and network overhead of repeatedly fetching data from cloud object storage. This is particularly effective for read-heavy analytical workloads, enabling significantly faster query execution times by leveraging high-speed local disk I/O.

Answer

It enables ACID transactions for non-Delta tables.

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It automatically scales the cluster based on disk usage.

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It forces the cluster to store all data in memory.

Question 2

A data engineer is analyzing a Spark job that is failing with 'Out of Memory' (OOM) errors. Which configuration parameter should be tuned to increase the amount of memory allocated to the execution of joins and aggregations?

Accepted Answer

spark.memory.fraction. Spark's memory management divides heap memory into storage and execution. Joins and aggregations occur in the execution memory. When these operations process large datasets that exceed the allocated execution memory, Spark may fail with OOM. Tuning the memory fraction configuration allows the engineer to shift the balance between storage (for caching) and execution, providing more headroom for complex shuffle-heavy operations like joins and group-by aggregations.

Answer

spark.executor.cores

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spark.sql.shuffle.partitions

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spark.driver.memory

Question 3

Which THREE of the following are benefits of using Unity Catalog over the legacy Hive Metastore?

Accepted Answer

Support for data lineage at the column level.. Unity Catalog provides a unified, centralized governance layer that spans all workspaces, clouds, and regions. Unlike the legacy Hive Metastore, which is often siloed and lacks granular control, Unity Catalog offers robust auditing, data lineage, and simplified access management. These features are vital for modern enterprises managing complex, multi-workspace environments that require consistent security postures and comprehensive visibility into data movement and usage patterns.

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Automated data replication to other cloud vendors.

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Automatic hardware upgrades for all clusters.

Question 4

Which of the following is a fundamental principle of implementing effective CI/CD for Databricks workflows?

Accepted Answer

Using environment variables to manage service endpoints and authentication secrets.. Effective CI/CD requires separating code logic from environment-specific configurations. By parameterizing job settings and using environment-specific variables, teams ensure that the same code can be deployed safely across development, testing, and production. This practice minimizes errors during promotion and allows for automated testing cycles, which are essential for maintaining a robust, stable, and scalable data platform in any enterprise Databricks environment.

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Deploying code from a local laptop directly to the production cluster.

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Merging all development code into the main branch every few months.

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Ignoring unit testing until the code is fully deployed in production.

Question 5

A data engineer is tasked with securing sensitive PII data in Unity Catalog. Which THREE actions are recommended to ensure robust security and compliance?

Accepted Answer

Apply Dynamic Data Masking to columns containing PII.. Implementing a defense-in-depth strategy is crucial for PII. Using Unity Catalog's fine-grained access controls, dynamic masking, and audit logs provides a layered approach to security. These tools allow organizations to restrict access, obscure sensitive values, and maintain a verifiable trail of who accessed what data, which is essential for meeting regulatory requirements and minimizing the risk of unauthorized data breaches.

Answer

Grant SELECT access to all users to ensure data accessibility.

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Store all PII data in the root metastore directory for easy access.

Question 6

A streaming job using Structured Streaming is lagging significantly behind the 'current time'. Which THREE of the following could be the root cause of this processing latency?

Accepted Answer

The trigger interval is set to a time shorter than the processing time of the batch.. Streaming latency often stems from resource contention, inefficient state management, or micro-batch trigger configurations. When the input rate exceeds the processing rate, lag accumulates. Identifying these bottlenecks requires analyzing the Spark UI for processing time vs. batch time. Understanding these factors is vital for maintaining low-latency pipelines and ensuring that SLAs are met in production-grade streaming environments deployed on Databricks.

Answer

Using a fixed-size cluster with no auto-scaling enabled.

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Using too few partitions in the input source.

Question 7

Which of the following describes the correct order of operations to configure a new external location in Unity Catalog?

Accepted Answer

Create credential, create location, grant privileges.. The process begins with creating a storage credential, which holds the cloud-specific security details (like an IAM role or service account). This credential is then used to define the external location, which points to a specific path in cloud storage. Finally, you grant the user or service the 'READ FILES' or 'WRITE FILES' privilege on the external location to permit interaction with the underlying data files within the Unity Catalog framework.

Answer

Grant privileges, create location, create credential.

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Create location, grant privileges, create credential.

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Create credential, grant privileges, create location.

Question 8

Refer to the exhibit. A data engineer is configuring an IAM policy for a Databricks storage credential. Which of the following is the most significant security risk in this configuration?

Accepted Answer

The policy allows access to all objects in the bucket rather than using specific prefixes.. The policy uses overly permissive resource definitions and potentially excessive actions if not scoped correctly. In production environments, adhering to the principle of least privilege is critical. Granting broad 's3:ListBucket' or 's3:GetObject' access to an entire bucket without specific prefix scoping can lead to data exfiltration risks or unauthorized access to non-production data stored in the same bucket.

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The policy restricts access to only a single file.

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The policy lacks an explicit 'Deny' statement for all other resources.

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The policy is missing a condition to require multi-factor authentication (MFA).

Question 9

An engineer has a large table that is frequently joined with small dimension tables. To optimize this, which optimization technique should be applied to the join operation?

Accepted Answer

Use a Broadcast Hash Join.. Broadcasting small tables prevents the 'shuffle' of the larger table. By sending a copy of the dimension table to every executor, Spark can perform the join locally, significantly reducing network overhead. This is a fundamental optimization for star schemas in Databricks. Knowing when and how to force broadcast joins allows engineers to dramatically speed up analytical queries by minimizing the movement of massive datasets across the cluster network.

Answer

Implement a Shuffle Hash Join.

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Increase the number of shuffle partitions.

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Enable Z-Ordering on the join key.

Question 10

Which component of Databricks CI/CD is responsible for executing automated tests on code before it is merged into the main branch?

Accepted Answer

The CI/CD runner (e.g., GitHub Actions, Jenkins).. CI/CD runners or build servers (like GitHub Actions, GitLab CI, or Jenkins) are the components responsible for triggering automated testing. By running tests in an isolated environment during the pull request process, these tools ensure that only validated code is merged. This 'fail-fast' approach is critical for maintaining code quality, reducing regression risks, and providing developers with immediate feedback on their changes before they ever impact the production pipeline.

Answer

The Databricks Delta Lake storage layer.

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The Unity Catalog governance framework.

Answer

The Databricks SQL Warehouse.