Databricks Certified Data Engineer Associate Practice Test Questions, Exam Dumps

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Certified Data Engineer Associate Databricks Practice Test Questions and Exam Dumps

Question No 1:

A data organization leader is frustrated because the data analysis team and the data engineering team are producing inconsistent reports. Upon investigation, it becomes clear that the teams are using separate data systems and architectures, leading to data silos and discrepancies in business insights. The leader wants to resolve these inconsistencies and promote a unified, consistent view of organizational data.

How could adopting a data lake house architecture help resolve these discrepancies and promote alignment between both teams?

A. Both teams would autoscale their work as data size evolves.
B. Both teams would use the same source of truth for their work.
C. Both teams would reorganize to report to the same department.
D. Both teams would be able to collaborate on projects in real-time.
E. Both teams would respond more quickly to ad-hoc requests.

Correct Answer:
B. Both teams would use the same source of truth for their work.

Explanation:

A data lakehouse architecture combines the best of data lakes (scalable, low-cost storage for structured and unstructured data) and data warehouses (performance-optimized analytics). One of the primary benefits of the lakehouse model is that all data consumers — whether analysts, engineers, or scientists — can operate from the same source of truth.

In traditional setups:

Data engineers often use raw or staged data from lakes.
Data analysts may rely on transformed warehouse data. This separation leads to inconsistent insights and misaligned KPIs due to different processing pipelines, versions, and schemas — a problem known as data silos.

A lakehouse solves this by allowing all teams to:

Access versioned, structured data directly from the data lake using SQL APIs.
Share unified governance, lineage, and quality controls.
Ensure that transformations and results are consistent across teams.

Why other options are incorrect:

A (Autoscaling) is helpful for performance but does not solve data consistency.
C (Reorganization) addresses team structure, not data architecture.
D (Real-time collaboration) may be a benefit, but it does not solve the core issue of inconsistent data sources.
E (Faster ad-hoc response) is desirable but secondary to eliminating inconsistent data.

Thus, using a shared data source is the true solution, which the lakehouse model provides.

Question No 2:

A data team runs an automated report on Databricks. The report is scheduled and needs to start running as quickly as possible once triggered. The team wants to minimize startup delays while saving compute costs for regular tasks. They are evaluating features that improve resource efficiency and reduce wait time.

Which scenario best describes a use case where a cluster pool would be appropriate?

A. An automated report needs to be refreshed as quickly as possible.
B. An automated report needs to be made reproducible.
C. An automated report needs to be tested to identify errors.
D. An automated report needs to be version-controlled across multiple collaborators.
E. An automated report needs to be runnable by all stakeholders.

Correct Answer:
A. An automated report needs to be refreshed as quickly as possible.

Explanation:

Cluster pools in Databricks are a resource management feature that reduces cluster startup latency by maintaining a ready-to-use set of idle instances. When a job is triggered, it can quickly launch on the pre-warmed resources, saving minutes compared to starting a new cluster from scratch.

This makes cluster pools ideal for:

Scheduled jobs and automated reporting.
Production pipelines where fast response time is key.
Repetitive, latency-sensitive tasks.

Why other options are incorrect:

B (Reproducibility) relates more to versioning of notebooks or data, not compute.
C (Testing for errors) is part of development, not production optimization.
D (Version control) is handled via Git or Databricks Repos.
E (Access control) is managed through permissions, not cluster pools.

Thus, Option A accurately reflects why teams would use cluster pools — to refresh reports quickly with minimal delay.

Question No 3:

In the classic Databricks architecture, the platform is divided into the control plane and the data plane. The control plane handles management functions, while the data plane handles customer data processing.

Which of the following components is hosted entirely in the control plane?

A. Worker node
B. JDBC data source
C. Databricks web application
D. Databricks Filesystem (DBFS)
E. Driver node

Correct Answer: C. Databricks web application

Explanation:

The classic Databricks architecture separates responsibilities between:

Control Plane: Managed by Databricks. Handles job scheduling, cluster configuration, notebook interfaces, authentication, etc.
Data Plane: Runs in your cloud account. Handles actual data processing on your VMs (e.g., worker and driver nodes).

The Databricks web application (UIs, APIs, workspace management) is part of the control plane, hosted by Databricks. This ensures centralized access control, governance, and a consistent user experience across environments.

Why other options are incorrect:

A & E (Worker/Driver nodes): These reside in the data plane and handle data execution.
B (JDBC sources): These are external systems accessed from the data plane.
D (DBFS): Although managed, most DBFS data resides in the data plane, depending on storage configuration.

Hence, only Option C, the web application, is fully hosted and managed in the control plane.

Question No 4:

The Databricks Lakehouse Platform integrates the scalability and flexibility of data lakes with the performance and reliability of data warehouses. A critical component that enables this hybrid architecture is Delta Lake, an open-source storage layer that brings advanced capabilities to data lakes.

One of the key goals of using Delta Lake is to improve data reliability and performance while enabling advanced data workloads. In the context of this architecture, Delta Lake plays an essential role in making the Databricks Lakehouse Platform suitable for a broad range of use cases.

Which of the following is a specific benefit that Delta Lake provides within the Databricks Lakehouse Platform?

A. The ability to manipulate the same data using a variety of languages
B. The ability to collaborate in real time on a single notebook
C. The ability to set up alerts for query failures
D. The ability to support batch and streaming workloads
E. The ability to distribute complex data operations

Correct Answer: D. The ability to support batch and streaming workloads

Explanation:

Delta Lake is a powerful storage layer designed to add ACID transaction support, schema enforcement, and unified processing to Apache Spark-based data lakes. One of its most distinguishing features is support for both batch and streaming workloads on the same underlying data.

In traditional architectures, batch processing and streaming are often handled by separate systems, leading to data duplication, inconsistency, and operational complexity. Delta Lake solves this by enabling a unified data architecture where you can use the same table for both real-time streaming data and batch analytics. This is made possible by Delta’s transaction log (Delta Log), which records all changes to the data and allows for reliable, atomic operations.

This capability is crucial for modern data platforms, where businesses want real-time insights while still running periodic batch ETL processes. For example, an e-commerce company could stream user interactions to a Delta table in real time for fraud detection, while also running daily sales reports from the same table in batch mode.

Comparing with other options:

A (Variety of languages) is a feature of Apache Spark and Databricks itself, not specifically of Delta Lake.
B (Collaborate on notebooks) is a Databricks Workspace feature, unrelated to Delta Lake.
C (Query failure alerts) is more aligned with monitoring/alerting tools like Databricks Jobs or external platforms.
E (Distribute operations) refers to Spark's general distributed processing model, not a unique Delta Lake feature.

By supporting streaming and batch workloads on a single data store, Delta Lake reduces infrastructure complexity, ensures data consistency, and simplifies engineering workflows—making it a core enabler of the Lakehouse architecture.

Question No 5:

Delta Lake enhances the traditional data lake by providing a robust storage format for big data workloads. A Delta table is a structured format that tracks data changes, maintains schema consistency, and supports transactional operations.

To understand how Delta Lake achieves these capabilities, it's essential to look at how Delta tables are physically stored.

Which of the following best describes how data and metadata are organized within a Delta table?

A. Delta tables are stored in a single file that contains data, history, metadata, and other attributes.
B. Delta tables store their data in a single file and all metadata in a collection of files in a separate location.
C. Delta tables are stored in a collection of files that contain data, history, metadata, and other attributes.

D. Delta tables are stored in a collection of files that contain only the data stored within the table.
E. Delta tables are stored in a single file that contains only the data stored within the table.

Correct Answer: C. Delta tables are stored in a collection of files that contain data, history, metadata, and other attributes

Explanation:

Delta Lake stores data in a structured directory format on cloud storage systems like Amazon S3, Azure Data Lake, or HDFS. A Delta table consists of Parquet data files for the actual data and a transaction log directory that stores metadata, history, and operational context.

Here’s a breakdown of how Delta Lake organizes a table:

Data Files: The actual content (rows and columns) is stored in Parquet format across multiple files. This supports efficient columnar storage and querying.
Transaction Log (_delta_log): This directory contains JSON and checkpoint files that store:

Metadata about the schema
Details of every transaction (insert, update, delete)
Table version history for time travel
Information used for ACID compliance and schema enforcement

Together, this structure allows Delta Lake to offer:

ACID Transactions
Schema Evolution
Time Travel and Data Versioning
Scalable Metadata Handling

Each Delta table is not a single file but rather a directory structure—making it suitable for distributed processing systems like Spark. The log and metadata files track changes, making the data resilient to failures and consistent across concurrent reads and writes.

Incorrect options:

A, B, E suggest Delta tables are stored in single files, which is not true and would limit scalability.
D wrongly implies that Delta tables only contain data, ignoring the crucial role of the transaction log and metadata.

By using this multi-file, transaction-aware structure, Delta Lake enables powerful features that surpass traditional Parquet tables or basic data lakes.

Question No 6:

You are working with a Delta Lake table named my_table that contains a column called age. Your task is to delete all rows where the value in the age column is greater than 25. After this operation, the updated version of the table should only contain rows where age is 25 or less.

Which SQL command correctly removes the desired rows from the Delta table and saves the updated version?

A. SELECT * FROM my_table WHERE age > 25;
B. UPDATE my_table WHERE age > 25;
C. DELETE FROM my_table WHERE age > 25;
D. UPDATE my_table WHERE age <= 25;
E. DELETE FROM my_table WHERE age <= 25;

Correct Answer:
C. DELETE FROM my_table WHERE age > 25;

Explanation:

Delta Lake is a storage layer that brings ACID transactions to big data workloads using Apache Spark. One of its advantages is the ability to perform declarative operations, such as DELETE, on massive datasets while maintaining reliability.

To remove rows from a Delta table based on a specific condition, the DELETE SQL command is the correct approach. In this scenario, the requirement is to delete rows where age > 25, and the proper syntax is:

This operation will:

Check each row in the my_table
Remove only those where age is greater than 25
Keep all other rows unchanged
Update the table while preserving ACID guarantees

Explanation of Incorrect Options:

A (SELECT) only reads data, it does not modify or save changes.
B (UPDATE) is incorrectly formatted and would require setting a value (e.g., UPDATE my_table SET column=value WHERE...)
D (UPDATE WHERE age <= 25) does not remove any rows, and again lacks the required SET clause.
E (DELETE WHERE age <= 25) deletes the wrong rows — it would remove those you are supposed to keep.

Using Delta’s built-in support for DELETE, you can efficiently clean your data while maintaining data integrity and auditability via the Delta transaction log.

Question No 7:

A data engineer accidentally introduced an error into a Delta Lake table during a daily update. They want to use Delta Time Travel to restore the table to its state from 3 days ago. However, when attempting to access that version, the process fails because the data files no longer exist.

What is the most likely reason the required historical files for time travel have been deleted?

A. The VACUUM command was run on the table
B. The TIME TRAVEL command was run on the table
C. The DELETE HISTORY command was run on the table
D. The OPTIMIZE command was run on the table
E. The HISTORY command was run on the table

Correct Answer:
A. The VACUUM command was run on the table

Explanation:

Delta Lake offers a powerful feature called Time Travel, which allows users to query and restore data from previous versions of a Delta table. This capability is useful for:

Auditing historical changes
Debugging issues
Reverting unwanted updates

However, this feature depends on older data files being available on disk. By default, Delta Lake retains data files for 7 days, unless manually configured otherwise.

The VACUUM command is used to permanently delete files that are no longer referenced by the latest version of the table. If a VACUUM is run with a retention period shorter than the age of the desired version (e.g., VACUUM my_table RETAIN 0 HOURS), then older files — including those needed for time travel — are physically deleted from storage.

Once deleted, time travel to those earlier versions becomes impossible, and users receive errors when attempting to access them.

Why other options are incorrect:

B (TIME TRAVEL) is a read-only feature; it does not remove data.
C (DELETE HISTORY) is not a valid Delta Lake command.
D (OPTIMIZE) compacts files for performance but doesn’t remove historical versions.
E (HISTORY) lists previous operations but doesn’t delete data.

To preserve access to older versions, avoid running VACUUM with aggressive retention unless you're sure time travel is not needed. It’s best practice to retain historical data for at least as long as required by your data governance or rollback policies.

Question No 8:

Databricks Repos provide seamless Git integration, allowing teams to collaborate on notebooks and other code assets using source control best practices. However, not all Git operations can be performed directly within the Databricks user interface. Some operations must be handled externally using a Git client or a Git hosting service like GitHub, GitLab, or Bitbucket.

Which of the following Git operations must be performed outside of Databricks Repos?

A. Commit
B. Pull
C. Push
D. Clone
E. Merge

Correct Answer: E. Merge

Explanation:

Databricks Repos provide an integrated interface for working with Git-based version control systems. Users can perform common Git operations like pulling changes, committing updates, and pushing modifications directly within the Databricks UI. These features allow data teams to collaborate on notebooks, jobs, and libraries using source control best practices without leaving the platform.

However, not all Git operations are fully supported within Databricks Repos. Specifically, merging branches—a key Git operation used to integrate changes from one branch into another—must be performed outside of the Databricks Repos interface. Merging typically involves resolving conflicts and reviewing code, tasks that are better handled in dedicated Git tools or Git hosting platforms (e.g., GitHub pull requests, Git CLI, or a Git GUI client).

Let’s evaluate the other options:

A. Commit – Supported in Databricks Repos. Users can stage and commit changes from the UI.
B. Pull – Supported. Users can sync their local changes with the remote repository using the “Pull” function.
C. Push – Supported. After committing changes, users can push them to the remote repository.
D. Clone – Supported when linking a Git repository to a Databricks Repo.

Only E. Merge is excluded from in-UI operations. Instead, users are advised to perform merges outside of Databricks and then use pull within Databricks Repos to update the workspace.

This limitation exists to keep the Repos interface lightweight and to prevent complex conflict resolution scenarios that are better handled in full-featured Git environments.

Question No 9:

A major advantage of the data lakehouse architecture over traditional data lakes is improved data reliability and quality. Traditional data lakes often suffer from data corruption, inconsistency, or missing schema enforcement, which can impact analytics and machine learning accuracy.

Which of the following features of a data lakehouse architecture directly contributes to better data quality compared to a traditional data lake?

A. A data lakehouse provides storage solutions for structured and unstructured data.
B. A data lakehouse supports ACID-compliant transactions.
C. A data lakehouse allows the use of SQL queries to examine data.
D. A data lakehouse stores data in open formats.
E. A data lakehouse enables machine learning and artificial intelligence workloads.

Correct Answer: B. A data lakehouse supports ACID-compliant transactions

Explanation:

Data lakehouses blend the flexibility of data lakes with the reliability and performance of data warehouses. One of the most transformative features introduced by the lakehouse model—enabled by storage layers like Delta Lake—is support for ACID transactions (Atomicity, Consistency, Isolation, Durability).

In a traditional data lake, updates and deletes can lead to inconsistent states, data corruption, and concurrency issues. There’s no built-in way to ensure that read and write operations are properly isolated or that failures don’t leave the data in a partial or corrupt state.

ACID transactions solve this by guaranteeing:

Atomicity – All parts of a transaction succeed or none do.
Consistency – Data adheres to defined rules after every transaction.
Isolation – Concurrent operations don't interfere with each other.
Durability – Once a transaction is committed, it remains intact despite failures.

This dramatically improves data quality, making it easier to trust the results of analytics and machine learning models. It also supports time travel, schema enforcement, and rollback capabilities—none of which are natively possible in raw cloud storage systems like S3 or HDFS.

Other options:

A (Structured & unstructured storage): True, but doesn’t directly improve data quality.
C (SQL queries): Enables querying, but doesn’t ensure accuracy or consistency.
D (Open formats): Promotes interoperability, not quality enforcement.
E (ML/AI workloads): Related to use cases, not data reliability.

Thus, ACID compliance is the cornerstone of higher data quality in a lakehouse.

Question No 10:

A data engineering team is choosing between using Databricks Notebooks’ built-in version history and integrating Databricks Repos for Git-based version control. While the notebook version history provides a basic track record of changes, it has limitations in collaborative workflows and project branching.

Which of the following is a major advantage of using Databricks Repos over the built-in notebook versioning system?

A. Databricks Repos automatically saves development progress
B. Databricks Repos supports the use of multiple branches
C. Databricks Repos allows users to revert to previous versions of a notebook
D. Databricks Repos provides the ability to comment on specific changes
E. Databricks Repos is wholly housed within the Databricks Lakehouse Platform

Correct Answer: B. Databricks Repos supports the use of multiple branches

Explanation:

Databricks Notebooks have a built-in version history that lets users revert changes and track past versions of a notebook. However, this feature is relatively limited—it doesn't support Git-style collaboration, branching, or advanced version control workflows. For more robust project management, teams turn to Databricks Repos, which allows Git-based versioning directly in the Databricks environment.

One of the most significant advantages of using Databricks Repos is its support for multiple Git branches. This allows developers to:

Work on different features in isolation
Collaborate without overwriting each other’s changes
Run experiments or fixes without affecting the main codebase
Merge changes after review, improving quality control

Branching is a core Git capability and essential in software development, data engineering, and MLOps. It enables collaborative and iterative development, which the built-in notebook versioning doesn’t support.

Let’s review the other choices:

A (Autosave) – Both systems autosave progress.
C (Revert versions) – Built-in versioning already allows reverts.
D (Comments on changes) – Requires external Git providers like GitHub for pull request commenting, not native to Databricks Repos.
E (Wholly housed) – Databricks Repos integrates with external Git platforms (like GitHub or GitLab), so it's not entirely within Databricks.

Thus, B is the correct answer. Support for multiple branches in Databricks Repos brings powerful version control and collaborative benefits that go far beyond basic version tracking in Notebooks.