Databricks  Certified Data Engineer Associate Exam Dumps & Practice Test Questions

Question 1:

An executive overseeing data operations is facing issues due to inconsistent reporting between the analytics and engineering teams. Upon reviewing the situation, it's discovered that each team uses different data storage and processing systems, which has led to fragmented insights and unreliable data-driven decisions. The executive wants to eliminate these silos and ensure both teams operate with a consistent and unified view of enterprise data.

How can implementing a data lakehouse architecture help resolve these reporting inconsistencies between the 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

Explanation:

A data lakehouse architecture blends the capabilities of traditional data warehouses and data lakes, providing a unified platform for storage, governance, and analytics. The key benefit of this architecture is its ability to serve as a single source of truth for all data consumers, which helps align insights and decisions across departments.

In conventional environments, data engineers typically interact with unstructured or semi-structured raw data in data lakes, whereas data analysts access refined and structured data in data warehouses. These parallel systems can lead to differences in data transformation, schema evolution, and even data freshness. Consequently, KPIs and business metrics may vary depending on which team produced them.

The lakehouse model eliminates this disconnect by enabling all teams to operate on the same underlying data layer using familiar tools like SQL. With version control, unified metadata, and consistent schema enforcement, everyone accesses the same curated data set — reducing discrepancies and improving trust in reports.

Why the other options fall short:

• A refers to autoscaling, which enhances performance, but doesn’t resolve the core issue of fragmented data sources.

• C is about organizational restructuring, which won't solve architectural inconsistencies.

• D suggests improved collaboration, but doesn’t guarantee consistent data interpretation.

• E addresses response time for requests, not the root problem of mismatched data.

Ultimately, Option B is the most effective solution because a shared data foundation in a lakehouse ensures data consistency, which is the executive’s primary concern.

Question 2:

A data team is responsible for running a scheduled report on the Databricks platform. The report must begin execution as soon as it is triggered to meet strict time constraints. However, the team also wants to manage costs efficiently. They are exploring ways to reduce startup latency while keeping cloud resource usage optimized.

Which situation best illustrates when using a cluster pool would be the right approach?

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

Explanation:

A cluster pool in Databricks is a mechanism for improving job startup performance while maintaining cost efficiency. By pre-warming compute instances and keeping them idle in a pool, Databricks allows new jobs to start much faster than provisioning a fresh cluster each time. This setup is particularly advantageous for automated and scheduled jobs that need to run with minimal delay.

When a job is triggered, rather than waiting for new virtual machines to spin up and be configured — which can take several minutes — the job can immediately grab a ready-to-use cluster from the pool. This significantly reduces startup latency, making it ideal for time-sensitive workflows such as regularly scheduled reports or pipelines that must meet strict SLAs.

Why the other options don’t fit:

• B involves making code or processes reproducible, which is more about versioning and documentation than resource readiness.

• C focuses on debugging or development work, which doesn’t usually require minimized startup time.

• D refers to source code control and collaboration, typically handled by Git or Databricks Repos, not clusters.

• E relates to user access and permissions, which are managed via IAM roles or workspace-level controls, not through cluster pools.

Therefore, Option A is correct because cluster pools are designed specifically to minimize startup delays, making them an optimal choice for automated reporting tasks that demand immediate execution while also balancing cost.

Question 3:

In the traditional Databricks architecture, the platform is split into two main components: the control plane and the data plane. The control plane is responsible for centralized management tasks, while the data plane executes data processing workloads in the customer's cloud environment.

Which of the following components operates solely within the control plane?

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

Correct Answer: C

Explanation:

The classic Databricks architecture is designed around a separation of control and data responsibilities to improve scalability, security, and operational efficiency. The control plane is entirely managed by Databricks and is responsible for centralized functions such as workspace management, job scheduling, notebook execution, and API services. All user interactions with Databricks—such as creating notebooks, starting jobs, and managing clusters—go through this control plane.

The Databricks web application, which provides the user interface and APIs for interacting with notebooks, jobs, clusters, and security configurations, resides completely within the control plane. This centralization simplifies user management, access control, and auditing, and ensures a consistent experience regardless of the cloud infrastructure used.

The data plane, by contrast, runs inside the customer’s cloud account (such as AWS, Azure, or GCP) and is responsible for executing code and managing compute resources like worker and driver nodes. These nodes are provisioned to run actual data processing tasks, whether batch or streaming.

Let’s look at the incorrect options:

• A (Worker node) and E (Driver node): These are both part of the data plane and are launched in the customer’s environment to handle data processing.

• B (JDBC data source): This is an external database connection typically accessed during data ingestion or transformation, operating outside the Databricks control or data plane.

• D (DBFS): While DBFS integrates with both planes, the physical data often resides in the data plane (e.g., S3 or Azure Blob), although metadata may be managed through the control plane.

In conclusion, the Databricks web application is the only component listed that is completely managed within the control plane.

Question 4:

The Databricks Lake House architecture unifies the data lake and data warehouse paradigms by enabling a single platform for diverse analytics workloads. A key element that powers this architecture is Delta Lake, which brings advanced capabilities to raw data lakes.

What is one of the major advantages that Delta Lake offers in the Lakehouse environment?

A. Allows code to be written in different programming languages
B. Enables simultaneous real-time collaboration on notebooks
C. Provides alerting mechanisms for failed queries
D. Supports both batch and streaming data processing
E. Handles the distribution of complex computational tasks

Correct Answer: D

Explanation:

Delta Lake is an open-source storage layer that brings powerful features to Apache Spark and cloud object stores like Amazon S3 or Azure Data Lake. Its most critical value proposition in the context of the Databricks Lakehouse Platform is its ability to unify batch and streaming data processing. This means users can perform real-time data ingestion and analytics, while still being able to run traditional batch ETL pipelines—all on the same table.

The mechanism that enables this capability is the Delta Log, which records every change made to Delta tables. With this transactional log, Delta Lake ensures ACID transactions, schema enforcement, and data versioning. This means both streaming jobs (using structured streaming) and batch queries (using SQL or Spark jobs) can operate on the same data reliably, without the risk of data corruption or duplication.

This unified approach reduces the complexity that typically arises from maintaining separate infrastructures for real-time and batch data processing. For example, a retail business might use Delta Lake to capture streaming data from point-of-sale systems in real time for immediate analytics, while also running nightly sales aggregation reports—using the same Delta table.

Reviewing the alternatives:

• A refers to Spark’s multi-language support (e.g., Python, Scala, SQL), not Delta Lake specifically.

• B highlights a Databricks feature for team collaboration, which is unrelated to Delta Lake’s storage capabilities.

• C involves monitoring and alerting, typically managed via Databricks Jobs or third-party tools, not Delta Lake itself.

• E points to Spark’s distributed computing model, not a direct feature of Delta Lake.

By enabling batch and streaming on a single, consistent data source, Delta Lake makes the Lakehouse model both practical and powerful.

Question 5:

How does Delta Lake physically store its tables in terms of data, metadata, and versioning to support transactional consistency and other advanced capabilities?

A. Delta tables are kept in a single file that includes data, metadata, and history.
B. Delta tables store data in one file, while metadata is stored separately across multiple files.
C. Delta tables are stored as a set of files that contain data, metadata, transaction history, and other details.
D. Delta tables are composed of several files that store only the data records.
E. Delta tables use a single file format that stores only the data content.

Correct Answer: C

Explanation:

Delta Lake enhances the capabilities of traditional data lakes by introducing support for ACID transactions, schema evolution, and time travel. It accomplishes this by storing its tables in a structured folder format, rather than a single file or data-only approach.

At a high level, a Delta table consists of two core components:

1. Data files – These are stored in Parquet format, which enables efficient, columnar storage. The Parquet files contain the actual table rows (data records).

2. Transaction log (_delta_log) – This is a critical part of every Delta table. It contains a series of JSON files that record each transaction, such as inserts, updates, and deletes. It also stores schema information, table versioning, and operational metadata.

Every operation performed on a Delta table is appended as a new entry in the transaction log. Periodically, checkpoint files are created in Parquet format to optimize query performance by reducing the number of JSON log files that need to be read during query execution.

This structure allows Delta Lake to offer:

• ACID compliance, so concurrent reads and writes are consistent.

• Time travel, allowing access to earlier versions of the data.

• Schema enforcement and evolution, so schemas can evolve safely.

• Efficient metadata handling, which supports big data scale.

The incorrect answers misrepresent Delta’s architecture. Options A, B, and E falsely suggest data is stored in a single file. Option D ignores the transaction log, which is essential for Delta Lake’s consistency and advanced capabilities.

Thus, Delta tables are stored as a collection of files that include both data and rich metadata—making Option C the correct choice.

Question 6:

You need to remove rows from a Delta Lake table named my_table where the column age has a value greater than 25. What is the correct SQL command to accomplish this while ensuring the updated table reflects only the desired rows?

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

Explanation:

Delta Lake allows SQL-based data manipulation operations on large-scale datasets using Apache Spark. One of these operations is the DELETE command, which enables the removal of rows that match a specific condition while preserving the integrity of the table through ACID-compliant transactions.

In this case, the task is to delete all rows where age > 25 from the my_table Delta table. The correct SQL syntax for this operation is:

This command scans all records in the table and removes those that satisfy the condition age > 25. The rest of the rows remain unchanged. The operation is logged in Delta Lake’s transaction log (_delta_log), making it auditable and reversible if needed via time travel.

Why the other options are incorrect:

• A (SELECT): This only retrieves data and doesn’t modify the table. It cannot delete rows.

• B (UPDATE without SET clause): The UPDATE command is syntactically incorrect without a SET statement to modify a specific column.

• D (UPDATE WHERE age <= 25): This also lacks a SET clause and targets the wrong records — it updates rows you want to keep.

• E (DELETE WHERE age <= 25): This deletes the rows you intended to retain, which is the opposite of the desired behavior.

Delta Lake’s support for DELETE makes it easy to perform such conditional row removals without managing partitions or writing complex Spark jobs. It streamlines data engineering workflows, especially in dynamic datasets requiring real-time or batch cleanup tasks. Hence, Option C is the accurate and efficient solution.

Question 7:

A data engineer used Delta Lake’s Time Travel feature to recover a table's version from three days ago, aiming to undo a recent mistake. However, the operation failed with an error stating that the required historical files no longer exist. 

What is the most likely reason this data is no longer available?

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

Explanation:

Delta Lake's Time Travel feature is designed to let data engineers view or restore a table to a previous state by referencing a version number or timestamp. This is particularly useful in scenarios like rolling back accidental updates or debugging historical changes.

Time Travel works by keeping track of previous table states and maintaining references to the underlying Parquet data files that were part of those states. However, this functionality is only reliable as long as the physical data files are still present in the storage layer (e.g., S3 or DBFS). By default, Delta Lake retains these data files for seven days, but this period can be adjusted.

The VACUUM command is used to permanently remove files that are no longer needed—specifically, files that are not part of the latest state or any retained historical versions. If a user executes VACUUM with a very short retention window (e.g., RETAIN 0 HOURS), then the older files that Time Travel relies on may be deleted immediately.

As a result, when someone tries to restore a version that’s older than the vacuum retention period, the process fails because the actual data files needed to reconstruct that version are gone.

Let’s look at the incorrect options:

• B (TIME TRAVEL) is a read-only feature. It accesses data but does not remove or alter files.

• C (DELETE HISTORY) is not a valid Delta Lake command.

• D (OPTIMIZE) reorganizes small files into larger ones for performance; it does not delete historical data.

• E (HISTORY) simply provides an audit log of past operations; it doesn’t delete any data.

Thus, the most likely cause for the failure in accessing the older version is that VACUUM was run with a retention period that excluded the required historical files.

Question 8:

Databricks Repos allow teams to integrate with Git-based source control tools for collaboration on notebooks and other artifacts. While many Git actions can be performed directly within the Databricks user interface.

Which of the following Git operations must still be carried out externally using a Git hosting service or command-line interface?

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

Correct Answer:  E

Explanation:

Databricks Repos offer native integration with popular Git providers such as GitHub, GitLab, and Bitbucket, making it easier for teams to follow version control best practices while developing and maintaining notebooks, workflows, and supporting files. This includes the ability to perform standard Git actions like committing code changes, pulling updates from remote branches, pushing new commits to the remote repository, and cloning repositories directly into the Databricks workspace.

However, not every Git operation is fully supported within the Databricks UI. One key limitation is merging branches, which remains an external process. Merging is often complex, as it involves reconciling conflicting changes, validating integration logic, and running pre-merge checks—tasks that are typically done using a Git client, the command line, or platforms like GitHub with pull requests.

Here’s why the other options are incorrect:

• A (Commit): Users can stage and commit changes directly from the Databricks interface. This supports basic Git workflows without leaving the workspace.

• B (Pull): Syncing local changes with the remote repository is fully supported and allows users to keep their notebooks up to date.

• C (Push): Once changes are committed, users can push them to the remote repository with the in-UI tools.

• D (Clone): Databricks supports cloning Git repositories when first setting up a Databricks Repo, allowing seamless integration with existing version-controlled projects.

Only E (Merge) requires users to leave the Databricks environment. This is because merging typically needs conflict resolution tools and deeper version control logic that isn’t provided in the streamlined Databricks interface. Once a merge is completed externally, users can pull the changes back into Databricks Repos to continue working with the latest branch version.

To summarize, merge operations must be handled outside of Databricks, making E the correct choice.

Question 9:

How does the lakehouse architecture specifically enhance data quality compared to a traditional data lake setup?

A. It stores both structured and unstructured data formats
B. It enables transactions that comply with ACID properties
C. It allows data analysis using SQL queries
D. It uses open file formats for data storage
E. It supports machine learning and artificial intelligence workloads

Correct Answer: B

Explanation:

A significant improvement introduced by the data lakehouse architecture over traditional data lakes is its ability to guarantee data integrity and reliability through ACID-compliant transactions. In traditional data lakes, issues like data corruption, partial updates, and schema inconsistencies are common due to the absence of transactional controls. This lack of structure can degrade the reliability of analytics and machine learning outputs.

By supporting ACID (Atomicity, Consistency, Isolation, Durability) properties, lakehouses enable reliable and consistent data management. Atomicity ensures that all components of a transaction either complete together or not at all. Consistency ensures that data always adheres to defined constraints. Isolation ensures that concurrent transactions do not interfere with each other. Durability means once a transaction is committed, it remains so, even in the case of system failures.

This transactional support is typically powered by technologies like Delta Lake within the lakehouse, allowing users to safely perform updates, deletes, merges, and time-travel queries. These features are vital for enforcing schema and guaranteeing accurate read/write operations. None of these features are inherently available in traditional data lakes that rely on systems like Amazon S3 or Hadoop HDFS.

Examining the other options:

• A highlights format flexibility, which aids storage, not necessarily data quality.

• C enables querying but doesn’t resolve inconsistency issues.

• D offers interoperability benefits but doesn’t enforce schema or correctness.

• E refers to workloads enabled by the architecture, not data integrity itself.

Therefore, ACID compliance is the most direct and influential factor in improving data quality in a lakehouse environment.

Question 10:

When comparing Databricks Repos to the built-in notebook version history, what is the main benefit that Repos offer for collaborative development workflows?

A. It automatically saves users’ changes during development
B. It supports working with multiple development branches
C. It allows previous notebook versions to be restored
D. It lets users comment on code changes directly
E. It is completely integrated into the Databricks platform

Correct Answer: B

Explanation:

While Databricks Notebooks offer built-in version history that allows users to view and revert past changes, this feature lacks the flexibility and collaboration tools necessary for complex development workflows. This is where Databricks Repos shine—particularly because they offer native support for multiple Git branches, which is essential for managing evolving codebases and enabling team collaboration.

With branching, team members can work on different features, fixes, or experiments in parallel without overwriting each other’s progress. Each developer can push changes to their own branch, review the work, and then merge it into the main branch once it's ready. This process is a fundamental practice in software engineering and is equally valuable in data engineering, where reproducibility and controlled collaboration are vital.

In contrast, the notebook version history only allows for linear tracking of changes, which quickly becomes inadequate as projects grow and involve multiple contributors. It doesn't support isolated development tracks or advanced merging strategies.

Looking at the alternatives:

• A (Autosave) is present in both notebooks and Repos, offering no unique advantage.

• C (Version rollback) is already available in the built-in history feature.

• D (Commenting) requires integration with external Git tools like GitHub—Repos alone don’t provide in-platform commenting.

• E is inaccurate since Databricks Repos depend on connections with external Git repositories and are not fully contained within Databricks.

Thus, the key differentiator is branching support, making Databricks Repos a superior choice for teams needing advanced collaboration, testing, and version control workflows.