Databricks Certified Data Engineer Professional Exam Dumps & Practice Test Questions
Question No 1:
A junior data engineer is tasked with setting up a Delta Lake silver table called silver_device_recordings, which will be used as a key data source for both production-level dashboards and a machine learning model. The raw data consists of deeply nested JSON structures, each containing 100 unique fields. Out of these, 45 fields are currently utilized by the various downstream applications.
To handle this complex schema, the engineer is considering the best approach for declaring the schema of the silver_device_recordings table. The engineer must decide between relying on automatic schema inference or manually defining the schema to ensure data integrity and maintainability.
Which statement best reflects the considerations specific to Delta Lake and Databricks that would guide the engineer's decision?
A The Tungsten encoding in Databricks is optimized for string storage; with native support for JSON queries, string data types are always more efficient.
B Since Delta Lake uses Parquet, schema changes can be made directly by modifying file footers, making evolution effortless.
C Human coding effort is the most expensive part of data engineering, so schema automation should always be prioritized.
D Schema inference in Databricks uses permissive data types to accommodate varied data; thus, manually defining the schema offers stronger guarantees for data quality enforcement.
E Databricks’ schema inference and evolution features ensure perfect alignment with downstream data type expectations.
Answer: D
Explanation:
In a Lakehouse architecture using Delta Lake on Databricks, managing schemas properly is essential, especially when dealing with complex and nested data like JSON. Delta Lake provides two options for schema management: schema inference and schema enforcement. Although schema inference can save time, it comes with risks, particularly regarding data quality and compatibility with downstream applications.
When schema inference is used, Databricks automatically deduces data types, which may result in overly flexible or permissive data types, such as Strings or Arrays of Structs. These generalized types may not match the exact expectations of downstream systems, such as dashboards or machine learning models. For instance, if a field is expected to be a Double but is formatted inconsistently in the raw JSON, the system might infer it as a String, leading to potential errors.
On the other hand, manually defining the schema allows for precise control over data types, which can help ensure that the data is correctly structured and that any inconsistencies or errors are caught early in the process. This approach offers stronger guarantees regarding data integrity and compatibility with downstream systems.
Option D accurately reflects this tradeoff between flexibility and control. It highlights how schema inference may be permissive, whereas manually defining the schema provides stronger enforcement of data quality. Options A, B, and E present incorrect technical assumptions, and Option C suggests an over-reliance on automation without considering the risks associated with schema inference.
For production environments, particularly where data quality and consistency are critical, manually declaring the schema is recommended to avoid unexpected issues.
Question No 2:
An enterprise data engineering team is migrating a large-scale system, consisting of thousands of tables and views, into a Lakehouse architecture. The data is categorized into three quality tiers:
Bronze tables primarily support production data engineering workflows.
Silver tables are used by both data engineering and machine learning workloads.
Gold tables are mainly used for business intelligence (BI) and reporting.
All tiers contain personally identifiable information (PII), with strict pseudonymization and anonymization policies applied at the silver and gold levels.
Given the organization's dual goals of minimizing security risks and enhancing cross-team collaboration, which practice best aligns with industry standards for structuring and securing data in the Lakehouse?
A Isolating tables into separate databases based on data quality tiers enables easy permissions management using database ACLs and allows for physical separation of storage locations for managed tables.
B Since databases in Databricks are a logical construct, the organization of databases does not impact security or discoverability in the Lakehouse.
C Storing all production tables in a single database provides a unified view of all data assets, simplifying discoverability by granting all users view privileges.
D Working in the default Databricks database provides the greatest security for managed tables, as they are created in the DBFS root.
E Because all tables must reside in the same storage containers as the database they are created in, organizations may need to create dozens or even thousands of databases depending on their data isolation requirements.
Answer: A
Explanation:
In a Lakehouse architecture, structuring and securing data according to its quality tier—Bronze, Silver, and Gold—is a widely recommended best practice. This organization not only improves clarity but also ensures better access control and data security. Option A suggests a well-regarded method: isolating tables into separate databases based on their quality tier, which simplifies permission management using database-level Access Control Lists (ACLs).
This approach allows administrators to enforce the principle of least privilege, ensuring that users have access only to the data that is relevant to their role. For example, data engineers may have full access to Bronze and Silver databases but may only have read-only access to the Gold tables used for reporting. Additionally, separating data by tier also ensures physical isolation of managed table storage, providing an added layer of data security, especially for sensitive data such as personally identifiable information (PII).
Option B is incorrect because even though Databricks databases are logical, they are essential for managing access control and organizing data in a way that makes it easier to find. Option C, while promoting discoverability, exposes too much data to users, which can compromise security. Option D is inaccurate because the default Databricks database is not designed for production use and does not offer any significant security benefits. Option E unnecessarily complicates the architecture by suggesting the creation of numerous databases, which is typically not required unless there is an extremely granular need for data isolation.
In summary, isolating data into separate databases according to its quality tier (as outlined in Option A) is the most effective way to manage large Lakehouse systems, ensuring both secure data access and smooth collaboration across teams.
Question No 3:
A data architect has enforced a policy that all tables within the Lakehouse architecture must be external Delta Lake tables. This ensures that data is stored in a defined location outside of the default managed storage.
As a data engineer tasked with creating new tables in this environment, which of the following actions will ensure compliance with the architect’s directive?
A Ensure that the LOCATION keyword is specified whenever a new database is created.
B Use Databricks for all ELT operations when setting up an external data warehouse.
C Ensure that the LOCATION keyword is specified whenever a new table is created.
D Always include the EXTERNAL keyword in the CREATE TABLE statement.
E Mount external cloud object storage during workspace configuration.
Answer: C
Explanation:
In Delta Lake, there are two types of tables: managed and external. Managed tables are stored in the default storage location managed by the metastore, while external tables are stored at a user-defined location such as cloud object storage.
To create an external table, it is crucial to specify the LOCATION clause during the CREATE TABLE statement. This clause defines the exact path where the data files are located, ensuring that the table is external and does not use the default managed storage location.
For example:
Without the LOCATION clause, the table will be created as a managed table by default, which is not compliant with the architect’s directive.
Let’s look at the other options:
A: Specifying the LOCATION when creating a database does not guarantee that the tables will be external.
B: ELT operations in Databricks do not address the creation of external tables.
D: Delta Lake does not require an EXTERNAL keyword for table creation.
E: Mounting external cloud object storage is preparatory but does not directly ensure external table creation.
Thus, Option C is the correct method to ensure tables are created as external Delta Lake tables.
Question No 4:
To optimize storage and compute costs, the data engineering team is tasked with maintaining a set of aggregate tables. These tables are essential for a range of downstream use cases, including business intelligence dashboards, customer-facing applications, machine learning models, and ad-hoc queries.
The team recently received updated requirements from a customer-facing application, which they manage. These updates require renaming several existing fields and adding new fields to an aggregate table that is shared by other teams within the organization.
Given that:
The customer-facing application is the only workload the team manages,
The table is shared across multiple teams,
The team wants to minimize disruption to other users and avoid creating additional tables unnecessarily,
Which solution best addresses the updated requirements without significantly impacting other teams or increasing the complexity of data management?
A Notify all users of the schema changes and provide logic to revert to the old schema.
B Create a new table with updated fields for the customer-facing app, and a view that preserves the original schema and name by aliasing from the new table.
C Use Delta Lake’s deep clone to sync schema changes across two identical tables.
D Replace the original table with a view containing the original logic and create a new table for the customer app.
E Add a warning comment and overwrite the table in place with the new schema.
Answer: B
Explanation:
This situation presents a common data engineering challenge of managing evolving requirements for one team while ensuring stability for others who rely on the same data.
Option B provides the most efficient and non-disruptive solution. Here’s why:
By creating a new physical table with the updated schema, the team can meet the customer-facing application's new requirements.
A logical view is then created on top of this new table using SQL SELECT statements and aliases. This view will preserve the original table name and schema that other teams are dependent on, ensuring that their existing queries, dashboards, and models continue to work without modification.
This approach minimizes disruption to other teams and avoids creating unnecessary new tables. Only one new view is introduced, which is simpler to manage than duplicating data in new tables for each team.
Example SQL for the view:
Other options are less effective:
A and E involve notifying users or requiring them to adapt, which adds complexity and potential for errors.
C introduces unnecessary complexity by using deep clones.
D unnecessarily alters the original table and introduces risk by replacing it with a view.
Option B ensures minimal disruption, providing a smooth transition for all teams while maintaining only one additional view for the new schema.
Question No 5:
You are working with a Delta Lake table that stores metadata related to user-generated content posts. The table is partitioned by the date column, and a filter query is executed on the longitude column.
How will Delta Lake filter the data for this query?
A Statistics in the Delta Log will be used to identify partitions that might include files in the filtered range.
B No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.
C The Delta Engine will use row-level statistics in the transaction log to identify the files that meet the filter criteria.
D Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.
E The Delta Engine will scan the Parquet file footers to identify each row that meets the filter criteria.
Answer:
D Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.
Explanation
Delta Lake uses a transaction log to track data changes, schema versions, and file-level statistics, all of which help optimize query performance. One of the optimization features is file skipping, which prevents unnecessary file scans during query execution.
Although the table is partitioned by the date column, the filter is applied to the longitude column, which is not part of the partitioning scheme. Therefore, partition pruning cannot be used. Instead, Delta Lake relies on column-level statistics, such as the minimum and maximum values for each column in each data file, to determine which files are relevant to the query filter.
For this scenario, when a filter on longitude (< 20 and > -20) is applied, Delta Lake checks the file-level statistics in the Delta Log. It skips over data files where the longitude value is outside the specified range, reducing the number of files that need to be scanned. This process is known as file-level skipping, and it doesn’t require the filtered column to be part of the partitioning scheme.
Option D is correct because Delta Lake uses the statistics recorded in the Delta Log to determine which data files contain records matching the filter criteria.
Other options are incorrect because they either misunderstand the role of partitioning (A and B), incorrectly refer to row-level statistics (C), or suggest an inefficient approach (E) that doesn’t align with Delta Lake's optimization techniques.
Question No 6:
A small U.S.-based company has partnered with a consulting firm in India to design and implement data engineering pipelines for AI initiatives. The company's data is stored in the cloud within a U.S. region. The workspace administrator is unsure which region to select for deploying the Databricks workspace that the consultants will use.
Which of the following considerations is most accurate for selecting the region?
A Databricks operates on HDFS through cloud volume storage, requiring virtual machines to be in the same region as the data.
B Databricks workspaces are independent of regional infrastructure, so the location should be based on administrator convenience.
C Cross-region data access can result in increased costs and latency; therefore, compute resources should ideally be in the same region as the data.
D Databricks uses users' personal machines as the driver during development, so workspaces should be located near the developers.
E Databricks transmits executable code from users' browsers over the open internet; hence, selecting a region close to end users is most secure.
Answer:
C Cross-region data access can result in increased costs and latency; therefore, compute resources should ideally be in the same region as the data.
Explanation
When deploying a Databricks workspace, the region selection is critical for optimizing both performance and cost, particularly in cloud environments. Databricks utilizes cloud-native services such as cloud storage and virtual machines for computation. If the workspace is placed in a different region than where the data resides, cross-region data transfer is required.
This leads to two main issues:
Increased Latency: Data must travel across longer distances, which can slow down data processing, increase response times, and negatively affect the overall performance of data-intensive tasks.
Higher Costs: Cloud providers often charge for data transfer between regions, which can become expensive, especially when processing large amounts of data.
To avoid these issues, it is recommended to deploy the Databricks workspace in the same region where the data is stored. This ensures that data processing occurs locally, reducing latency and minimizing data transfer costs.
Option C is the most accurate, as it addresses the practical concerns of performance and cost by suggesting that compute resources should be located in the same region as the data.
Other options are incorrect because they either present misleading information about Databricks' operations (A, D) or misunderstand the relationship between workspace location and data access (B, E).
Question No 7:
Which of the following correctly describes a key feature of Delta Lake and its role in the Lakehouse architecture?
A. Since Parquet stores data row-wise, string data can only be compressed if the same character is repeated consecutively.
B. Delta Lake automatically gathers statistics for the first 32 columns of a table, which are used to optimize performance through data skipping during query execution.
C. Views in the Lakehouse always reflect the most recent state of their source tables due to persistent caching.
D. Enforcing primary and foreign key constraints in Delta Lake prevents duplicate records from being inserted into dimension tables.
E. Z-ordering in Delta Lake is restricted to numeric data types only.
Answer: B
Explanation
Delta Lake is an open-source storage layer built on top of Apache Spark and cloud storage, providing ACID transactions, scalable metadata handling, and unifying both streaming and batch data processing. It plays a crucial role in the Lakehouse architecture, which integrates the strengths of both data lakes and data warehouses.
One of the performance enhancements Delta Lake offers is data skipping, which helps to improve query execution. Delta Lake collects statistics on the first 32 columns of each table during data writes. These statistics include the minimum and maximum values of each column within each data file. When a query includes a filter condition (for example, a WHERE clause), Delta Lake can skip over files that don't meet the filter criteria, which helps to reduce I/O and boost query performance. This feature is particularly beneficial when dealing with large datasets.
Now, let's explain why the other options are incorrect:
A is incorrect because Parquet is a columnar storage format, not row-based. This allows for efficient compression, particularly for repeated values within a column, rather than row-based compression as described.
C is incorrect because views in Delta Lake are not cached. They reflect the real-time state of the underlying tables only when queried, and do not retain the latest state persistently.
D is incorrect because while Delta Lake supports some constraints like NOT NULL and CHECK, full enforcement of primary and foreign key constraints, particularly to prevent duplicates, is not available in the same way it would be in traditional relational databases.
E is incorrect because Z-ordering, which is used to optimize data clustering, can be applied to a variety of data types, not just numeric types.
Therefore, B is the correct option, as it accurately describes how Delta Lake helps optimize performance through data skipping in a Lakehouse architecture.
Question No 8:
A DevOps team has set up a production workload that runs a series of notebooks daily via the Databricks Jobs UI. These notebooks contain crucial production logic for the workflow. A new data engineer has joined the team and needs access to one of the notebooks used in the production pipeline to understand the logic and workflow.
To maintain the integrity of the production environment, what permission level should be granted to the engineer to allow them to review the notebook’s content but prevent any accidental changes to the production code or the execution of production workloads?
A. Can Manage
B. Can Edit
C. No Permissions
D. Can Read
E. Can Run
Answer: D
Explanation
In a Databricks environment, managing user access to production notebooks requires a careful approach to security and data integrity. The principle of least privilege should be followed, meaning users should be granted only the level of access necessary for their tasks. In this case, the new data engineer needs to understand the content of the notebook without the risk of modifying the code or accidentally executing it in the production environment.
Here’s a breakdown of each permission level:
Can Manage: This permission provides full control over the notebook, including the ability to edit, run, and manage permissions. This is too broad for a new user who should not have control over production code.
Can Edit: This permission allows the user to make changes to the notebook content. Granting this permission would allow the engineer to modify the production code, which could result in unintended errors or changes.
Can Run: This permission allows the user to execute the notebook, which could trigger production workloads and potentially disrupt the production environment.
Can Read: This permission only allows the user to view the notebook content without the ability to make changes or run the code. This is ideal for providing access to the engineer to understand the workflow without compromising the production system.
No Permissions: This option blocks all access, which would prevent the engineer from reviewing the notebook at all, defeating the purpose of providing them access to understand the logic.
Thus, the most suitable permission level for the engineer is Can Read, as it ensures that they can view the notebook’s content while maintaining the security and integrity of the production environment.
Question No 9:
What is the primary purpose of Delta Lake in a Databricks environment?
A. It provides a scalable data warehouse solution.
B. It enables real-time streaming and batch data processing.
C. It ensures ACID transactions on big data workloads.
D. It optimizes data for machine learning model training.
Explanation:
The primary function of Delta Lake in a Databricks environment is to provide ACID transactions (Atomicity, Consistency, Isolation, Durability) for big data workloads. It allows users to manage both batch and streaming data with the guarantee of data consistency and reliability, which is crucial in ensuring high-quality data in big data environments. Delta Lake is built on top of Apache Spark and integrates tightly with Databricks to support the creation of transactional data lakes.
The other options are not the primary function of Delta Lake. Option A refers to data warehouses, which are distinct from Delta Lake's capabilities. Option B describes streaming and batch data processing, which Delta Lake supports but is not its core purpose. Option D refers to machine learning, which is important in the Databricks environment but is not the primary purpose of Delta Lake.
Question No 10:
Which of the following features in Databricks allows users to version control and track changes in their data pipelines?
A. MLflow
B. Delta Lake
C. Databricks Repos
D. Apache Airflow
Explanation:
Databricks Repos allows users to version control and track changes in their data pipelines. By integrating with Git-based version control systems like GitHub or Bitbucket, Databricks Repos enables users to manage code, notebooks, and workflows in a collaborative and efficient manner. It is a crucial tool for enabling reproducibility and tracking changes in the data engineering lifecycle.
Option A, MLflow, is focused on managing the machine learning lifecycle and experiment tracking, rather than managing data pipelines. Option B, Delta Lake, provides transactional capabilities for big data workloads but does not handle version control for data pipelines. Option D, Apache Airflow, is a popular tool for orchestrating workflows but is not specific to version control within Databricks.
