Pass Databricks Certified Data Engineer Associate in 3 Days

Pass Databricks Certified Data Engineer Associate in 3 Days free download

Databricks Certified Data Engineer Associate | Real Exam Questions | Spark SQL | Delta Lake | Data Pipelines | 2025 July

Easily Pass Databricks Certified Data Engineer Exam in 3 Days (updated Jun 2025 with latest questions)

Free Sample Question 1 out of 3:

The Data Platform team at CloudScale Analytics requires full access to the `customers` database to effectively manage a newly assigned ELT project.  Which of the following commands can be used to grant full permissions on the database to the Data Platform team?

A. GRANT USAGE ON DATABASE customers TO team;

B. GRANT ALL PRIVILEGES ON DATABASE team TO customers;

C. GRANT SELECT PRIVILEGES ON DATABASE customers TO teams;

D. GRANT SELECT CREATE MODIFY USAGE PRIVILEGES ON DATABASE customers TO team;

E. GRANT ALL PRIVILEGES ON DATABASE customers TO team;

Correct Answer: E

Explanation:

The correct command to grant full privileges on the database `customers` to the team is `GRANT ALL PRIVILEGES ON DATABASE customers TO team;`. `ALL PRIVILEGES` encompasses all possible permissions, allowing the team to fully manage the database.  Option A only grants the `USAGE` privilege, which is insufficient for full management. `USAGE` allows the grantee to connect to the database but does not grant the ability to create, modify, or select data.  Option B has the arguments reversed and would attempt to grant permissions from the `team` database to the `customers` database, which is incorrect.  Option C grants only `SELECT` privileges, which is not enough for full management. The team would not be able to create, modify, or delete data. Also, the question states "team" not "teams".  Option D, `GRANT SELECT CREATE MODIFY USAGE PRIVILEGES ON DATABASE customers TO team;`, while granting a broader range of privileges than options A and C, it still doesn't cover all privileges (e.g., `DROP`, `ALTER`, etc.). Using `ALL PRIVILEGES` ensures that the team has complete control.

Free Sample Question 2 out of 3:

The Data Engineering team at InnovaTech is evaluating the Databricks Lakehouse Platform for their new data pipeline; what advantage does the platform's use of open source technologies offer InnovaTech?

A. Cloud-specific integrations

B. Simplified governance

C. Ability to scale storage

D. Ability to scale workloads

E. Avoiding vendor lock-in

Correct Answer: E

Explanation:

Vendor lock-in refers to the situation where a customer becomes dependent on a specific vendor for products or services, making it difficult or costly to switch to another vendor. By embracing open-source technologies, the Databricks Lakehouse Platform allows users to avoid being locked into a single vendor's ecosystem because open standards and formats enable interoperability and portability across different systems and platforms. This gives users the flexibility to choose the tools and services that best meet their needs, without being constrained by proprietary technologies or vendor-specific limitations.

Free Sample Question 3 out of 3:

At Apex Analytics, the BI Engineering team is deciding on cluster configurations. Which of the following describes a scenario where a data engineer would choose to use a single-node cluster?

A. When they are working interactively with a small amount of data

B. When they are running automated reports to be refreshed as quickly as possible

C. When they are working with SQL within Databricks SQL

D. When they are concerned about the ability to automatically scale with larger data

E. When they are manually running reports with a large amount of data

Correct Answer: A

Explanation:

A single-node cluster consists of an Apache Spark driver and no Spark workers. This configuration is ideal for lightweight, interactive tasks and working with small amounts of data because it avoids the overhead of distributing computations across multiple nodes.  Let's evaluate the options:

*   A. When they are working interactively with a small amount of data: This is the primary use case for a single-node cluster. It's cost-effective for development, testing, and exploratory data analysis where the dataset is small enough to fit within the memory and processing capabilities of a single machine.

*   B. When they are running automated reports to be refreshed as quickly as possible: Automated reports, especially those needing quick refresh times, typically involve larger datasets and demand high performance. This scenario would benefit from a standard (multi-node) cluster to leverage distributed processing and parallelism, which a single-node cluster cannot provide.

*   C. When they are working with SQL within Databricks SQL: While you can run SQL on any Databricks cluster, Databricks SQL provides specialized SQL Warehouses (formerly SQL Endpoints) that are optimized for SQL workloads, offering instant compute and often employing multi-node configurations for performance and concurrency. A single-node cluster is not the primary or most efficient choice for general Databricks SQL workloads.

*   D. When they are concerned about the ability to automatically scale with larger data: Single-node clusters do *not* scale automatically with larger data. They are fixed-size and suitable only for data that fits on a single machine. For scalability with larger data, a standard (multi-node) cluster or Databricks SQL Warehouse is required.

*   E. When they are manually running reports with a large amount of data: Large amounts of data necessitate distributed processing to handle memory requirements and computational complexity efficiently. A single-node cluster would likely run out of memory or take an excessively long time to process large datasets.  Therefore, the most appropriate scenario for using a single-node cluster is interactive work with small amounts of data.

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