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The Databricks Certified Data Engineer Associate Exam certification exam tests the candidate's knowledge of Databricks, Apache Spark, and data engineering concepts. It covers various topics such as data ingestion, data processing, data transformation, and data storage. Databricks-Certified-Data-Engineer-Associate Exam also evaluates the candidate's ability to design and implement data pipelines using Databricks, as well as their knowledge of best practices for data management and governance.

Databricks Certified Data Engineer Associate certification exam is a valuable credential for data engineers who work with the Databricks platform. It demonstrates their mastery of data engineering concepts and their practical application using Databricks. Candidates who pass the exam receive a certificate that can help them advance their careers and gain recognition for their expertise.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q94-Q99):

NEW QUESTION # 94
A data engineering team is analyzing customer transactions using a PySpark DataFrame named transactions with columns customer_id, amount, and category.
The team needs to compute, for each customer, the total transaction amount, the average transaction amount, and the maximum amount - all in a single operation for efficiency.
Which code fragment should be used to achieve this in PySpark?

Answer: D

Explanation:
Grouping by customer_id and applying multiple aggregate functions inside a single agg() call is the correct PySpark pattern for computing total, average, and maximum values per customer in one operation.


NEW QUESTION # 95
A data engineer wants to read only a subset of columns from a Delta table to reduce memory usage and improve performance. Which Spark DataFrame operation should be used?

Answer: D


NEW QUESTION # 96
Which of the following SQL keywords can be used to convert a table from a long format to a wide format?

Answer: A

Explanation:
The SQL keyword that can be used to convert a table from a long format to a wide format is PIVOT. The PIVOT clause is used to rotate the rows of a table into columns of a new table1. The PIVOT clause can aggregate the values of a column based on the distinct values of another column, and use those values as the column names of the new table1. The PIVOT clause can be useful for transforming data from a long format, where each row represents an observation with multiple attributes, to a wide format, where each row represents an observation with a single attribute and multiple values2. For example, the PIVOT clause can be used to convert a table that contains the sales of different products by different regions into a table that contains the sales of each product by each region as separate columns1.
The other options are not suitable for converting a table from a long format to a wide format. CONVERT is a function that can be used to change the data type of an expression3. WHERE is a clause that can be used to filter the rows of a table based on a condition4. TRANSFORM is a keyword that can be used to apply a user- defined function to a group of rows in a table5. SUM is a function that can be used to calculate the total of a numeric column.
1: PIVOT | Databricks on AWS
2: Reshaping Data - Long vs Wide Format | Databricks on AWS
3: CONVERT | Databricks on AWS
4: WHERE | Databricks on AWS
5: TRANSFORM | Databricks on AWS
[SUM | Databricks on AWS]


NEW QUESTION # 97
An organization has implemented a data pipeline in Databricks and needs to ensure it can scale automatically based on varying workloads without manual cluster management. The goal is to meet the company's Service Level Agreements (SLAs), which require high availability and minimal downtime, while Databricks automatically handles resource allocation and optimization.
Which approach fulfills these requirements?

Answer: A

Explanation:
Serverless compute in Databricks automatically provisions and scales resources to meet workload demands, ensuring high availability and minimal downtime while reducing the need for manual cluster management, which aligns with SLA requirements.


NEW QUESTION # 98
A data engineer has a Job with multiple tasks that runs nightly. Each of the tasks runs slowly because the clusters take a long time to start.
Which of the following actions can the data engineer perform to improve the start up time for the clusters used for the Job?

Answer: E

Explanation:
Explanation
Cluster pools are a way to pre-provision clusters that are ready to use. This can reduce the start up time for clusters, as they do not have to be created from scratch. All-purpose clusters are not pre-provisioned, so they will take longer to start up. Jobs clusters are a type of cluster pool, but they are not the best option for this use case. Jobs clusters are designed for long-running jobs, and they can be more expensive than other types of cluster pools. Single-node clusters are the smallest type of cluster, and they will start up the fastest. However, they may not be powerful enough to run the Job's tasks. Autoscaling clusters can scale up or down based on demand. This can help to improve the start up time for clusters, as they will only be created when they are needed. However, autoscaling clusters can also be more expensive than other types of cluster pool
https://docs.databricks.com/en/clusters/pool-best-practices.html


NEW QUESTION # 99
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