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Databricks Certified Data Engineer Associate Exam Sample Questions (Q148-Q153):

NEW QUESTION # 148
Which of the following data lakehouse features results in improved data quality over a traditional data lake?

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

Answer: D

Explanation:
Explanation
One of the key features of a data lakehouse that results in improved data quality over a traditional data lake is its support for ACID (Atomicity, Consistency, Isolation, Durability) transactions. ACID transactions provide data integrity and consistency guarantees, ensuring that operations on the data are reliable and that data is not left in an inconsistent state due to failures or concurrent access. In a traditional data lake, such transactional guarantees are often lacking, making it challenging to maintain data quality, especially in scenarios involving multiple data writes, updates, or complex transformations. A data lakehouse, by offering ACID compliance, helps maintain data quality by providing strong consistency and reliability, which is crucial for data pipelines and analytics.

NEW QUESTION # 149
A data engineer is running code in a Databricks Repo that is cloned from a central Git repository. A colleague of the data engineer informs them that changes have been made and synced to the central Git repository. The data engineer now needs to sync their Databricks Repo to get the changes from the central Git repository.
Which of the following Git operations does the data engineer need to run to accomplish this task?

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

Answer: B

Explanation:
To sync a Databricks Repo with the changes from a central Git repository, the data engineer needs to run the Git pull operation. This operation fetches the latest updates from the remote repository and merges them with the local repository. The data engineer can use the Pull button in the Databricks Repos UI, or use the git pull command in a terminal session. The other options are not relevant for this task, as they either push changes to the remote repository (Push), combine two branches (Merge), save changes to the local repository (Commit), or create a new local repository from a remote one (Clone). Reference:
Run Git operations on Databricks Repos
Git pull

NEW QUESTION # 150
Which of the following code blocks will remove the rows where the value in column age is greater than 25 from the existing Delta table my_table and save the updated table?

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

Answer: E

Explanation:
1: The DELETE command in Delta Lake allows you to remove data that matches a predicate from a Delta table. This command will delete all the rows where the value in the column age is greater than 25 from the existing Delta table my_table and save the updated table. The other options are either incorrect or do not achieve the desired result. Option A will only select the rows that match the predicate, but not delete them. Option B will update the rows that match the predicate, but not delete them. Option D will update the rows that do not match the predicate, but not delete them. Option E will delete the rows that do not match the predicate, which is the opposite of what we want. Reference: Table deletes, updates, and merges - Delta Lake Documentation

NEW QUESTION # 151
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?

A. They can use endpoints available in Databricks SQL
B. They can configure the clusters to be single-node
C. They can configure the clusters to autoscale for larger data sizes
D. They can use jobs clusters instead of all-purpose clusters
E. They can use clusters that are from a cluster pool

Answer: E

Explanation:
The best action that the data engineer can perform to improve the start up time for the clusters used for the Job is to use clusters that are from a cluster pool. A cluster pool is a set of idle clusters that can be used by jobs or interactive sessions. By using a cluster pool, the data engineer can avoid the cluster creation time and reduce the latency of the tasks. Cluster pools also offer cost savings and resource efficiency, as they can be shared by multiple users and jobs.
Option A is not relevant, as endpoints available in Databricks SQL are used for creating and managing SQL analytics workloads, not for improving cluster start up time.
Option B is not correct, as jobs clusters and all-purpose clusters have similar start up times. Jobs clusters are clusters that are dedicated to run a single job and are terminated when the job is completed. All-purpose clusters are clusters that can be used for multiple purposes, such as interactive sessions, notebooks, or multiple jobs. Both types of clusters can benefit from using a cluster pool.
Option C is not advisable, as configuring the clusters to be single-node will reduce the parallelism and performance of the tasks. Single-node clusters are clusters that have only one worker node and are typically used for testing or development purposes. They are not suitable for running production jobs that require high scalability and fault tolerance.
Option E is not helpful, as configuring the clusters to autoscale for larger data sizes will not affect the start up time of the clusters. Autoscaling is a feature that allows clusters to dynamically adjust the number of worker nodes based on the workload. It can help optimize the resource utilization and cost efficiency of the clusters, but it does not speed up the cluster creation process.
Reference:
Cluster Pools
Jobs
Clusters
[Databricks Data Engineer Professional Exam Guide]

NEW QUESTION # 152

Calculate the total sales amount for each region and store the results in a new dataframe called region_sales.
Given the expected result:

Which code will generate the expected result?

A. region_sales = sales_df. sum ( " salen_aiTiount " ) . groupBy ( " region " ) .alias ( " total_sale3_amount
" )
B. region sales - sales_df.agg(sum( " sales_amount " ).groupBy( " region " ).alias( " total sales amount " ))
C. region_sales= sales_df.groupBy( " category " ).sum(nsales_amount " ).alias( " t_otal_sales_amounl " )
D. region_sales = sales_df.groupBy( " region " ).agg(sum( " sales_amountM).alias( " total_sales_amount " ))

Answer: D

NEW QUESTION # 153
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