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Free PDF Databricks - Databricks-Certified-Data-Engineer-Associate - Databricks Certified Data Engineer Associate Exam–Efficient Certificate Exam

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q101-Q106):

NEW QUESTION # 101
Which of the following must be specified when creating a new Delta Live Tables pipeline?

A. A key-value pair configuration
B. At least one notebook library to be executed
C. The preferred DBU/hour cost
D. A path to cloud storage location for the written data
E. A location of a target database for the written data

Answer: B

Explanation:
Option E is the correct answer because it is the only mandatory requirement when creating a new Delta Live Tables pipeline. A pipeline is a data processing workflow that contains materialized views and streaming tables declared in Python or SQL source files. Delta Live Tables infers the dependencies between these tables and ensures updates occur in the correct order. To create a pipeline, you need to specify at least one notebook library to be executed, which contains the Delta Live Tables syntax. You can also specify multiple libraries of different languages within your pipeline. The other options are optional or not applicable for creating a pipeline. Option A is not required, but you can optionally provide a key-value pair configuration to customize the pipeline settings, such as the storage location, the target schema, the notifications, and the pipeline mode.
Option B is not applicable, as the DBU/hour cost is determined by the cluster configuration, not the pipeline creation. Option C is not required, but you can optionally specify a storage location for the output data from the pipeline. If you leave it empty, the system uses a default location. Option D is not required, but you can optionally specify a location of a target database for the written data, either in the Hive metastore or the Unity Catalog.
References: Tutorial: Run your first Delta Live Tables pipeline, What is Delta Live Tables?, Create a pipeline, Pipeline configuration.

NEW QUESTION # 102
Which of the following describes the type of workloads that are always compatible with Auto Loader?

A. Dashboard workloads
B. Machine learning workloads
C. Streaming workloads
D. Serverless workloads
E. Batch workloads

Answer: C

Explanation:
Explanation
Auto Loader is a feature of Databricks that simplifies and automates the process of loading streaming data into Delta Lake tables. Auto Loader can detect new and updated files in cloud storage and efficiently load them as micro-batches or as a continuous stream. Auto Loader is always compatible with streaming workloads, as it is designed to handle streaming sources such as Amazon S3, Azure Data Lake Storage Gen2, and Azure Blob Storage. The other types of workloads may or may not be compatible with Auto Loader, depending on the data source and the use case. References: The information can be referenced from Databricks documentation on Auto Loader: Auto Loader.
https://community.databricks.com/t5/data-engineering/practice-exams-for-databricks-certified-data-engineer/td-p

NEW QUESTION # 103
A data engineer needs to determine whether to use the built-in Databricks Notebooks versioning or version their project using Databricks Repos.
Which of the following is an advantage of using Databricks Repos over the Databricks Notebooks versioning?

A. Databricks Repos is wholly housed within the Databricks Lakehouse Platform
B. Databricks Repos supports the use of multiple branches
C. Databricks Repos allows users to revert to previous versions of a notebook
D. Databricks Repos provides the ability to comment on specific changes
E. Databricks Repos automatically saves development progress

Answer: B

NEW QUESTION # 104
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 use jobs clusters instead of all-purpose clusters
D. They can use clusters that are from a cluster pool
E. They can configure the clusters to autoscale for larger data sizes

Answer: D

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.
References:
* Cluster Pools
* Jobs
* Clusters
* [Databricks Data Engineer Professional Exam Guide]

NEW QUESTION # 105
An engineering manager wants to monitor the performance of a recent project using a Databricks SQL query. For the first week following the project's release, the manager wants the query results to be updated every minute. However, the manager is concerned that the compute resources used for the query will be left running and cost the organization a lot of money beyond the first week of the project's release.
Which of the following approaches can the engineering team use to ensure the query does not cost the organization any money beyond the first week of the project's release?

A. They can set the query's refresh schedule to end after a certain number of refreshes.
B. They can set a limit to the number of individuals that are able to manage the query's refresh schedule.
C. They can set the query's refresh schedule to end on a certain date in the query scheduler.
D. They can set a limit to the number of DBUs that are consumed by the SQL Endpoint.
E. They cannot ensure the query does not cost the organization money beyond the first week of the project's release.

Answer: C

Explanation:
In Databricks SQL, you can use scheduled query executions to update your dashboards or enable routine alerts. By default, your queries do not have a schedule. To set the schedule, you can use the dropdown pickers to specify the frequency, period, starting time, and time zone. You can also choose to end the schedule on a certain date by selecting the End date checkbox and picking a date from the calendar. This way, you can ensure that the query does not run beyond the first week of the project's release and does not incur any additional cost. Option A is incorrect, as setting a limit to the number of DBUs does not stop the query from running. Option B is incorrect, as there is no option to end the schedule after a certain number of refreshes. Option C is incorrect, as there is a way to ensure the query does not cost the organization money beyond the first week of the project's release. Option D is incorrect, as setting a limit to the number of individuals who can manage the query's refresh schedule does not affect the query's execution or cost. Reference: Schedule a query, Schedule a query - Azure Databricks - Databricks SQL

NEW QUESTION # 106
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