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Databricks Machine Learning Professional exam dumps: questions 101 to 200 of 279

Page 2 of the free Databricks Machine Learning Professional question bank for the Databricks Certified Machine Learning Professional exam. Questions 101 to 200 are listed below, the first 5 in full with answers and explanations. Back to page 1 for the exam overview and FAQ.

Question bank last updated January 2025

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Free Databricks Machine Learning Professional practice questions

Questions 101 to 105 of 279

Pick an answer before you open the explanation. Each question also has its own page with a permalink.

Databricks Machine Learning Professional Question 101

Select 3

You are working on a machine learning workflow in Databricks where you need to run a batch inference job on a large dataset. You are considering whether to use a Job cluster or an all-purpose cluster. What are the advantages of using a Job cluster for this task?

A
Job clusters automatically terminate after the job is complete, reducing costs.
B
Job clusters provide better runtime performance compared to all-purpose clusters for all workloads.
C
Job clusters are dedicated to specific tasks, ensuring no interference from other workloads.
D
Job clusters allow for reusable cluster configurations for multiple interactive users.
E
Job clusters can be customized for each job, enabling optimized resource utilization.

Show answer and explanation

Correct answers: A, C, E

Explanation

Job clusters in Databricks are designed for single-use, ephemeral workloads like batch processing or scheduled tasks. They offer benefits such as automatic termination, task isolation, and customizable configurations, making them cost-effective and resource-efficient for job-specific tasks. Unlike all-purpose clusters, they are not intended for interactive or multi-user workflows.

A. Correct.
Correct: Job clusters automatically terminate once the job finishes, which helps reduce unnecessary costs by not keeping the cluster running when it is no longer needed.
B. Incorrect.
Incorrect: While Job clusters are optimized for job execution, they do not inherently guarantee better performance for all workloads compared to all-purpose clusters. Performance depends on the workload and cluster configuration.
C. Correct.
Correct: Job clusters are isolated and dedicated to the specific job they are created for, avoiding interference from other workloads, unlike all-purpose clusters which may host multiple concurrent tasks.
D. Incorrect.
Incorrect: Job clusters are typically ephemeral and designed for single-use, meaning they are not intended for reuse across multiple users or sessions. All-purpose clusters are better suited for this purpose.
E. Correct.
Correct: Job clusters can be configured specifically for the requirements of a given job, allowing you to allocate resources optimally and avoid over- or under-provisioning.

Databricks Machine Learning Professional Question 102

Single answer

You are designing a machine learning workflow on Databricks and need a cluster to execute a batch inference job that runs once daily. Cost optimization and automated resource management are key considerations for your team. Why might you choose a Job cluster over an All-purpose cluster for this task?

A
Job clusters automatically shut down after the job is completed, reducing costs.
B
Job clusters are pre-configured with libraries required for most machine learning tasks.
C
Job clusters can be reused across multiple jobs, improving resource utilization.
D
Job clusters provide better scalability compared to All-purpose clusters.

Show answer and explanation

Correct answer: A

Explanation

Job clusters are specifically designed for single-job execution and are automatically terminated once the job is finished, making them an excellent choice for cost optimization in tasks like batch inference jobs. In contrast, All-purpose clusters are designed for long-running, interactive workloads, which can lead to higher costs if not actively managed.

A. Correct.
Job clusters are ephemeral and are automatically terminated after the job execution is complete, which helps minimize costs by avoiding unnecessary resource usage.
B. Incorrect.
This is incorrect because libraries must still be explicitly configured or installed for Job clusters, just like All-purpose clusters.
C. Incorrect.
This is incorrect as Job clusters are designed for single-use and are not intended to be reused across multiple jobs. All-purpose clusters are better suited for shared, interactive workloads.
D. Incorrect.
This is incorrect because both Job clusters and All-purpose clusters can scale similarly, depending on the job requirements and configurations.

Databricks Machine Learning Professional Question 103

Select 3

You are tasked with running a one-time data preprocessing job that involves training a machine learning model in Databricks. The job will run for a few hours and does not require real-time user interaction. Why would you choose a job cluster over an all-purpose cluster for this task?

A
Job clusters are automatically terminated after the job completes, reducing unnecessary cloud costs.
B
Job clusters support higher compute performance compared to all-purpose clusters.
C
Job clusters are specifically configured and optimized for the workload of a single job.
D
Job clusters allow multiple users to collaborate in real time while the job is running.
E
Job clusters are created on demand, ensuring a clean environment free of prior configurations.

Show answer and explanation

Correct answers: A, C, E

Explanation

Job clusters are ideal for one-time or scheduled tasks, such as data preprocessing or training machine learning models, because they are created on demand for a specific job and automatically terminated upon job completion. This reduces costs, ensures an isolated environment, and optimizes the cluster for the specific workload. Unlike all-purpose clusters, job clusters are not designed for real-time collaboration or persistent use.

A. Correct.
Correct: Job clusters are terminated automatically once the job completes, which helps optimize cloud resource usage and reduce costs.
B. Incorrect.
Incorrect: Job clusters do not inherently provide higher compute performance compared to all-purpose clusters; performance is determined by the cluster configuration.
C. Correct.
Correct: Job clusters are designed to handle the workload of a single job and are optimized for that specific job's requirements.
D. Incorrect.
Incorrect: Job clusters do not support real-time collaboration as they are intended for automated, single-job workloads. All-purpose clusters are better suited for this kind of interactive use case.
E. Correct.
Correct: Job clusters are created on demand for each job, ensuring a clean and isolated environment free of prior configurations or interference from other jobs.

Databricks Machine Learning Professional Question 104

Single answer

You are a machine learning engineer working with Databricks, and your team has implemented a CI/CD pipeline for managing MLflow models. You want to automate the retraining of your model whenever it transitions from the 'Staging' stage to the 'Production' stage in MLflow. How can you achieve this in Databricks?

A
Create a Databricks Job with a webhook trigger, configure the webhook to listen for 'model_version_stage_transition' events in MLflow, and write logic to retrain the model.
B
Configure an MLflow Model Registry lifecycle event to automatically retrain the model when a stage transition occurs.
C
Create a Databricks Job with a time-based schedule and include logic to check for model stage transitions in MLflow.
D
Use Databricks AutoML to detect stage transitions and automatically retrain the model.

Show answer and explanation

Correct answer: A

Explanation

To automate tasks in Databricks when a model transitions between stages, a Databricks Job triggered by an MLflow webhook is the appropriate solution. MLflow provides a 'model_version_stage_transition' event that can be used to trigger such Jobs, enabling you to implement custom logic such as retraining the model.

A. Correct.
This is the correct solution. Databricks Jobs can be triggered using webhooks, and MLflow provides a 'model_version_stage_transition' event that can be used to trigger actions like retraining models.
B. Incorrect.
MLflow Model Registry does not have built-in support for automatically executing custom logic (like retraining) during stage transitions. You need to use a Databricks Job for this purpose.
C. Incorrect.
A time-based schedule would not directly respond to stage transitions, making it less efficient and not aligned with the requirements of the scenario.
D. Incorrect.
Databricks AutoML cannot detect MLflow model stage transitions or automate retraining based on these transitions. It is used for initial model development instead.

Databricks Machine Learning Professional Question 105

Single answer

A machine learning team at your organization has deployed a model to the 'Staging' stage in the MLflow Model Registry. They want to automatically trigger a Databricks Job to validate the model whenever it transitions to the 'Production' stage. How can you configure this workflow?

A
Create a Databricks Job and set the model transition event trigger to execute the Job whenever the model transitions to the 'Production' stage.
B
Use the Databricks REST API to poll the MLflow Model Registry for stage transitions and manually trigger the Job when the model reaches the 'Production' stage.
C
Set up an MLflow Webhook that listens for model transition events to the 'Production' stage and triggers the Databricks Job.
D
Manually monitor the MLflow Model Registry and trigger the Databricks Job using the UI when the model transitions to the 'Production' stage.

Show answer and explanation

Correct answer: C

Explanation

To automate the triggering of a Databricks Job when a model transitions to the 'Production' stage, the recommended approach is to use MLflow Webhooks. MLflow Webhooks can listen for specific model registry events (e.g., stage transitions) and trigger external actions, such as running a Databricks Job. This is the most efficient and scalable solution compared to manual monitoring or polling via the API.

A. Incorrect.
Incorrect: Databricks Jobs do not have built-in triggers for MLflow model transition events. You need another mechanism, such as MLflow Webhooks, to achieve this automation.
B. Incorrect.
Incorrect: Polling the MLflow Model Registry using the REST API is inefficient and not ideal for triggering Jobs. It is better to use event-driven mechanisms such as Webhooks.
C. Correct.
Correct: MLflow Webhooks can listen for model transition events, such as transitioning to the 'Production' stage, and trigger external actions like Databricks Jobs.
D. Incorrect.
Incorrect: Manually monitoring the MLflow Model Registry is not automated and does not meet the requirement of triggering the Job automatically.

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Databricks Machine Learning Professional practice questions 101 to 200 of 279

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