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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:

Topic	Details
Topic 1	Identify a use case for HTTP webhooks and where the Webhook URL needs to come Identify advantages of using Job clusters over all-purpose clusters
Topic 2	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 3	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 4	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 5	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 6	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 7	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 8	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 9	Test whether the updated model performs better on the more recent data Identify when retraining and deploying an updated model is a probable solution to drift

>> New Databricks-Machine-Learning-Professional Exam Topics <<

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Databricks Certified Machine Learning Professional Sample Questions (Q50-Q55):

NEW QUESTION # 50
A machine learning engineer and data scientist are working together to convert a batch deployment to an always-on streaming deployment. The machine learning engineer has expressed that rigorous data tests must be put in place as a part of their conversion to account for potential changes in data formats. Which of the following describes why these types of data type tests and checks are particularly important for streaming deployments?

A. None of these statements
B. Because the streaming deployment is always on, there is no practitioner to debug poor model performance
C. All of these statements
D. Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale
E. Because the streaming deployment is always on, all types of data must be handled without producing an error

Answer: E

NEW QUESTION # 51
Which of the following lists all of the model stages are available in the MLflow Model Registry?

A. Development. Staging. Production. Archived
B. Development. Staging. Production
C. None. Staging. Production
D. None. Staging. Production. Archived
E. Staging. Production. Archived

Answer: D

NEW QUESTION # 52
A data scientist set up a machine learning pipeline to automatically log a data visualization with each run. They now want to view the visualizations in Databricks.
Which of the following locations in Databricks will show these data visualizations?

A. The Artifacts section of the MLflow Experiment page
B. The MLflow Model Registry Model paqe
C. The Figures section of the MLflow Run page
D. Logged data visualizations cannot be viewed in Databricks
E. The Artifacts section of the MLflow Run page

Answer: C

NEW QUESTION # 53
In a Spark ML Pipeline, what is the role of a Transformer?

A. Evaluates models
B. Trains models
C. Converts datasets into new datasets
D. Fits data

Answer: C

Explanation:
A Transformer takes a dataset and transforms it into another dataset.

NEW QUESTION # 54
A Data Scientist is building a propensity model for an e-commerce start-up. The company maintains 7GB of historical data and receives about 5MB of new transaction data daily. The goal is to generate daily purchase predictions for all users by 7:00 AM each morning. As the start-up is in its early stages, the data scientist must prioritize a highly cost-efficient approach. Which approach should the Data Scientist take?

A. Use a single-node compute to build a model with libraries like scikit-learn. The model can then be integrated into an always-on streaming pipeline, to ensure immediate processing of incoming data in order to meet the SLA.
B. Use a multi-node compute and leverage distributed frameworks like SparkML to train the model.
The model can then be scheduled as a nightly batch job that runs on a multi-node compute.
C. Use a single-node compute to build a model with libraries like scikit-learn. The model can then be deployed as an always-on REST API so that users can query the API to get the predictions whenever they want.
D. Use a single-node memory compute to build a model with libraries like scikit-learn. The model can then be scheduled as a nightly batch job that runs on a single-node compute.

Answer: D

Explanation:
With only 7GB of historical data and a small daily increment (about 5MB), a single-node memory- optimized cluster can comfortably train and score using scikit-learn without the overhead and cost of distributed compute. Scheduling a nightly batch job is the most cost-efficient way to meet a fixed daily SLA (7:00 AM) because the compute can be started only for the job run and then terminated, avoiding the expense of always-on serving or streaming infrastructure.

NEW QUESTION # 55
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