Latest Databricks-Machine-Learning-Professional Test Practice - Databricks-Machine-Learning-Professional Free Exam Questions

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

TopicDetails
Topic 1
  • Identify which code block will trigger a shown webhook
  • Describe the basic purpose and user interactions with Model Registry
Topic 2
  • Identify less performant data storage as a solution for other use cases
  • Describe why complex business logic must be handled in streaming deployments
Topic 3
  • 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
Topic 4
  • Describe concept drift and its impact on model efficacy
  • Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 5
  • Create, overwrite, merge, and read Feature Store tables in machine learning workflows
  • View Delta table history and load a previous version of a Delta table
Topic 6
  • Identify JIT feature values as a need for real-time deployment
  • Describe how to list all webhooks and how to delete a webhook
Topic 7
  • Identify live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines
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
  • Identify the requirements for tracking nested runs
  • Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 10
  • 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

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Databricks Certified Machine Learning Professional Sample Questions (Q74-Q79):

NEW QUESTION # 74
Which of the following lists all of the model stages are available in the MLflow Model Registry?

Answer: D


NEW QUESTION # 75
A machine learning engineer is working on a fraud detection machine learning application. When a transaction is made with a credit card, the machine learning application will immediately process the data and make a prediction to determine whether or not to approve the transaction based on the probability that the transaction is fraudulent. Which deployment strategy can be used to meet these requirements?

Answer: C

Explanation:
Real-time deployment is required for applications that must provide immediate predictions, such as fraud detection during a credit card transaction. This ensures that the system can instantly assess risk and approve or deny the transaction without delay.


NEW QUESTION # 76
A data scientist would like to enable MLflow Autologging for all machine learning libraries used in a notebook. They want to ensure that MLflow Autologging is used no matter what version of the Databricks Runtime for Machine Learning is used to run the notebook and no matter what workspace-wide configurations are selected in the Admin Console. Which of the following lines of code can they use to accomplish this task?

Answer: E


NEW QUESTION # 77
A Machine Learning Engineer is setting up Databricks Lakehouse Monitoring to issue alerts based on IoT sensors that monitor various water conditions at a high tech hydroponic farm.
Specifically:
- pH sensors
- Records acidity/alkalinity on a logarithmic scale from 0 to 14
- Pump status monitor
- Records the current status of the pump as "on", "off", "maintenance",
"fault", or "clean"
- Electrical Conductivity (EC) sensors
- Records nutrient level of the water as "very rich", "rich", "medium",
"poor", "very poor"
What are the appropriate drift metrics for each of the given sensor types?

Answer: C

Explanation:
pH readings are continuous numerical values, so the Kolmogorov-Smirnov test is appropriate for detecting distribution shifts. Pump status is a nominal categorical variable, making the chi- squared test suitable for identifying changes in category frequencies. Electrical Conductivity levels are categorical with ordered labels, and Jensen-Shannon distance effectively measures distributional changes in such categorical probability distributions.


NEW QUESTION # 78
What does an Estimator do in Spark ML?

Answer: A

Explanation:
An Estimator learns from data and produces a Transformer.


NEW QUESTION # 79
......

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