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Databricks Databricks-Machine-Learning-Professional Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Machine Learning Professional
Exam Number:	Databricks-Machine-Learning-Professional
Exam Price:	USD 200
Exam Duration:	120 minutes
Related Certifications:	Databricks Certified Machine Learning Associate
Certificate Validity Period:	2 years
Exam Format:	Multiple choice
Real Exam Qty:	60
Passing Score:	70%
Available Languages:	English
Sample Questions:	Databricks Databricks-Machine-Learning-Professional Sample Questions
Exam Way:	Online (proctored) or Test Center
Pre Condition:	No formal prerequisites, but 1+ years of hands-on experience performing the machine learning tasks outlined in the exam guide is highly recommended. Recommended courses: Machine Learning at Scale and Advanced Machine Learning Operations (instructor-led or self-paced via Databricks Academy).
Official Syllabus URL:	https://www.databricks.com/learn/certification/machine-learning-professional

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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	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment
Topic 3	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 4	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 5	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 6	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
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 the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 9	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook

Databricks Certified Machine Learning Professional Sample Questions (Q90-Q95):

NEW QUESTION # 90
A Data Scientist at an online gaming company is creating a model to predict player churn. The company currently collects terabytes of player activity logs daily, which are stored in Databricks and processed for daily reporting. The Data Scientist has completed feature engineering and the resulting data is saved as a Delta Table with a size of 500GB. They need to next build the model for the most performant and cost-effective performance for Databricks. Which approach will do this?

A. Load the feature data as a pandas DataFrame and train the model using scikit-learn's RandomForestClassifier on a single-node Databricks cluster.
B. Load the feature data as a Spark DataFrame and train the model using Spark's DeepspeedTorchDistributor on a multi-node Databricks cluster.
C. Load the feature data as a Spark DataFrame and train the model using SparkML's RandomForestClassifier on a multi-node Databricks cluster.
D. Load the feature data as a pandas DataFrame and train the model using scikit-learn's RandomForestClassifier on a multi-node Databricks cluster.

Answer: C

Explanation:
A 500GB Delta Table is far beyond what is practical to load into a single pandas DataFrame, and scaling pandas-based scikit-learn training across nodes is not the right fit for this workload. Using a Spark DataFrame with Spark ML's RandomForestClassifier leverages distributed data processing and distributed model training on a multi-node cluster, which is the most performant and cost-effective approach for large tabular datasets in Databricks.

NEW QUESTION # 91
A machine learning engineer wants to view all of the active MLflow Model Registry Webhooks for a specific model.
They are using the following code block:

Which of the following changes does the machine learning engineer need to make to this code block so it will successfully accomplish the task?

A. Replace list with webhooks in the endpoint URL
B. Replace list with view in the endpoint URL
C. There are no necessary changes
D. Replace POST with GET in the call to http request
E. Replace POST with PUT in the call to http request

Answer: D

NEW QUESTION # 92
Which of the following is a simple, low-cost method of monitoring numeric feature drift?

A. Chi-squared test
B. Summary statistics trends
C. Kolmogorov-Smirnov (KS) test
D. None of these can be used to monitor feature drift
E. Jensen-Shannon test

Answer: B

NEW QUESTION # 93
A machine learning engineer has registered a Spark ML model in the MLflow Model Registry using the Spark ML model flavor with UI model_uri. Which operation can be used to load the model as a Spark ML object for batch deployment?

A. mlflow.spark.read_model(model_uri)
B. mlflow.spark.load_model(model_uri)
C. mlflow.pyfunc.load_model(model_uri)
D. mlflow.sklearn.load_model(model_uri)

Answer: B

Explanation:
To load a model that was logged using the Spark ML flavor, the correct operation is mlflow.spark.load_model(model_uri). This restores the model as a Spark ML object, preserving its original structure and functionality for batch inference or further processing in Spark environments.

NEW QUESTION # 94
Which is a benefit of logging an input example with an MLflow model?

A. The environment requirements are packaged with the MLmodel file
B. The model evaluation metrics are stored with the model
C. An example record is available to test serving applications
D. An example prediction is available to test post-inference pipelines

Answer: C

Explanation:
Logging an input example with an MLflow model provides a concrete sample of the data format expected by the model. This helps serving applications and users understand the model's input structure and verify that inference requests are properly formatted when deploying or testing the model in production.

NEW QUESTION # 95
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