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

Section	Objectives
Topic 1: MLflow and Experiment Tracking	- Model registry 1. Versioning and lifecycle management - Experiment management 1. Tracking runs and parameters 2. Model comparison and selection
Topic 2: Machine Learning Models and Algorithms	- Unsupervised learning methods 1. Clustering techniques 2. Dimensionality reduction - Supervised learning methods 1. Model evaluation metrics 2. Classification and regression models
Topic 3: Machine Learning Workflow on Databricks	- End-to-end ML pipelines 1. Reusable ML workflows 2. Pipeline construction and orchestration - Data preparation and feature engineering 1. Feature engineering techniques 2. Data ingestion and cleaning in Databricks
Topic 4: Model Deployment and Serving	- Model deployment strategies 1. Batch inference vs real-time inference 2. Databricks Model Serving
Topic 5: MLOps, Monitoring, and Governance	- Governance and compliance 1. Feature Store usage and management 2. Model lifecycle governance - Model monitoring 1. Performance tracking in production 2. Data drift detection

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Databricks Certified Machine Learning Professional Sample Questions (Q85-Q90):

NEW QUESTION # 85
A machine learning engineering manager has asked all of the engineers on their team to add text descriptions to each of the model projects in the MLflow Model Registry. They are starting with the model project "model" and they'd like to add the text in the model_description variable.
The team is using the following line of code:

Which of the following changes does the team need to make to the above code block to accomplish the task?

A. Replace description with artifact
B. Replace update_registered_model with update_model_version
C. Replace client.update_registered_model with mlflow
D. Add a Python model as an argument to update_registered_model
E. There no changes necessary

Answer: E

NEW QUESTION # 86
A data scientist is using MLflow to track their machine learning experiment. As a part of each MLflow run, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values.
They are using the following code block:

The code block is not nesting the runs in MLflow as they expected.
Which of the following changes does the data scientist need to make to the above code block so that it successfully nests the child runs under the parent run in MLflow?

A. Indent the child run blocks within the parent run block
B. Add the nested=True argument to the parent run
C. Add the nested=True argument to the parent run and remove the nested=True arguments from the child runs
D. Provide the same name to the run name parameter for all three run blocks
E. Remove the nested=True argument from the child runs

Answer: C

NEW QUESTION # 87
A machine learning engineer is in the process of implementing a concept drift monitoring solution. They are planning to use the following steps:
1. Deploy a model to production and compute predicted values
2. Obtain the observed (actual) label values
3. _____
4. Run a statistical test to determine if there are changes over time
Which of the following should be completed as Step #3?

A. Obtain the observed values (actual) feature values
B. None of these should be completed as Step #3
C. Compute the evaluation metric using the observed and predicted values
D. Measure the latency of the prediction time
E. Retrain the model

Answer: B

NEW QUESTION # 88
A data scientist would like to switch from manually using MLflow logging to MLflow Autologging for all machine learning libraries used in a notebook.
They begin by adding mlflow.autolog()to the top of the below code block:

The data scientist is now trying to determine which line of code will kick off the MLflow Autologging process.
Which line of code within the above code block will start the MLflow Autologging process?

A. mlflow.sklearn.log_model(rf_model, "rf_model")
B. rf_model = rf.fit(X_train, y_train)
C. with mlflow.start_run(run_name="ML Project") as run:
D. mlflow.log_metric("training_mse", mse)

Answer: B

Explanation:
The MLflow Autologging process is triggered when a model's training function is called. In this case, rf.fit(X_train, y_train) starts the autologging process because mlflow.autolog() automatically tracks parameters, metrics, and the model when a supported estimator (like RandomForestRegressor) is fitted. The context manager mlflow.start_run() only initiates an MLflow run, but the actual autologging begins during the .fit() execution.

NEW QUESTION # 89
Which of the following deployment paradigms can centrally compute predictions for a single record with exceedingly fast results?

A. Batch
B. Edge/on-device
C. Streaming
D. None of these strategies will accomplish the task.
E. Real-time

Answer: C

NEW QUESTION # 90
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