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

Section	Weight	Objectives
Topic 1: Model Development	44%	- Distributed training and hyperparameter tuning - Advanced MLflow usage - Scalable ML pipelines with SparkML - Feature Store and automated feature pipelines
Topic 2: ML Ops	44%	- Automated retraining workflows - Testing and validation strategies - Environment management with Databricks Asset Bundles - Model monitoring and drift detection with Lakehouse Monitoring
Topic 3: Model Deployment	12%	- Deployment strategies - Model rollout and version management - Custom model serving

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Databricks Certified Machine Learning Professional Sample Questions (Q59-Q64):

NEW QUESTION # 59
A data scientist has written a function to track the runs of their random forest model. The data scientist is changing the number of trees in the forest across each run. Which of the following MLflow operations is designed to log single values like the number of trees in a random forest?

A. mlflow.log_artifact
B. mlflow.log_metric
C. mlflow.log_param
D. mlflow.log_model
E. There is no way to store values like this.

Answer: B

NEW QUESTION # 60
A Machine Learning Engineer is using Lakehouse Monitoring to track the performance of ML models deployed in their environment. They want to monitor significant distributional drift in categorical features with a metric bounded on [0,1] for easy interpretation. Which statistical method should they use?

A. Kullback-Leibler Distance
B. Jensen-Shannon Distance
C. Kolmogorov-Smirnov Test
D. Wasserstein Distance

Answer: B

Explanation:
Jensen-Shannon Distance is well suited for measuring distributional drift in categorical features. It is symmetric, numerically stable, and bounded between 0 and 1, which makes it easy to interpret and ideal for monitoring categorical feature drift in Lakehouse Monitoring.

NEW QUESTION # 61
A data scientist has developed a model model and computed the RMSE of the model on the test set. They have assigned this value to the variable rmse. They now want to manually store the RMSE value with the MLflow run.
They write the following incomplete code block:

Which of the following lines of code can be used to fill in the blank so the code block can successfully complete the task?

A. log_artifact
B. log_model
C. log_param
D. log_metric
E. There is no way to store values like this.

Answer: A

NEW QUESTION # 62
A Machine Learning Engineer is setting up a cluster for a deep learning training run, but has a number of settings options to choose from. Their cluster will be reused by other engineers on their team and their datasets vary in size from hundreds of MBs to hundreds of GBs. They need to choose a configuration that allows for performant, stable deep learning training without excessive costs. Which configuration will do this?

A. Using ML Runtime, use a moderately sized CPU VM for the driver and a variable number of GPU enabled VMs for the workers with autoscale enabled
B. Using Databricks Runtime, use a moderately sized CPU VM for the driver and a variable number of GPU enabled VMs for the workers with autoscale enabled
C. Using ML Runtime, use a large single node, GPU-enabled VM with auto termination set to 20 minutes to reduce costs
D. Using ML Runtime, use a moderate sized GPU VM for the driver and a variable number of memory optimized CPU VMs for the workers with autoscale enabled

Answer: A

Explanation:
Using the ML Runtime ensures deep learning frameworks and GPU drivers are preconfigured and optimized. A moderately sized CPU driver is sufficient for coordination, while GPU-enabled worker nodes handle the computationally intensive training workload. Enabling autoscaling allows the cluster to efficiently adapt to datasets ranging from hundreds of megabytes to hundreds of gigabytes, providing strong performance without overprovisioning and controlling costs in a shared team environment.

NEW QUESTION # 63
A Machine Learning Engineer needs to deploy a custom model using Databricks Model Serving.
The model requires an external tokenizer file (for example, a vocabulary or pre-trained tokenizer) to function correctly. They need to ensure this tokenizer file is included with the model so it is available during model serving. How should they package this tokenizer file as part of the model deployment?

A. Access the tokenizer file directly from Volumes at model inference time, which requires pre- provisioning access to the Volume for every model serving endpoint.
B. Include tokenizer file as part of the model's software dependencies by adding it to the conda_env specification.
C. Use the code_paths parameter in mlflow.pyfunc.log_model() to include the tokenizer file, assuming it's part of the custom Python logic needed by the model.
D. Use the artifacts parameter in mlflow.pyfunc.log_model() to package the tokenizer file, referencing its path in Volumes.

Answer: D

Explanation:
The artifacts parameter in mlflow.pyfunc.log_model is designed for packaging non-code assets required at inference time, such as tokenizer files. By logging the tokenizer as a model artifact and referencing its path, MLflow ensures the file is versioned with the model and automatically made available to Databricks Model Serving during inference.

NEW QUESTION # 64
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