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

SectionWeightObjectives
ML Ops44%- Testing and validation strategies
- Environment management with Databricks Asset Bundles
- Automated retraining workflows
- Model monitoring and drift detection with Lakehouse Monitoring
Model Development44%- Scalable ML pipelines with SparkML
- Feature Store and automated feature pipelines
- Distributed training and hyperparameter tuning
- Advanced MLflow usage
Model Deployment12%- Model rollout and version management
- Deployment strategies
- Custom model serving

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Databricks Certified Machine Learning Professional Sample Questions (Q41-Q46):

NEW QUESTION # 41
A machine learning engineer needs a python_model to access a collection of files using its load_context operation. The collection of files being accessed by python_model.load_context needs to be saved when the model is being logged. A dictionary of the names and paths of these files is properly stored in my_dict.
The machine learning engineer has written the following incomplete block of code:

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

Answer: C

Explanation:
When logging a custom Python model with mlflow.pyfunc.log_model(), the artifacts parameter is used to specify a dictionary (my_dict) of file names and their paths that the model may need to access during loading or inference. These files are stored as model artifacts and become available to the model through the load_context method.


NEW QUESTION # 42
What is the purpose of ParamGridBuilder?

Answer: D

Explanation:
ParamGridBuilder creates parameter search grids for tuning.


NEW QUESTION # 43
A machine learning engineer has created a webhook with the following code block:

Which code block will trigger this webhook to run the associate job?

Answer: D

Explanation:
The webhook in the first image is configured to trigger on the
MODEL_VERSION_TRANSITIONED_TO_PRODUCTION event. The chosen code uses
client.transition_model_version_stage(...) to move the model version to "Production", which matches the event type in the webhook and will correctly trigger the associated job.


NEW QUESTION # 44
A Machine Learning Engineer has a computer vision model in Databricks Model Serving that obscures sensitive data from images. Internal teams use the model throughout the work week when they request access to new files. Recently model users complained that the model takes much longer in the morning. This coincides with when people arrive at work and request files for the day. When the engineer reviews the endpoint health metrics, they see P50 model latency peaks around 9AM at 20 seconds. Request rate also peaks at 9AM at 15 requests/second. The GPU utilization is over 60%, GPU memory usage over 50%, and provisioned concurrency at 4 throughout the day. What can the engineer do to reduce user wait time when request rate peaks at 9AM each morning?

Answer: C

Explanation:
The symptoms indicate a concurrency bottleneck during the 9AM traffic spike: request rate increases sharply, latency jumps, and the endpoint is already running at a fixed provisioned concurrency of 4. Increasing workload_size scales the endpoint horizontally to handle more concurrent requests, reducing queueing and bringing down user-perceived wait time during peak demand.


NEW QUESTION # 45
A machine learning engineer has created a webhook with the following code block:

Which of the following code blocks will trigger this webhook to run the associate job?

Answer: C


NEW QUESTION # 46
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