Databricks Certified Machine Learning Professional Exam Practice Questions (P. 5)
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Question #21
Which of the following MLflow operations can be used to automatically calculate and log a Shapley feature importance plot?
- Amlflow.shap.log_explanationMost Voted
- BNone of these operations can accomplish the task.
- Cmlflow.shap
- Dmlflow.log_figure
- Eclient.log_artifact
Correct Answer:
C
C
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Question #22
A data scientist has developed a scikit-learn random forest model model, but they have not yet logged model with MLflow. They want to obtain the input schema and the output schema of the model so they can document what type of data is expected as input.
Which of the following MLflow operations can be used to perform this task?
Which of the following MLflow operations can be used to perform this task?
- Amlflow.models.schema.infer_schema
- Bmlflow.models.signature.infer_signatureMost Voted
- Cmlflow.models.Model.get_input_schema
- Dmlflow.models.Model.signature
- EThere is no way to obtain the input schema and the output schema of an unlogged model.
Correct Answer:
E
E
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Question #23
A machine learning engineer and data scientist are working together to convert a batch deployment to an always-on streaming deployment. The machine learning engineer has expressed that rigorous data tests must be put in place as a part of their conversion to account for potential changes in data formats.
Which of the following describes why these types of data type tests and checks are particularly important for streaming deployments?
Which of the following describes why these types of data type tests and checks are particularly important for streaming deployments?
- ABecause the streaming deployment is always on, all types of data must be handled without producing an errorMost Voted
- BAll of these statements
- CBecause the streaming deployment is always on, there is no practitioner to debug poor model performance
- DBecause the streaming deployment is always on, there is a need to confirm that the deployment can autoscale
- ENone of these statements
Correct Answer:
D
D
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Question #24
Which of the following deployment paradigms can centrally compute predictions for a single record with exceedingly fast results?
- AStreaming
- BBatch
- CEdge/on-device
- DNone of these strategies will accomplish the task.
- EReal-timeMost Voted
Correct Answer:
D
D
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Question #25
A machine learning engineering team wants to build a continuous pipeline for data preparation of a machine learning application. The team would like the data to be fully processed and made ready for inference in a series of equal-sized batches.
Which of the following tools can be used to provide this type of continuous processing?
Which of the following tools can be used to provide this type of continuous processing?
- ASpark UDFs
- BStructured StreamingMost Voted
- CMLflow
- DDelta Lake
- EAutoML
Correct Answer:
A
A
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