Free Databricks Databricks-Machine-Learning-Professional Exam Dumps Questions & Answers

A Data Scientist at a company with rapidly increasing sales has deployed a scikit-learn model in production, which is retrained weekly on a single-node cluster. During the most recent retraining, the job failed due to an out-of-memory error. Upon investigation, the Data Scientist discovered that the training data had increased to 700GB as a result of the company's expanding customer base. Which approach will reliably resolve this issue in the long term?

• A.  Refactor the code to use the DeepSpeed library to shard the scikit-learn model across multiple nodes, enabling distributed model training on large volumes of data
• B.  Increase the size of the current single-node cluster to handle the increase of data while minimizing the need for code refactoring
• C.  Wrap the current model training code as a Spark UDF and apply it to the Spark DataFrame to distribute model training across multiple nodes.
• D.  Refactor the code to use MLlib across multiple nodes, enabling distributed model training on large volumes of data

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?

• A.  Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale
• B.  Because the streaming deployment is always on, there is no practitioner to debug poor model performance
• C.  None of these statements
• D.  Because the streaming deployment is always on, all types of data must be handled without producing an error
• E.  All of these statements

A Machine Learning Engineer needs to build a credit risk model using Databricks. Due to strict data governance, production data cannot be accessed from development or staging environments. To manage MLOps, the engineer uses a "deploy code" strategy with separate development, staging, and production environments mapped to different catalogs in Unity Catalog. The CI/CD pipeline automates environment transitions. What is the primary architectural component promoted from staging to production to generate the final, production-ready model in this scenario?

• A.  A containerized image of the complete inference pipeline, including feature transformations.
• B.  The model version entry in the Unity Catalog that has been transitioned from the "Staging" to the
  "Production" alias.
• C.  The model artifact trained and validated in the staging environment on a subset of the data.
• D.  The CI/CD-approved model training source code, which will be executed in the production environment on the full production dataset.

Which tool can be used to automatically start a testing Job when a new version of an MLflow Model Registry model is registered?

• A.  MLflow Model Registry Webhooks
• B.  MLflow Client API
• C.  MLflow Model Registry UI
• D.  MLflow REST API

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?

• A.  mlflow.models.Model.get_input_schema
• B.  mlflow.models.Model.signature
• C.  mlflow.models.signature.infer_signature
• D.  mlflow.models.schema.infer_schema
• E.  There is no way to obtain the input schema and the output schema of an unlogged model.