Databricks Databricks-Machine-Learning-Professional : Databricks Certified Machine Learning Professional

A Data Scientist is building a machine learning pipeline to classify raw text using a Logistic Regression model in Spark using Spark MLlib's Pipelines. This pipeline has three stages: the Tokenizer (to split the raw text in tokens), a HashingTF (to transform tokens into hashes) and the Logistic Regression itself (to perform the classification of texts). The Spark DataFrame with the training data is called trainingDF and the one with the test data is called testDF.
In order to do this, they use the following incomplete piece of code:

Which option correctly states:
(i) The complete command to run model training;
(ii) The complete command to execute the prediction on test data;
(iii) The object type of the model object returned by the model
training command.

• A.
  
• B.
  
• C.
  
• D.
  

A Machine Learning Engineer has a production AI model that predicts fraudulent transactions in real time. This model is integrated into critical business workflows and any downtime could result in significant financial loss and regulatory penalties. The engineer needs to deploy a new, improved version of the model to production to replace the existing model. The bank's fairness policy requires that all transactions be exposed to the same model at any given time. Additionally, the deployment must meet the following requirements:
- Zero downtime: The fraud detection service must remain continuously
available to users and downstream systems.
- Immediate rollback: If the new model causes issues, you must be able
to revert to the previous version instantly.
Which deployment strategy meets these requirements?

• A. Blue-Green
• B. Canary
• C. Shadow
• D. Rolling

A machine learning engineer is developing a recommendation system for online content. They are using the Databricks Feature Store to store features for training and inference. Which unit test should they create?

• A. Test feature transformation functions
• B. Test Feature Store lookup during inference
• C. Test MLflow Model Registry operations
• D. Test that Pandas DataFrame operations work as expected

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

A Machine Learning Engineer developed a dynamic pricing model in MLflow that requires values from the company's cloud database to generate predictions. At inference time the PyFunc model uses the cloud provider's Python SDK to retrieve the latest values from the database. The inference code works well in a notebook, but when the engineer deploys the model to Databricks Model Serving, they receive 401 errors saying the user is not authenticated when trying to access the database. The engineer deploys the code via the REST API with the following payload:

Their Databricks administrators store cloud credentials under a Databricks secret scope called
"cloud_creds" with key "db_key". These credentials can be used to authenticate to the cloud provider's SDK.
Which change can the engineer make so the endpoint can authenticate to the remote database while avoiding storing the access tokens in plain text?

• A. Retrieve the secret values using a notebook with dbutils.secrets.get(scope="cloud_creds", key="db_key"). Log the model to MLflow with the access token value as a custom MLflow artifact.
  Register the model in Unity Catalog. Change the PyFunc model code to read the artifact and pass the secret to the cloud provider's SDK.
• B. Retrieve the secret values using a notebook with dbutils.secrets.get(scope="cloud_creds", key="db_key"). Add "environment_vars":
  {"DB_ACCESS_TOKEN": ""} to the served_entity. Change the PyFunc model code to read the environment variable and pass the secret to the cloud provider's SDK.
• C. Add "environment_vars": {"DB_ACCESS_TOKEN": "{{secrets/cloud_creds/db_key}}"} to the served_entity. Change the PyFunc model code to read the environment variable and pass the secret to the cloud provider's SDK.
• D. Log the model to MLflow and store {{secrets/cloud_creds/db_key}} as a MLflow secret in
  /.mlflow/credentials. Register the model in Unity Catalog. When deployed in Databricks Model Serving, MLflow will automatically authenticate when the model server starts.