Certified Machine Learning Professional Practice Test

Certified Machine Learning Professional Practice Test practice questions, page 7 of 9, with answer review and explanations.

Question 31

Which of the following statements describes streaming with Spark as a model deployment strategy?

  1. The inference of batch processed records as soon as a trigger is hit
  2. The inference of all types of records in real-time
  3. The inference of batch processed records as soon as a Spark job is run
  4. The inference of incrementally processed records as soon as trigger is hit
  5. The inference of incrementally processed records as soon as a Spark job is run

Question 32

A machine learning engineer has deployed a model recommender using MLflow Model Serving. They now want to query the version of that model that is in the Production stage of the MLflow Model Registry. Which of the following model URIs can be used to query the described model version?

  1. https://<databricks-instance>/model-serving/recommender/Production/invocations
  2. The version number of the model version in Production is necessary to complete this task.
  3. https://<databricks-instance>/model/recommender/stage-production/invocations
  4. https://<databricks-instance>/model-serving/recommender/stage-production/invocations
  5. https://<databricks-instance>/model/recommender/Production/invocations

Question 33

Which of the following tools can assist in real-time deployments by packaging software with its own application, tools, and libraries?

  1. Cloud-based compute
  2. None of these tools
  3. REST APIs
  4. Containers
  5. Autoscaling clusters

Question 34

A machine learning engineer has registered a sklearn model in the MLflow Model Registry using the sklearn model flavor with UI model_uri. Which of the following operations can be used to load the model as an sklearn object for batch deployment?

  1. mlflow.spark.load_model(model_uri)
  2. mlflow.pyfunc.read_model(model_uri)
  3. mlflow.sklearn.read_model(model_uri)
  4. mlflow.pyfunc.load_model(model_uri)
  5. mlflow.sklearn.load_model(model_uri)

Question 35

A data scientist set up a machine learning pipeline to automatically log a data visualization with each run. They now want to view the visualizations in Databricks. Which of the following locations in Databricks will show these data visualizations?

  1. The MLflow Model Registry Model page
  2. The Artifacts section of the MLflow Experiment page
  3. Logged data visualizations cannot be viewed in Databricks
  4. The Artifacts section of the MLflow Run page
  5. The Figures section of the MLflow Run page