Databricks DATABRICKS-MACHINE-LEARNING-PROFESSIONAL Online Practice Questions and Exam Preparation

Databricks DATABRICKS-MACHINE-LEARNING-PROFESSIONAL Online Questions & Answers

• Question 11:

  A machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. They have programmatically identified the best run from an MLflow Experiment and stored its URI in the model_uri variable and its Run ID in the run_id variable. They have also determined that the model was logged with the name "model". Now, the machine learning engineer wants to register that model in the MLflow Model Registry with the name "best_model". Which of the following lines of code can they use to register the model to the MLflow Model Registry?

  A. mlflow.register_model(model_uri, "best_model")
  B. mlflow.register_model(run_id, "best_model")
  C. mlflow.register_model(f"runs:/{run_id}/best_model", "model")
  D. mlflow.register_model(model_uri, "model")
  E. mlflow.register_model(f"runs:/{run_id}/model")
  A. mlflow.register_model(model_uri, "best_model")

• Question 12:

  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?

  A. https:///model-serving/recommender/Production/invocations
  B. The version number of the model version in Production is necessary to complete this task.
  C. https:///model/recommender/stage-production/invocations
  D. https:///model-serving/recommender/stage-production/invocations
  E. https:///model/recommender/Production/invocations
  E. https:///model/recommender/Production/invocations

• Question 13:

  A data scientist has developed a model to predict ice cream sales using the expected temperature and expected number of hours of sun in the day. However, the expected temperature is dropping beneath the range of the input variable on

  which the model was trained.

  Which of the following types of drift is present in the above scenario?

  A. Label drift
  B. None of these
  C. Concept drift
  D. Prediction drift
  E. Feature drift
  E. Feature drift

• Question 14:

  A machine learning engineer is attempting to create a webhook that will trigger a Databricks Job job_id when a model version for model model transitions into any MLflow Model Registry stage. They have the following incomplete code block:

  

  Which of the following lines of code can be used to fill in the blank so that the code block accomplishes the task?

  A. "MODEL_VERSION_CREATED"
  B. "MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"
  C. "MODEL_VERSION_TRANSITIONED_TO_STAGING"
  D. "MODEL_VERSION_TRANSITIONED_STAGE"
  E. "MODEL_VERSION_TRANSITIONED_TO_STAGING", "MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"
  D. "MODEL_VERSION_TRANSITIONED_STAGE"

• Question 15:

  Which of the following machine learning model deployment paradigms is the most common for machine learning projects?

  A. On-device
  B. Streaming
  C. Real-time
  D. Batch
  E. None of these deployments
  D. Batch

• Question 16:

  A machine learning engineer is monitoring categorical input variables for a production machine learning application. The engineer believes that missing values are becoming more prevalent in more recent data for a particular value in one of

  the categorical input variables.

  Which of the following tools can the machine learning engineer use to assess their theory?

  A. Kolmogorov-Smirnov (KS) test
  B. One-way Chi-squared Test
  C. Two-way Chi-squared Test
  D. Jenson-Shannon distance
  E. None of these
  B. One-way Chi-squared Test

• Question 17:

  A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model. They have custom preprocessing that needs to be completed on feature variables prior to fitting the model or computing predictions using that model.

  They decide to wrap this preprocessing in a custom model class ModelWithPreprocess, where the preprocessing is performed when calling fit and when calling predict. They then log the fitted model of the ModelWithPreprocess class as a

  pyfunc model.

  Which of the following is a benefit of this approach when loading the logged pyfunc model for downstream deployment?

  A. The pyfunc model can be used to deploy models in a parallelizable fashion
  B. The same preprocessing logic will automatically be applied when calling fit
  C. The same preprocessing logic will automatically be applied when calling predict
  D. This approach has no impact when loading the logged pyfunc model for downstream deployment
  E. There is no longer a need for pipeline-like machine learning objects
  C. The same preprocessing logic will automatically be applied when calling predict

• Question 18:

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

  A. Cloud-based compute
  B. None of these tools
  C. REST APIs
  D. Containers
  E. Autoscaling clusters
  D. Containers

• Question 19:

  Which of the following is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov-Smirnov (KS) test for numeric feature drift detection?

  A. All of these reasons
  B. JS is not normalized or smoothed
  C. None of these reasons
  D. JS is more robust when working with large datasets
  E. JS does not require any manual threshold or cutoff determinations
  E. JS does not require any manual threshold or cutoff determinations

• Question 20:

  A data scientist has written a function to track the runs of their random forest model. The data scientist is changing the number of trees in the forest across each run. Which of the following MLflow operations is designed to log single values like the number of trees in a random forest?

  A. mlflow.log_artifact
  B. mlflow.log_model
  C. mlflow.log_metric
  D. mlflow.log_param
  E. There is no way to store values like this.
  D. mlflow.log_param