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

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

• Question 21:

  A machine learning engineer is manually refreshing a model in an existing machine learning pipeline. The pipeline uses the MLflow Model Registry model "project". The machine learning engineer would like to add a new version of the model

  to "project".

  Which of the following MLflow operations can the machine learning engineer use to accomplish this task?

  A. mlflow.register_model
  B. MlflowClient.update_registered_model
  C. mlflow.add_model_version
  D. MlflowClient.get_model_version
  E. The machine learning engineer needs to create an entirely new MLflow Model Registry model
  A. mlflow.register_model

• Question 22:

  Which of the following describes the purpose of the context parameter in the predict method of Python models for MLflow?

  A. The context parameter allows the user to specify which version of the registered MLflow Model should be used based on the given application's current scenario
  B. The context parameter allows the user to document the performance of a model after it has been deployed
  C. The context parameter allows the user to include relevant details of the business case to allow downstream users to understand the purpose of the model
  D. The context parameter allows the user to provide the model with completely custom if-else logic for the given application's current scenario
  E. The context parameter allows the user to provide the model access to objects like preprocessing models or custom configuration files
  E. The context parameter allows the user to provide the model access to objects like preprocessing models or custom configuration files

• Question 23:

  A machine learning engineer is using the following code block as part of a batch deployment pipeline:

  

  Which of the following changes needs to be made so this code block will work when the inference table is a stream source?

  A. Replace "inference" with the path to the location of the Delta table
  B. Replace schema(schema) with option("maxFilesPerTrigger", 1)
  C. Replace spark.read with spark.readStream
  D. Replace format("delta") with format("stream")
  E. Replace predict with a stream-friendly prediction function
  C. Replace spark.read with spark.readStream

• Question 24:

  A data scientist is utilizing MLflow to track their machine learning experiments. After completing a series of runs for the experiment with experiment ID exp_id, the data scientist wants to programmatically work with the experiment run data in a

  Spark DataFrame. They have an active MLflow Client client and an active Spark session spark.

  Which of the following lines of code can be used to obtain run-level results for exp_id in a Spark DataFrame?

  A. client.list_run_infos(exp_id)
  B. spark.read.format("delta").load(exp_id)
  C. There is no way to programmatically return row-level results from an MLflow Experiment.
  D. mlflow.search_runs(exp_id)
  E. spark.read.format("mlflow-experiment").load(exp_id)
  E. spark.read.format("mlflow-experiment").load(exp_id)

• Question 25:

  Which of the following is a benefit of logging a model signature with an MLflow model?

  A. The model will have a unique identifier in the MLflow experiment
  B. The schema of input data can be validated when serving models
  C. The model can be deployed using real-time serving tools
  D. The model will be secured by the user that developed it
  E. The schema of input data will be converted to match the signature
  B. The schema of input data can be validated when serving models

• Question 26:

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

  A. The inference of batch processed records as soon as a trigger is hit
  B. The inference of all types of records in real-time
  C. The inference of batch processed records as soon as a Spark job is run
  D. The inference of incrementally processed records as soon as trigger is hit
  E. The inference of incrementally processed records as soon as a Spark job is run
  D. The inference of incrementally processed records as soon as trigger is hit

• Question 27:

  A machine learning engineer wants to view all of the active MLflow Model Registry Webhooks for a specific model. They are using the following code block:

  

  Which of the following changes does the machine learning engineer need to make to this code block so it will successfully accomplish the task?

  A. There are no necessary changes
  B. Replace list with view in the endpoint URL C. Replace POST with GET in the call to http_request
  D. Replace list with webhooks in the endpoint URL
  E. Replace POST with PUT in the call to http_request
  C

• Question 28:

  A machine learning engineering manager has asked all of the engineers on their team to add text descriptions to each of the model projects in the MLflow Model Registry. They are starting with the model project "model" and they'd like to add

  the text in the model_description variable.

  The team is using the following line of code:

  

  Which of the following changes does the team need to make to the above code block to accomplish the task?

  A. Replace update_registered_model with update_model_version
  B. There no changes necessary
  C. Replace description with artifact
  D. Replace client.update_registered_model with mlflow
  E. Add a Python model as an argument to update_registered_model
  B. There no changes necessary

• Question 29:

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

• Question 30:

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