databricks certified machine learning professional practice test

certified machine learning professional

Last exam update: May 17 ,2024
Page 1 out of 6. Viewing questions 1-10 out of 57

Question 1

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
Mark Question:
Answer:

b

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Question 2

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?

  • A. mlflow.spark.load_model(model_uri)
  • B. mlflow.pyfunc.read_model(model_uri)
  • C. mlflow.sklearn.read_model(model_uri)
  • D. mlflow.pyfunc.load_model(model_uri)
  • E. mlflow.sklearn.load_model(model_uri)
Mark Question:
Answer:

d

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Question 3

A data scientist is using MLflow to track their machine learning experiment. As a part of each MLflow run, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values.
They are using the following code block:

The code block is not nesting the runs in MLflow as they expected.
Which of the following changes does the data scientist need to make to the above code block so that it successfully nests the child runs under the parent run in MLflow?

  • A. Indent the child run blocks within the parent run block
  • B. Add the nested=True argument to the parent run
  • C. Remove the nested=True argument from the child runs
  • D. Provide the same name to the run_name parameter for all three run blocks
  • E. Add the nested=True argument to the parent run and remove the nested=True arguments from the child runs
Mark Question:
Answer:

e

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Question 4

A machine learning engineer wants to move their model version model_version for the MLflow Model Registry model model from the Staging stage to the Production stage using MLflow Client client.
Which of the following code blocks can they use to accomplish the task?

  • E. None
Mark Question:
Answer:

a

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hank123
3 days, 10 hours ago

update model version


Question 5

Which of the following MLflow operations can be used to delete a model from the MLflow Model Registry?

  • A. client.transition_model_version_stage
  • B. client.delete_model_version
  • C. client.update_registered_model
  • D. client.delete_model
  • E. client.delete_registered_model
Mark Question:
Answer:

e

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Question 6

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.
Mark Question:
Answer:

c

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Question 7

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
Mark Question:
Answer:

b

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Question 8

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
Mark Question:
Answer:

b

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Question 9

A machine learning engineer has developed a random forest model using scikit-learn, logged the model using MLflow as random_forest_model, and stored its run ID in the run_id Python variable. They now want to deploy that model by performing batch inference on a Spark DataFrame spark_df.
Which of the following code blocks can they use to create a function called predict that they can use to complete the task?

  • B. It is not possible to deploy a scikit-learn model on a Spark DataFrame.
  • E. None
Mark Question:
Answer:

d

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Question 10

Which of the following describes concept drift?

  • A. Concept drift is when there is a change in the distribution of an input variable
  • B. Concept drift is when there is a change in the distribution of a target variable
  • C. Concept drift is when there is a change in the relationship between input variables and target variables
  • D. Concept drift is when there is a change in the distribution of the predicted target given by the model
  • E. None of these describe Concept drift
Mark Question:
Answer:

d

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