DATABRICKS-MACHINE-LEARNING-PROFESSIONAL Online Practice Questions and Answers

A data scientist wants to remove the star_rating column from the Delta table at the location path. To do this, they need to load in data and drop the star_rating column. Which of the following code blocks accomplishes this task?

A. spark.read.format(“delta”).load(path).drop(“star_rating”)

B. spark.read.format(“delta”).table(path).drop(“star_rating”)

C. Delta tables cannot be modified

D. spark.read.table(path).drop(“star_rating”)

E. spark.sql(“SELECT * EXCEPT star_rating FROM path”)

Which of the following is a simple statistic to monitor for categorical feature drift?

A. Mode

B. None of these

C. Mode, number of unique values, and percentage of missing values

D. Percentage of missing values

E. Number of unique values

A machine learning engineering team wants to build a continuous pipeline for data preparation of a machine learning application. The team would like the data to be fully processed and made ready for inference in a series of equal-sized

batches.

Which of the following tools can be used to provide this type of continuous processing?

A. Spark UDFs

B. Structured Streaming

C. MLflow

D. Delta Lake

E. AutoML

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.

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

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

Which of the following describes the concept of MLflow Model flavors?

A. A convention that deployment tools can use to wrap preprocessing logic into a Model

B. A convention that MLflow Model Registry can use to version models

C. A convention that MLflow Experiments can use to organize their Runs by project

D. A convention that deployment tools can use to understand the model

E. A convention that MLflow Model Registry can use to organize its Models by project

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")

Which of the following is an advantage of using the python_function(pyfunc) model flavor over the built-in library-specific model flavors?

A. python_function provides no benefits over the built-in library-specific model flavors

B. python_function can be used to deploy models in a parallelizable fashion

C. python_function can be used to deploy models without worrying about which library was used to create the model

D. python_function can be used to store models in an MLmodel file

E. python_function can be used to deploy models without worrying about whether they are deployed in batch, streaming, or real-time environments

A machine learning engineer wants to log feature importance data from a CSV file at path importance_path with an MLflow run for model model. Which of the following code blocks will accomplish this task inside of an existing MLflow run block?

A. Option A

B. Option B

C. Option C

D. Option D

E. Option E