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Question 1
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
Question 2
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
Question 3
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)
Question 4
A data scientist has developed and logged a scikit-learn random forest model model, and then they ended their Spark session and terminated their cluster. After starting a new cluster, they want to review the feature_importances_ of the original model object.
Which of the following lines of code can be used to restore the model object so that feature_importances_ is available?
- A: mlflow.load_model(model_uri)
- B: client.list_artifacts(run_id)["feature-importances.csv"]
- C: mlflow.sklearn.load_model(model_uri)
- D: This can only be viewed in the MLflow Experiments UI
- E: client.pyfunc.load_model(model_uri)
Question 5
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
Question 6
Which of the following is a probable response to identifying drift in a machine learning application?
- A: None of these responses
- B: Retraining and deploying a model on more recent data
- C: All of these responses
- D: Rebuilding the machine learning application with a new label variable
- E: Sunsetting the machine learning application
Question 7
A data scientist has computed updated feature values for all primary key values stored in the Feature Store table features. In addition, feature values for some new primary key values have also been computed. The updated feature values are stored in the DataFrame features_df. They want to replace all data in features with the newly computed data.
Which of the following code blocks can they use to perform this task using the Feature Store Client fs?
- A:
- B:
- C:
- D:
- E:
Question 8
After a data scientist noticed that a column was missing from a production feature set stored as a Delta table, the machine learning engineering team has been tasked with determining when the column was dropped from the feature set.
Which of the following SQL commands can be used to accomplish this task?
- A: VERSION
- B: DESCRIBE
- C: HISTORY
- D: DESCRIBE HISTORY
- E: TIMESTAMP
Question 9
Which of the following describes label drift?
- A: Label drift is when there is a change in the distribution of the predicted target given by the model
- B: None of these describe label drift
- C: Label drift is when there is a change in the distribution of an input variable
- D: Label drift is when there is a change in the relationship between input variables and target variables
- E: Label drift is when there is a change in the distribution of a target variable
Question 10
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
Question 11
A data scientist would like to enable MLflow Autologging for all machine learning libraries used in a notebook. They want to ensure that MLflow Autologging is used no matter what version of the Databricks Runtime for Machine Learning is used to run the notebook and no matter what workspace-wide configurations are selected in the Admin Console.
Which of the following lines of code can they use to accomplish this task?
- A: mlflow.sklearn.autolog()
- B: mlflow.spark.autolog()
- C: spark.conf.set(“autologging”, True)
- D: It is not possible to automatically log MLflow runs.
- E: mlflow.autolog()
Question 12
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
Question 13
A data scientist has developed a model model and computed the RMSE of the model on the test set. They have assigned this value to the variable rmse. They now want to manually store the RMSE value with the MLflow run.
They write the following incomplete code block:
Which of the following lines of code can be used to fill in the blank so the code block can successfully complete the task?
- A: log_artifact
- B: log_model
- C: log_metric
- D: log_param
- E: There is no way to store values like this.
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