Certified Machine Learning Professional — Free Practice Questions
10 free sample questions from a bank of 60, with the correct answers and explanations. No signup required — start practising right now.
1Which of the following describes concept drift?
Concept drift is when there is a change in the distribution of an input variable
Concept drift is when there is a change in the distribution of a target variable
Concept drift is when there is a change in the relationship between input variables and target variables
Concept drift is when there is a change in the distribution of the predicted target given by the model
None of these describe Concept drift
Answer: C
2Which of the following is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov-Smirnov (KS) test for numeric feature drift detection?
All of these reasons
JS is not normalized or smoothed
None of these reasons
JS is more robust when working with large datasets
JS does not require any manual threshold or cutoff determinations
Answer: D
3A 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?
client.list_run_infos(exp_id)
spark.read.format("delta").load(exp_id)
There is no way to programmatically return row-level results from an MLflow Experiment.
4A 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?
This can only be viewed in the MLflow Experiments UI
client.pyfunc.load_model(model_uri)
Answer: C
5Which of the following is a simple statistic to monitor for categorical feature drift?
Mode
None of these
Mode, number of unique values, and percentage of missing values
Percentage of missing values
Number of unique values
Answer: C
6Which of the following is a probable response to identifying drift in a machine learning application?
None of these responses
Retraining and deploying a model on more recent data
All of these responses
Rebuilding the machine learning application with a new label variable
Sunsetting the machine learning application
Answer: B
7A 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?
Answer:
8After 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?
VERSION
DESCRIBE
HISTORY
DESCRIBE HISTORY
TIMESTAMP
Answer: D
9Which of the following describes label drift?
Label drift is when there is a change in the distribution of the predicted target given by the model
None of these describe label drift
Label drift is when there is a change in the distribution of an input variable
Label drift is when there is a change in the relationship between input variables and target variables
Label drift is when there is a change in the distribution of a target variable
Answer: E
10Which of the following machine learning model deployment paradigms is the most common for machine learning projects?