Databricks Certified Machine Learning Associate - Certified Machine Learning Associate Exam
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Total 140 questions
Question #11 (Topic: Exam A)
What is the name of the method that transforms categorical features into a series of binary indicator feature variables?
A. Leave-one-out encoding
B. Target encoding
C. One-hot encoding
D. Categorical embeddings
E. String indexing
Answer: C
Question #12 (Topic: Exam A)
A data scientist wants to parallelize the training of trees in a gradient boosted tree to speed up the training process. A colleague suggests that parallelizing a boosted tree algorithm can be difficult.
Which of the following describes why?
Which of the following describes why?
A. Gradient boosting is not a linear algebra-based algorithm which is required for parallelization.
B. Gradient boosting requires access to all data at once which cannot happen during parallelization.
C. Gradient boosting calculates gradients in evaluation metrics using all cores which prevents parallelization.
D. Gradient boosting is an iterative algorithm that requires information from the previous iteration to perform the next step.
E. Gradient boosting uses decision trees in each iteration which cannot be parallelized.
Answer: D
Question #13 (Topic: Exam A)
A data scientist wants to efficiently tune the hyperparameters of a scikit-learn model. They elect to use the Hyperopt library's fmin operation to facilitate this process. Unfortunately, the final model is not very accurate. The data scientist suspects that there is an issue with the objective_function being passed as an argument to fmin.
They use the following code block to create the objective_function:
Which of the following changes does the data scientist need to make to their objective_function in order to produce a more accurate model?
They use the following code block to create the objective_function:
Which of the following changes does the data scientist need to make to their objective_function in order to produce a more accurate model?
A. Add test set validation process
B. Add a random_state argument to the RandomForestRegressor operation
C. Remove the mean operation that is wrapping the cross_val_score operation
D. Replace the r2 return value with -r2
E. Replace the fmin operation with the fmax operation
Answer: D
Question #14 (Topic: Exam A)
A data scientist is attempting to tune a logistic regression model logistic using scikit-learn. They want to specify a search space for two hyperparameters and let the tuning process randomly select values for each evaluation.
They attempt to run the following code block, but it does not accomplish the desired task:
Which of the following changes can the data scientist make to accomplish the task?
They attempt to run the following code block, but it does not accomplish the desired task:
Which of the following changes can the data scientist make to accomplish the task?
A. Replace the GridSearchCV operation with RandomizedSearchCV
B. Replace the GridSearchCV operation with cross_validate
C. Replace the GridSearchCV operation with ParameterGrid
D. Replace the random_state=0 argument with random_state=1
E. Replace the penalty= ['12', '11'] argument with penalty=uniform ('12', '11')
Answer: A
Question #15 (Topic: Exam A)
Which of the following tools can be used to parallelize the hyperparameter tuning process for single-node machine learning models using a Spark cluster?
A. MLflow Experiment Tracking
B. Spark ML
C. Autoscaling clusters
D. Hyperopt
E. Delta Lake
Answer: D
