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Databricks Databricks-Machine-Learning-Associate Exam Syllabus Topics:

Topic	Details
Topic 1	Spark ML: It discusses the concepts of Distributed ML. Moreover, this topic covers Spark ML Modeling APIs, Hyperopt, Pandas API, Pandas UDFs, and Function APIs.
Topic 2	Scaling ML Models: This topic covers Model Distribution and Ensembling Distribution.
Topic 3	ML Workflows: The topic focuses on Exploratory Data Analysis, Feature Engineering, Training, Evaluation and Selection.
Topic 4	Databricks Machine Learning: It covers sub-topics of AutoML, Databricks Runtime, Feature Store, and MLflow.

Databricks Certified Machine Learning Associate Exam Sample Questions (Q16-Q21):

NEW QUESTION # 16
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. One-hot encoding
C. Categorical
D. String indexing
E. Target encoding

Answer: B

Explanation:
The method that transforms categorical features into a series of binary indicator variables is known as one-hot encoding. This technique converts each categorical value into a new binary column, which is essential for models that require numerical input. One-hot encoding is widely used because it helps to handle categorical data without introducing a false ordinal relationship among categories.
Reference:
Feature Engineering Techniques (One-Hot Encoding).

NEW QUESTION # 17
A machine learning engineer has identified the best run from an MLflow Experiment. They have stored the run ID in the run_id variable and identified the logged model name as "model". They now want to register that model in the MLflow Model Registry with the name "best_model".
Which lines of code can they use to register the model associated with run_id to the MLflow Model Registry?

A. mlflow.register_model(f"runs:/{run_id}/model", "best_model")
B. mlflow.register_model(f"runs:/{run_id}/best_model", "model")
C. mlflow.register_model(run_id, "best_model")
D. millow.register_model(f"runs:/{run_id)/model")

Answer: A

Explanation:
To register a model that has been identified by a specific run_id in the MLflow Model Registry, the appropriate line of code is:
mlflow.register_model(f"runs:/{run_id}/model", "best_model")
This code correctly specifies the path to the model within the run (runs:/{run_id}/model) and registers it under the name "best_model" in the Model Registry. This allows the model to be tracked, managed, and transitioned through different stages (e.g., Staging, Production) within the MLflow ecosystem.
Reference
MLflow documentation on model registry: https://www.mlflow.org/docs/latest/model-registry.html#registering-a-model

NEW QUESTION # 18
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?

A. Replace the GridSearchCV operation with cross_validate
B. Replace the GridSearchCV operation with ParameterGrid
C. Replace the random_state=0 argument with random_state=1
D. Replace the penalty= ['12', '11'] argument with penalty=uniform ('12', '11')
E. Replace the GridSearchCV operation with RandomizedSearchCV

Answer: E

Explanation:
The user wants to specify a search space for hyperparameters and let the tuning process randomly select values. GridSearchCV systematically tries every combination of the provided hyperparameter values, which can be computationally expensive and time-consuming. RandomizedSearchCV, on the other hand, samples hyperparameters from a distribution for a fixed number of iterations. This approach is usually faster and still can find very good parameters, especially when the search space is large or includes distributions.
Reference
Scikit-Learn documentation on hyperparameter tuning: https://scikit-learn.org/stable/modules/grid_search.html#randomized-parameter-optimization

NEW QUESTION # 19
Which of the following machine learning algorithms typically uses bagging?

A. Gradient boosted trees
B. Decision tree
C. Linear regression
D. Random forest
E. K-means

Answer: D

Explanation:
Random Forest is a machine learning algorithm that typically uses bagging (Bootstrap Aggregating). Bagging involves training multiple models independently on different random subsets of the data and then combining their predictions. Random Forests consist of many decision trees trained on random subsets of the training data and features, and their predictions are averaged to improve accuracy and control overfitting. This method enhances model robustness and predictive performance.
Reference:
Ensemble Methods in Machine Learning (Understanding Bagging and Random Forests).

NEW QUESTION # 20
The implementation of linear regression in Spark ML first attempts to solve the linear regression problem using matrix decomposition, but this method does not scale well to large datasets with a large number of variables.
Which of the following approaches does Spark ML use to distribute the training of a linear regression model for large data?

A. Least-squares method
B. Spark ML cannot distribute linear regression training
C. Singular value decomposition
D. Logistic regression
E. Iterative optimization

Answer: E

NEW QUESTION # 21
......

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Databricks Databricks-Machine-Learning-Associate New Dumps Files & Databricks-Machine-Learning-Associate Valid Exam Topics

Free PDF 2025 Databricks Fantastic Databricks-Machine-Learning-Associate New Dumps Files

Databricks Databricks-Machine-Learning-Associate Exam Syllabus Topics:

Databricks Certified Machine Learning Associate Exam Sample Questions (Q16-Q21):

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