Databricks-Machine-Learning-Associate Actual Exams, Databricks-Machine-Learning-Associate Valid Examcollection

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

Topic	Details
Topic 1	ML Workflows: The topic focuses on Exploratory Data Analysis, Feature Engineering, Training, Evaluation and Selection.
Topic 2	Scaling ML Models: This topic covers Model Distribution and Ensembling Distribution.
Topic 3	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 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
A data scientist is using Spark ML to engineer features for an exploratory machine learning project.
They decide they want to standardize their features using the following code block:

Upon code review, a colleague expressed concern with the features being standardized prior to splitting the data into a training set and a test set.
Which of the following changes can the data scientist make to address the concern?

A. Utilize a cross-validation process rather than a train-test split process to remove the need for standardizing data
B. Utilize the Pipeline API to standardize the test data according to the training data's summary statistics
C. Utilize the MinMaxScaler object to standardize the test data according to global minimum and maximum values
D. Utilize the Pipeline API to standardize the training data according to the test data's summary statistics
E. Utilize the MinMaxScaler object to standardize the training data according to global minimum and maximum values

Answer: B

Explanation:
To address the concern about standardizing features prior to splitting the data, the correct approach is to use the Pipeline API to ensure that only the training data's summary statistics are used to standardize the test data. This is achieved by fitting the StandardScaler (or any scaler) on the training data and then transforming both the training and test data using the fitted scaler. This approach prevents information leakage from the test data into the model training process and ensures that the model is evaluated fairly.
Reference:
Best Practices in Preprocessing in Spark ML (Handling Data Splits and Feature Standardization).

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 has developed a random forest regressor rfr and included it as the final stage in a Spark MLPipeline pipeline. They then set up a cross-validation process with pipeline as the estimator in the following code block:

Which of the following is a negative consequence of including pipeline as the estimator in the cross-validation process rather than rfr as the estimator?

A. The process will have a longer runtime because all stages of pipeline need to be refit or retransformed with each mode
B. The process will leak data from the training set to the test set during the evaluation phase
C. The process will leak data prep information from the validation sets to the training sets for each model
D. The process will be unable to parallelize tuning due to the distributed nature of pipeline

Answer: A

Explanation:
Including the entire pipeline as the estimator in the cross-validation process means that all stages of the pipeline, including data preprocessing steps like string indexing and vector assembling, will be refit or retransformed for each fold of the cross-validation. This results in a longer runtime because each fold requires re-execution of these preprocessing steps, which can be computationally expensive.
If only the random forest regressor (rfr) were included as the estimator, the preprocessing steps would be performed once, and only the model fitting would be repeated for each fold, significantly reducing the computational overhead.
Reference:
Databricks documentation on cross-validation: Cross Validation

NEW QUESTION # 19
An organization is developing a feature repository and is electing to one-hot encode all categorical feature variables. A data scientist suggests that the categorical feature variables should not be one-hot encoded within the feature repository.
Which of the following explanations justifies this suggestion?

A. One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms.
B. One-hot encoding is not a common strategy for representing categorical feature variables numerically.
C. One-hot encoding is dependent on the target variable's values which differ for each apaplication.
D. One-hot encoding is computationally intensive and should only be performed on small samples of training sets for individual machine learning problems.

Answer: A

Explanation:
The suggestion not to one-hot encode categorical feature variables within the feature repository is justified because one-hot encoding can be problematic for some machine learning algorithms. Specifically, one-hot encoding increases the dimensionality of the data, which can be computationally expensive and may lead to issues such as multicollinearity and overfitting. Additionally, some algorithms, such as tree-based methods, can handle categorical variables directly without requiring one-hot encoding.
Reference:
Databricks documentation on feature engineering: Feature Engineering

NEW QUESTION # 20
A data scientist is performing hyperparameter tuning using an iterative optimization algorithm. Each evaluation of unique hyperparameter values is being trained on a single compute node. They are performing eight total evaluations across eight total compute nodes. While the accuracy of the model does vary over the eight evaluations, they notice there is no trend of improvement in the accuracy. The data scientist believes this is due to the parallelization of the tuning process.
Which change could the data scientist make to improve their model accuracy over the course of their tuning process?

A. Change the number of compute nodes to be half or less than half of the number of evaluations.
B. Change the iterative optimization algorithm used to facilitate the tuning process.
C. Change the number of compute nodes and the number of evaluations to be much larger but equal.
D. Change the number of compute nodes to be double or more than double the number of evaluations.

Answer: B

Explanation:
The lack of improvement in model accuracy across evaluations suggests that the optimization algorithm might not be effectively exploring the hyperparameter space. Iterative optimization algorithms like Tree-structured Parzen Estimators (TPE) or Bayesian Optimization can adapt based on previous evaluations, guiding the search towards more promising regions of the hyperparameter space.
Changing the optimization algorithm can lead to better utilization of the information gathered during each evaluation, potentially improving the overall accuracy.
Reference:
Hyperparameter Optimization with Hyperopt

NEW QUESTION # 21
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DATABRICKS-MACHINE-LEARNING-ASSOCIATE ACTUAL EXAMS, DATABRICKS-MACHINE-LEARNING-ASSOCIATE VALID EXAMCOLLECTION

Databricks-Machine-Learning-Associate Actual Exams, Databricks-Machine-Learning-Associate Valid Examcollection