PDF Databricks-Machine-Learning-Associate Cram Exam, Databricks-Machine-Learning-Associate Reliable Test Cram

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

Topic	Details
Topic 1	Scaling ML Models: This topic covers Model Distribution and Ensembling Distribution.
Topic 2	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 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.

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Databricks Certified Machine Learning Associate Exam Sample Questions (Q70-Q75):

NEW QUESTION # 70
A data scientist is using MLflow to track their machine learning experiment. As a part of each of their MLflow runs, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values. All parent and child runs are being manually started with mlflow.start_run.
Which of the following approaches can the data scientist use to accomplish this MLflow run organization?

A. They can specify nested=True when starting the child run for each unique combination of hyperparameter values
B. They can turn on Databricks Autologging
C. They can start each child run inside the parent run's indented code block using mlflow.start runO
D. They can specify nested=True when starting the parent run for the tuning process
E. They can start each child run with the same experiment ID as the parent run

Answer: A

Explanation:
To organize MLflow runs with one parent run for the tuning process and a child run for each unique combination of hyperparameter values, the data scientist can specify nested=True when starting the child run. This approach ensures that each child run is properly nested under the parent run, maintaining a clear hierarchical structure for the experiment. This nesting helps in tracking and comparing different hyperparameter combinations within the same tuning process.
Reference:
MLflow Documentation (Managing Nested Runs).

NEW QUESTION # 71
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 the Pipeline API to standardize the training data according to the test data's summary statistics
B. Utilize the MinMaxScaler object to standardize the training data according to global minimum and maximum values
C. Utilize a cross-validation process rather than a train-test split process to remove the need for standardizing data
D. Utilize the Pipeline API to standardize the test data according to the training data's summary statistics
E. Utilize the MinMaxScaler object to standardize the test data according to global minimum and maximum values

Answer: D

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 # 72
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 random_state=0 argument with random_state=1
B. Replace the penalty= ['12', '11'] argument with penalty=uniform ('12', '11')
C. Replace the GridSearchCV operation with ParameterGrid
D. Replace the GridSearchCV operation with cross_validate
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 # 73
Which of the following machine learning algorithms typically uses bagging?

A. Decision tree
B. Random forest
C. K-means
D. IGradient boosted trees

Answer: B

Explanation:
Random Forest is a machine learning algorithm that typically uses bagging (Bootstrap Aggregating). Bagging is a technique that involves training multiple base models (such as decision trees) on different subsets of the data and then combining their predictions to improve overall model performance. Each subset is created by randomly sampling with replacement from the original dataset. The Random Forest algorithm builds multiple decision trees and merges them to get a more accurate and stable prediction.
Reference:
Databricks documentation on Random Forest: Random Forest in Spark ML

NEW QUESTION # 74
A data scientist has developed a linear regression model using Spark ML and computed the predictions in a Spark DataFrame preds_df with the following schema:
prediction DOUBLE
actual DOUBLE
Which of the following code blocks can be used to compute the root mean-squared-error of the model according to the data in preds_df and assign it to the rmse variable?

Answer: D

Explanation:
The code block to compute the root mean-squared error (RMSE) for a linear regression model in Spark ML should use the RegressionEvaluator class with metricName set to "rmse". Given the schema of preds_df with columns prediction and actual, the correct evaluator setup will specify predictionCol="prediction" and labelCol="actual". Thus, the appropriate code block (Option C in your list) that uses RegressionEvaluator to compute the RMSE is the correct choice. This setup correctly measures the performance of the regression model using the predictions and actual outcomes from the DataFrame.
Reference:
Spark ML documentation (Using RegressionEvaluator to Compute RMSE).

NEW QUESTION # 75
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PDF DATABRICKS-MACHINE-LEARNING-ASSOCIATE CRAM EXAM, DATABRICKS-MACHINE-LEARNING-ASSOCIATE RELIABLE TEST CRAM

PDF Databricks-Machine-Learning-Associate Cram Exam, Databricks-Machine-Learning-Associate Reliable Test Cram