Databricks-Machine-Learning-Associate Dumps 2024 – New Databricks Databricks-Machine-Learning-Associate Exam Questions [Q10-Q26]

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Databricks-Machine-Learning-Associate Dumps 2024 – New Databricks Databricks-Machine-Learning-Associate Exam Questions

Free Databricks-Machine-Learning-Associate Braindumps Download Updated on Sep 23, 2024 with 76 Questions

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.

Q10. 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?

Gradient boosting is not a linear algebra-based algorithm which is required for parallelization

Gradient boosting requires access to all data at once which cannot happen during parallelization.

Gradient boosting calculates gradients in evaluation metrics using all cores which prevents parallelization.

Gradient boosting is an iterative algorithm that requires information from the previous iteration to perform the next step.

Q11. Which of the following statements describes a Spark ML estimator?

An estimator is a hyperparameter arid that can be used to train a model

An estimator chains multiple alqorithms toqether to specify an ML workflow

An estimator is a trained ML model which turns a DataFrame with features into a DataFrame with predictions

An estimator is an alqorithm which can be fit on a DataFrame to produce a Transformer

An estimator is an evaluation tool to assess to the quality of a model

Q12. 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?

One-hot encoding is not supported by most machine learning libraries.

One-hot encoding is dependent on the target variable’s values which differ for each application.

One-hot encoding is computationally intensive and should only be performed on small samples of training sets for individual machine learning problems.

One-hot encoding is not a common strategy for representing categorical feature variables numerically.

One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms.

Q13. Which of the Spark operations can be used to randomly split a Spark DataFrame into a training DataFrame and a test DataFrame for downstream use?

TrainValidationSplit

DataFrame.where

CrossValidator

TrainValidationSplitModel

DataFrame.randomSplit

Q14. 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?

Logistic regression

Spark ML cannot distribute linear regression training

Iterative optimization

Least-squares method

Singular value decomposition

Q15. Which of the following machine learning algorithms typically uses bagging?

Gradient boosted trees

K-means

Random forest

Linear regression

Decision tree

Q16. A data scientist is developing a machine learning pipeline using AutoML on Databricks Machine Learning.
Which of the following steps will the data scientist need to perform outside of their AutoML experiment?

Model tuning

Model evaluation

Model deployment

Exploratory data analysis

Q17. A data scientist has produced three new models for a single machine learning problem. In the past, the solution used just one model. All four models have nearly the same prediction latency, but a machine learning engineer suggests that the new solution will be less time efficient during inference.
In which situation will the machine learning engineer be correct?

When the new solution requires if-else logic determining which model to use to compute each prediction

When the new solution’s models have an average latency that is larger than the size of the original model

When the new solution requires the use of fewer feature variables than the original model

When the new solution requires that each model computes a prediction for every record

When the new solution’s models have an average size that is larger than the size of the original model

Q18. A machine learning engineer wants to parallelize the training of group-specific models using the Pandas Function API. They have developed the train_model function, and they want to apply it to each group of DataFrame df.
They have written the following incomplete code block:

Which of the following pieces of code can be used to fill in the above blank to complete the task?

applyInPandas

mapInPandas

predict

train_model

groupedApplyIn

Q19. A machine learning engineer is trying to scale a machine learning pipeline pipeline that contains multiple feature engineering stages and a modeling stage. As part of the cross-validation process, they are using the following code block:

A colleague suggests that the code block can be changed to speed up the tuning process by passing the model object to the estimator parameter and then placing the updated cv object as the final stage of the pipeline in place of the original model.
Which of the following is a negative consequence of the approach suggested by the colleague?

The model will take longer to train for each unique combination of hvperparameter values

The feature engineering stages will be computed using validation data

The cross-validation process will no longer be

The cross-validation process will no longer be reproducible

The model will be refit one more per cross-validation fold

Q20. 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?

One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms.

One-hot encoding is dependent on the target variable’s values which differ for each apaplication.

One-hot encoding is computationally intensive and should only be performed on small samples of training sets for individual machine learning problems.

One-hot encoding is not a common strategy for representing categorical feature variables numerically.

Q21. A machine learning engineer has been notified that a new Staging version of a model registered to the MLflow Model Registry has passed all tests. As a result, the machine learning engineer wants to put this model into production by transitioning it to the Production stage in the Model Registry.
From which of the following pages in Databricks Machine Learning can the machine learning engineer accomplish this task?

The home page of the MLflow Model Registry

The experiment page in the Experiments observatory

The model version page in the MLflow Model Registry

The model page in the MLflow Model Registry

Q22. A data scientist learned during their training to always use 5-fold cross-validation in their model development workflow. A colleague suggests that there are cases where a train-validation split could be preferred over k-fold cross-validation when k > 2.
Which of the following describes a potential benefit of using a train-validation split over k-fold cross-validation in this scenario?

A holdout set is not necessary when using a train-validation split

Reproducibility is achievable when using a train-validation split

Fewer hyperparameter values need to be tested when using a train-validation split

Bias is avoidable when using a train-validation split

Fewer models need to be trained when using a train-validation split

Q23. Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?

pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata

pandas API on Spark DataFrames are more performant than Spark DataFrames

pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata

pandas API on Spark DataFrames are less mutable versions of Spark DataFrames

Q24. 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?

Utilize the MinMaxScaler object to standardize the training data according to global minimum and maximum values

Utilize the MinMaxScaler object to standardize the test data according to global minimum and maximum values

Utilize a cross-validation process rather than a train-test split process to remove the need for standardizing data

Utilize the Pipeline API to standardize the training data according to the test data’s summary statistics

Utilize the Pipeline API to standardize the test data according to the training data’s summary statistics

Q25. A data scientist has produced two models for a single machine learning problem. One of the models performs well when one of the features has a value of less than 5, and the other model performs well when the value of that feature is greater than or equal to 5. The data scientist decides to combine the two models into a single machine learning solution.
Which of the following terms is used to describe this combination of models?

Bootstrap aggregation

Support vector machines

Bucketing

Ensemble learning

Stacking

Q26. A data scientist is using Spark SQL to import their data into a machine learning pipeline. Once the data is imported, the data scientist performs machine learning tasks using Spark ML.
Which of the following compute tools is best suited for this use case?

Single Node cluster

Standard cluster

SQL Warehouse

None of these compute tools support this task