Free PDF 2025 Databricks-Machine-Learning-Associate: Databricks Certified Machine Learning Associate Exam–The Best Test Online

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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	Databricks Machine Learning: It covers sub-topics of AutoML, Databricks Runtime, Feature Store, and MLflow.
Topic 3	ML Workflows: The topic focuses on Exploratory Data Analysis, Feature Engineering, Training, Evaluation and Selection.
Topic 4	Scaling ML Models: This topic covers Model Distribution and Ensembling Distribution.

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Databricks Certified Machine Learning Associate Exam Sample Questions (Q20-Q25):

NEW QUESTION # 20
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 MinMaxScaler object to standardize the test data according to global minimum and maximum values
B. Utilize the Pipeline API to standardize the test data according to the training data's summary statistics
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 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 # 21
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?

A. Gradient boosting is an iterative algorithm that requires information from the previous iteration to perform the next step.
B. Gradient boosting is not a linear algebra-based algorithm which is required for parallelization
C. Gradient boosting calculates gradients in evaluation metrics using all cores which prevents parallelization.
D. Gradient boosting requires access to all data at once which cannot happen during parallelization.

Answer: A

Explanation:
Gradient boosting is fundamentally an iterative algorithm where each new tree is built based on the errors of the previous ones. This sequential dependency makes it difficult to parallelize the training of trees in gradient boosting, as each step relies on the results from the preceding step. Parallelization in this context would undermine the core methodology of the algorithm, which depends on sequentially improving the model's performance with each iteration.
Reference:
Machine Learning Algorithms (Challenges with Parallelizing Gradient Boosting).
Gradient boosting is an ensemble learning technique that builds models in a sequential manner. Each new model corrects the errors made by the previous ones. This sequential dependency means that each iteration requires the results of the previous iteration to make corrections. Here is a step-by-step explanation of why this makes parallelization challenging:
Sequential Nature: Gradient boosting builds one tree at a time. Each tree is trained to correct the residual errors of the previous trees. This requires the model to complete one iteration before starting the next.
Dependence on Previous Iterations: The gradient calculation at each step depends on the predictions made by the previous models. Therefore, the model must wait until the previous tree has been fully trained and evaluated before starting to train the next tree.
Difficulty in Parallelization: Because of this dependency, it is challenging to parallelize the training process. Unlike algorithms that process data independently in each step (e.g., random forests), gradient boosting cannot easily distribute the work across multiple processors or cores for simultaneous execution.
This iterative and dependent nature of the gradient boosting process makes it difficult to parallelize effectively.
Reference
Gradient Boosting Machine Learning Algorithm
Understanding Gradient Boosting Machines

NEW QUESTION # 22
Which statement describes a Spark ML transformer?

A. A transformer is an algorithm which can transform one DataFrame into another DataFrame
B. A transformer chains multiple algorithms together to transform an ML workflow
C. A transformer is a learning algorithm that can use a DataFrame to train a model
D. A transformer is a hyperparameter grid that can be used to train a model

Answer: A

Explanation:
In Spark ML, a transformer is an algorithm that can transform one DataFrame into another DataFrame. It takes a DataFrame as input and produces a new DataFrame as output. This transformation can involve adding new columns, modifying existing ones, or applying feature transformations. Examples of transformers in Spark MLlib include feature transformers like StringIndexer, VectorAssembler, and StandardScaler.
Reference:
Databricks documentation on transformers: Transformers in Spark ML

NEW QUESTION # 23
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 double or more than double the number of evaluations.
B. Change the number of compute nodes and the number of evaluations to be much larger but equal.
C. Change the number of compute nodes to be half or less than half of the number of evaluations.
D. Change the iterative optimization algorithm used to facilitate the tuning process.

Answer: D

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 # 24
A machine learning engineer is trying to scale a machine learning pipeline by distributing its single-node model tuning process. After broadcasting the entire training data onto each core, each core in the cluster can train one model at a time. Because the tuning process is still running slowly, the engineer wants to increase the level of parallelism from 4 cores to 8 cores to speed up the tuning process. Unfortunately, the total memory in the cluster cannot be increased.
In which of the following scenarios will increasing the level of parallelism from 4 to 8 speed up the tuning process?

A. When the data is particularly long in shape
B. When the data is particularly wide in shape
C. When the model is unable to be parallelized
D. When the tuning process in randomized
E. When the entire data can fit on each core

Answer: E

Explanation:
Increasing the level of parallelism from 4 to 8 cores can speed up the tuning process if each core can handle the entire dataset. This ensures that each core can independently work on training a model without running into memory constraints. If the entire dataset fits into the memory of each core, adding more cores will allow more models to be trained in parallel, thus speeding up the process.
Reference:
Parallel Computing Concepts

NEW QUESTION # 25
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FREE PDF 2025 DATABRICKS-MACHINE-LEARNING-ASSOCIATE: DATABRICKS CERTIFIED MACHINE LEARNING ASSOCIATE EXAM–THE BEST TEST ONLINE

Free PDF 2025 Databricks-Machine-Learning-Associate: Databricks Certified Machine Learning Associate Exam–The Best Test Online