Databricks Machine Learning Associate Exam Dumps 2024
Databricks Machine Learning Associate Exam Dumps 2024
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Question 1:
When should feature scaling techniques like Min-Max scaling be applied in Spark ML workflows?
A. Feature scaling is not necessary in Spark ML
B. Before data preprocessing
C. After model training
D. Before model training
Answer: D
Explanation:
Before model training.
Feature scaling techniques, such as Min-Max scaling, should be applied in Spark ML workflows before model training.
Feature scaling is necessary when using machine learning algorithms that are sensitive to the scale of features, such as algorithms based on distance metrics or optimization algorithms like Gradient Descent.
Scaling features ensures that they are on a similar scale, preventing any particular feature from dominating the learning process.
Min-Max scaling, for example, scales features to a specific range (e.g., between 0 and 1), maintaining the relative relationships between feature values while bringing them to a standardized scale. Therefore, it is a common practice to apply feature scaling as a preprocessing step before training machine learning models in Spark ML workflows.
Question 2:
Your machine learning project involves predicting numerical values based on input features, and you need a model capable of capturing complex relationships in the data. Which algorithm, supported by Databricks MLlib, is suitable for capturing complex nonlinear patterns?
A. Linear Regression
B. Decision Trees
C. Support Vector Machines
D. Gradient Boosting
Answer: D
Explanation:
For capturing complex nonlinear patterns in the data, Gradient Boosting is a suitable algorithm.
Gradient Boosting is an ensemble learning technique that builds a series of weak learners (typically decision trees) sequentially, with each one correcting the errors of the previous one.
This allows the model to capture intricate relationships in the data and improve predictive performance.
Databricks MLlib supports Gradient Boosting as an algorithm for regression tasks, making it a viable choice for predicting numerical values based on input features in situations where complex nonlinear patterns are present in the data.
Question 3:
In a distributed computing system, what does data co-location involve?
A. Distributing Data Across Nodes
B. Storing Related Data Together
C. Synchronizing Data Processing
D. Minimizing Task Complexity
Answer: B
Explanation:
Storing Related Data Together
In a distributed computing system, data co-location involves storing related or correlated data together on the same node or set of nodes within the system.
This technique is used to optimize data access patterns and reduce the need for data movement across nodes during computation.
Data co-location is particularly beneficial for workloads that involve frequent interactions or computations on related pieces of data.
By keeping related data together, the system can minimize the need for inter-node communication, leading to improved performance and reduced latency.
While distributing data across nodes is a broader concept related to data partitioning and distribution, data co-location specifically emphasizes the practice of keeping related data in close proximity to each other within the distributed system.
Question 4:
What aspect of machine learning tasks is optimized by Databricks Runtime for Machine Learning?
A. Model deployment
B. Data visualization
C. Data preprocessing
D. Performance
Answer: D
Explanation:
Databricks Runtime for Machine Learning is optimized for enhancing the performance of machine learning tasks.
It provides a set of pre-configured libraries, frameworks, and optimizations tailored specifically for efficient and scalable execution of machine learning workloads.
This optimization encompasses aspects such as distributed training, data preprocessing, and other machine learning-specific tasks, aiming to streamline the overall performance of machine learning workflows within the Databricks platform.
While Databricks as a platform supports various aspects of data processing, analytics, and visualization, Databricks Runtime for Machine Learning focuses on optimizing the performance of machine learning tasks.
Question 5:
What does Databricks Runtime for Machine Learning optimize for?
A. Cluster cost
B. General data processing
C. Machine learning tasks
D. Visualization
Answer: C
Explanation:
Databricks Runtime for Machine Learning (Databricks Runtime ML) optimizes for machine learning tasks.
Here‘s why: Machine learning tasks: This is the primary focus of Databricks Runtime ML. It includes pre-installed libraries, frameworks, and configurations specifically tailored for machine learning workflows, such as TensorFlow, PyTorch, scikit-learn, XGBoost, and Horovod.
It also offers optimizations for GPU usage and distributed deep learning. Cluster cost: While cost efficiency is important, Databricks Runtime ML primarily focuses on providing a high-performance environment for machine learning tasks.
It may not be the most cost-effective option for general data processing tasks that don‘t require specialized libraries or configurations.
General data processing: While Databricks Runtime ML can be used for general data processing tasks, it is not optimized for them. Other Databricks runtime options, such as Databricks Runtime for Light workloads, may be more suitable for general data processing.
Visualization: While Databricks Runtime ML includes visualization libraries like matplotlib and seaborn, it is not specifically optimized for visualization tasks. Other tools like Databricks Workspace may be more appropriate for interactive data visualization.
Therefore, considering the pre-built libraries, frameworks, and optimizations tailored for machine learning, machine learning tasks is the most accurate choice for what Databricks Runtime for Machine Learning optimizes for.
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Question 6:
A data scientist is working on a machine learning project in Databricks and needs to share the trained model with a team member for further evaluation.
What is the recommended way to package and share the machine learning model using MLflow?
A. Save the model as a pickled Python object.
B. Export the model as a CSV file.
C. Use MLflow to log and save the model artifacts, then share the MLflow run ID.
D. Share the entire Databricks notebook containing the model code.
Answer: C
Explanation:
The recommended way to package and share the machine learning model using MLflow is:
C. Use MLflow to log and save the model artifacts, then share the MLflow run ID.
Here‘s why:
A. Pickled Python object: This format is specific to Python and not portable across different environments. Sharing it might require additional context for the team member to understand and use.
B. CSV: Models are not typically stored in CSV format. This is suitable for storing data but not complex model structures.
C. MLflow run ID: MLflow provides a standardized way to package models with their associated metadata, metrics, and dependencies. Sharing the run ID uniquely identifies the model and allows the team member to easily retrieve and reproduce it using mlflow load_model or other MLflow tools.
D. Sharing the entire notebook: While it provides the model code, it doesn‘t guarantee a readily usable environment for the team member. They might need to install dependencies, configure settings, and navigate the notebook to find the relevant sections.
Therefore, using MLflow and sharing the run ID offers the most efficient, portable, and reproducible way to share the model for evaluation. The team member can easily access and utilize the model without needing to set up a specific environment or deal with complexities like pickled objects or notebook navigation.
Question 7:
What is the primary purpose of grid search in hyperparameter tuning for Spark ML algorithms?
A. To test every possible combination of hyperparameters
B. To select hyperparameters randomly
C. To limit the number of iterations in model training
D. To increase model complexity
Answer: A
Explanation:
To test every possible combination of hyperparameters.
The primary purpose of grid search in hyperparameter tuning is to systematically explore a predefined set, or grid, of hyperparameter combinations for a machine learning algorithm.
It tests every possible combination within the specified grid to find the set of hyperparameters that yields the best performance for the given task.
Grid search is a common approach to hyperparameter tuning, allowing practitioners to search across a range of hyperparameter values efficiently.
By evaluating the model‘s performance for each combination in the grid, grid search helps identify the optimal hyperparameters that result in the best model performance on a validation set or through cross-validation.
Question 8:
In a distributed computing system, what does data serialization involve?
A. Data Compression
B. Data Encoding for Transmission
C. Converting Data to Byte Streams
D. Data Encryption
Answer: C
Explanation:
Converting Data to Byte Streams.
In a distributed computing system, data serialization involves converting data into a byte stream format.
This process is necessary for transmitting data across a network or storing it in a format that can be easily reconstructed on different nodes or systems.
Serialization is commonly used in distributed computing to enable the efficient and standardized transfer of data between different components or nodes.
While compression is related to reducing the size of data, encoding for transmission involves representing data in a specific format for communication, and encryption focuses on securing data, data serialization specifically deals with converting data into a format that can be transmitted as a sequence of bytes, allowing for efficient communication between distributed components.
Question 9:
What is the primary purpose of early stopping techniques in Spark ML model training?
A. To slow down the training process
B. To prevent the model from learning
C. To stop model training when the validation performance stops improving
D. To increase the learning rate
Answer: C
Explanation:
To stop model training when the validation performance stops improving.
The primary purpose of early stopping techniques in Spark ML model training is to stop the training process when the validation performance stops improving.
Early stopping is a regularization technique that monitors the performance of the model on a validation dataset during training.
If the validation performance ceases to improve or starts to degrade, early stopping interrupts the training process to prevent overfitting and ensure that the model generalizes well to new, unseen data.
By stopping the training early when further iterations are unlikely to improve generalization, early stopping helps avoid overfitting and contributes to the development of a more effective and robust model.
Question 10:
Your team is working on a machine learning project that requires processing multimedia data in a distributed computing environment. What technique allows efficient indexing and retrieval of multimedia data for analysis?
A. Multimedia Clustering
B. Multimedia Indexing
C. Multimedia Partitioning
D. Multimedia Compression
Answer: B
Explanation:
Multimedia Indexing.
In a machine learning project that involves processing multimedia data in a distributed computing environment, efficient indexing and retrieval of multimedia data for analysis are crucial.
Multimedia Indexing is the technique that allows for the organization and retrieval of multimedia content based on various features, such as visual, audio, or text-based information.
Multimedia Indexing involves creating indexes or representations that enable efficient search and retrieval of multimedia data, facilitating analysis and modeling tasks.
It allows for the identification and retrieval of specific multimedia elements based on the content characteristics.
While clustering, partitioning, and compression are relevant techniques in multimedia processing, Multimedia Indexing specifically addresses the organization and retrieval aspects required for efficient analysis in a distributed computing environment.
For a full set of 420+ questions. Go to
https://skillcertpro.com/product/databricks-machine-learning-associate-exam-questions/
SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
SkillCertPro updates exam questions every 2 weeks.
You will get life time access and life time free updates
SkillCertPro assures 100% pass guarantee in first attempt.