Databricks Certified Machine Learning Associate Exam Dumps & Practice Test Questions

Question No 1:

Which of the following machine learning algorithms commonly utilizes the bagging technique to improve model performance and reduce variance?

A. Gradient Boosted Trees
B. K-means
C. Random Forest
D. Linear Regression
E. Decision Tree

Answer: C. Random Forest

Explanation:

Bagging (Bootstrap Aggregating) is an ensemble technique designed to enhance the performance of machine learning models by reducing variance and preventing overfitting. It involves training multiple models on different subsets of the training data, which are created by bootstrapping (sampling with replacement), and then aggregating the predictions from all models to make a final prediction. This technique stabilizes the model’s predictions by averaging the output (for regression) or using majority voting (for classification).

Random Forest is the algorithm that commonly uses the bagging technique. It extends the decision tree algorithm by creating an ensemble of decision trees, each trained on a different bootstrap sample of the data. The results of all the trees are combined to improve the overall performance of the model and reduce the risk of overfitting, especially compared to a single decision tree.

Here's why the other options don’t use bagging:

• A. Gradient Boosted Trees: This is a boosting technique, not a bagging technique. In boosting, models are built sequentially, with each new model attempting to correct the errors of the previous model. This is different from the parallel nature of bagging.

• B. K-means: K-means is a clustering algorithm that does not use any ensemble techniques like bagging. It assigns points to clusters based on proximity to centroids, and it does not rely on bootstrapping or aggregating multiple models.

• D. Linear Regression: Linear regression is a single-model technique that does not involve bagging or any ensemble methods. It simply fits a linear equation to data, aiming to minimize the error in prediction.

• E. Decision Tree: A single decision tree does not use bagging. It is a standalone model that makes decisions based on binary splits of the features. Bagging is employed in Random Forests, which involve training multiple trees independently on bootstrapped samples.

Conclusion: The correct answer is C. Random Forest, as it is the algorithm that utilizes bagging to improve performance and reduce variance.

Question No 2:

In Spark ML, the initial approach to solving the linear regression problem involves matrix decomposition. However, this approach does not scale efficiently when dealing with large datasets, especially when the number of variables is high. To overcome this limitation and distribute the training of a linear regression model effectively over large datasets, Spark ML employs an alternative method.

Which of the following techniques does Spark ML use to distribute the training of a linear regression model in such scenarios?

A. Logistic regression
B. Spark ML cannot distribute linear regression training
C. Iterative optimization
D. Least-squares method
E. Singular value decomposition

Answer: C. Iterative optimization

Explanation:

In Spark ML, iterative optimization is used to scale linear regression models for large datasets with many features. This approach is more efficient than traditional matrix decomposition techniques, which struggle with the computational complexity when the dataset is large or when there are many variables.

Here’s why iterative optimization is used:

• Matrix Decomposition and Scaling Issues: While matrix decomposition (like the least squares method) can work for small to medium-sized datasets, it becomes computationally expensive and inefficient as the size of the data and the number of features increases. Decomposing large matrices requires substantial memory and computation power, making it unsuitable for large-scale data processing.

• Iterative Optimization: Spark ML uses algorithms like Stochastic Gradient Descent (SGD) or L-BFGS (Limited-memory Broyden–Fletcher–Goldfarb–Shanno) to solve the linear regression problem. These methods are iterative, meaning that they gradually adjust the model’s parameters to minimize the error (or loss function), which makes them well-suited for large datasets and distributed computing environments. This method allows for parallel processing, where each node in the Spark cluster can work on different parts of the data simultaneously, making it scalable.

• Benefits: The iterative approach breaks the problem down into smaller parts and solves it incrementally, making it more efficient for large datasets. This reduces the memory footprint compared to matrix decomposition and allows Spark to distribute the computation across multiple nodes.

Here’s why the other options are incorrect:

• A. Logistic Regression: Logistic regression is used for classification tasks, not regression tasks. While Spark ML supports logistic regression, it is unrelated to the linear regression training process described in the question.

• B. Spark ML cannot distribute linear regression training: This statement is false. Spark ML does indeed support distributed linear regression training through iterative optimization.

• D. Least-squares method: The least-squares method is central to linear regression but doesn’t scale well for large datasets with many features. Spark ML avoids direct reliance on least squares in favor of iterative methods.

• E. Singular value decomposition: Singular value decomposition (SVD) is useful in certain linear algebra tasks, like dimensionality reduction or matrix factorization, but it’s not the method used by Spark ML to scale linear regression.

Conclusion: The correct answer is C. Iterative optimization, as Spark ML uses this approach to efficiently train linear regression models on large datasets.

Question No 3:

A machine learning engineer is in the process of converting a decision tree model from scikit-learn (sklearn) to Apache Spark ML. Despite using identical datasets and specifying the same hyperparameters manually, they observe that the results of the two models differ.

Which of the following explains why the decision tree models from sklearn and Spark ML may yield different results?

A. Spark ML decision trees evaluate every feature variable during the splitting process.
B. Spark ML decision trees automatically prune overfit trees.
C. Spark ML decision trees evaluate more split candidates during the splitting process.
D. Spark ML decision trees test a random sample of feature variables during the splitting process.
E. Spark ML decision trees evaluate binned feature values as candidate splits.

Answer:
D. Spark ML decision trees test a random sample of feature variables during the splitting process.

Explanation:

While both scikit-learn and Spark ML decision trees implement similar decision tree algorithms, there are key differences in the way they are built. One of the main differences lies in how they handle feature selection during the tree-building process.

• Option A: Incorrect. Spark ML does not evaluate every feature at each split. Instead, it uses a random feature subset for split evaluation, which can lead to different tree structures compared to scikit-learn.

• Option B: Incorrect. Spark ML decision trees do not automatically prune trees by default. Pruning must be explicitly configured, just like in scikit-learn. Without pruning, both libraries would create fully grown trees.

• Option C: Incorrect. While Spark ML may evaluate a different number of split candidates depending on the configuration (e.g., based on random subsets of features), it does not inherently evaluate more candidates than scikit-learn. The behavior is more about random feature selection rather than the number of splits.

• Option D: Correct. Spark ML decision trees use random feature selection during the splitting process, meaning that at each node, a random subset of features is chosen for evaluation. This is different from scikit-learn, where all features are typically evaluated at each split. This randomness in feature selection can lead to different tree structures and results.

• Option E: Incorrect. Spark ML does bin continuous features to speed up computations, but this does not directly explain the differences in model behavior between scikit-learn and Spark ML. The key difference lies in how features are selected during splits.

In conclusion, the key difference is random feature selection in Spark ML, which causes variations in the tree structure and results compared to scikit-learn’s more deterministic approach.

Question No 4:

A data scientist is using MLflow to track machine learning experiments. As part of each experiment, they are performing hyperparameter tuning. The data scientist wants to create a parent run for the entire hyperparameter tuning process and a child run for each unique combination of hyperparameter values.

Both the parent and child runs are manually initiated using mlflow.start_run(). How can the data scientist organize the MLflow runs to achieve this structure?

A. Turn on Databricks Autologging
B. Specify nested=True when starting the child run for each unique combination of hyperparameter values
C. Start each child run inside the parent run's indented code block using mlflow.start_run()
D. Start each child run with the same experiment ID as the parent run
E. Specify nested=True when starting the parent run for the tuning process

Answer:
C. Start each child run inside the parent run's indented code block using mlflow.start_run()

Explanation:

In MLflow, organizing runs with a parent-child relationship is crucial for structuring hyperparameter tuning experiments. Here's how you can achieve this:

• Option A: Incorrect. Databricks Autologging is used to automatically log parameters, metrics, and models but does not establish parent-child relationships between runs. It does not address the organization of runs for hyperparameter tuning.

• Option B: Incorrect. There is no nested=True argument for the mlflow.start_run() function. This would not create the desired parent-child relationship.

• Option C: Correct. The correct way to organize the runs is to start the parent run first using mlflow.start_run(), and then nest the child runs inside the parent run by calling mlflow.start_run() within the parent run’s block of code. This ensures that the child runs are explicitly logged as part of the parent run. Each child run represents a unique combination of hyperparameters, and MLflow tracks this hierarchy effectively.

• Option D: Incorrect. Starting each child run with the same experiment ID as the parent run does not establish a parent-child relationship. The experiment ID simply groups runs together but does not create the hierarchy. The structure must be established by nesting the mlflow.start_run() calls.

• Option E: Incorrect. There is no nested=True argument for the mlflow.start_run() function. The hierarchical structure is achieved by manually nesting runs within each other, not by using this argument.

In conclusion, Option C is the correct method for creating a parent-child relationship between runs in MLflow. The parent run is initiated first, and each child run (corresponding to a specific hyperparameter configuration) is started within the scope of the parent run's indented code block.

Question No 5:

In the context of MLflow, you may want to view the notebook or script that was executed to create a particular MLflow run. This is often essential for understanding the exact steps and code that produced a specific result, ensuring reproducibility and transparency in your machine learning workflows.

Which of the following approaches can be used to view the notebook that was run to create an MLflow run?

A. Open the MLmodel artifact in the MLflow run page
B. Click the “Models” link in the row corresponding to the run in the MLflow experiment page
C. Click the “Source” link in the row corresponding to the run in the MLflow experiment page
D. Click the “Start Time” link in the row corresponding to the run in the MLflow experiment page

Correct Answer:
C. Click the “Source” link in the row corresponding to the run in the MLflow experiment page

Explanation:

In MLflow, when you run an experiment, various aspects of the run are logged, including parameters, metrics, and artifacts. One critical aspect for ensuring reproducibility is the ability to access the source code (e.g., a script or notebook) that was executed during the run.

• Option C: Click the “Source” link is the correct answer. The “Source” link will provide access to the script or notebook that was executed for the run. This is helpful for auditing the code, ensuring reproducibility, and understanding how the model was trained.

Let's review the other options:

• Option A: Open the MLmodel artifact in the MLflow run page — This allows you to view the model artifact itself, such as the saved model object, but it does not provide access to the source code or notebook.

• Option B: Click the “Models” link — This takes you to details about the model used in the run (e.g., model parameters, metrics), but not the source code or script.

• Option D: Click the “Start Time” link — This simply provides the timestamp for when the run was initiated. It does not give access to the source code or notebook associated with the run.

Thus, the “Source” link is the direct and correct way to view the notebook or script used for the run.

Question No 6:

A data scientist is building a machine learning pipeline using AutoML within the Databricks Machine Learning platform. While AutoML automates many steps of the machine learning workflow, some tasks still need to be handled outside the AutoML experiment.

Which of the following tasks will the data scientist need to complete outside of their AutoML experiment?

A. Model tuning
B. Model evaluation
C. Model deployment
D. Exploratory data analysis

Answer: C. Model deployment

Explanation:

AutoML tools like those provided by Databricks streamline many steps in the machine learning pipeline, but there are still tasks that need to be completed outside of the AutoML experiment itself. Let’s break down the tasks:

• Model Tuning (Option A):
  AutoML systems handle hyperparameter optimization and model tuning automatically as part of the experiment. These steps are typically integrated into the AutoML pipeline, and the system will search for the best-performing model during the experiment, so this task generally does not need to be done separately.

• Model Evaluation (Option B):
  After the models are trained, AutoML tools provide various evaluation metrics (such as accuracy, precision, recall, F1 score). This evaluation process is typically included as part of the AutoML pipeline, so additional work outside the experiment is generally unnecessary.

• Model Deployment (Option C):
  Once a model is trained and evaluated, it must be deployed into a production environment. This step involves making the model accessible to real-world applications, integrating it with existing systems, and ensuring it can serve predictions efficiently. Deployment is outside the scope of the AutoML experiment, as it requires further infrastructure setup, such as deployment pipelines, API endpoints, and integration with the broader application. Therefore, model deployment is the task that requires action outside the AutoML experiment.

• Exploratory Data Analysis (Option D):
  While AutoML systems typically handle basic data preprocessing, Exploratory Data Analysis (EDA) is an essential part of the machine learning process. However, EDA is usually done before running the AutoML experiment, not during it. Although some AutoML platforms allow basic data exploration, deeper EDA (such as visualizations, understanding distributions, and identifying patterns) is generally completed as a separate step before starting the experiment.

Thus, the only task that needs to be completed after the AutoML experiment is model deployment.

Summary:

• Question 5: To view the notebook or script used to create an MLflow run, click the "Source" link in the MLflow experiment page.

• Question 6: Model deployment is the task that requires action outside the AutoML experiment, as it involves making the model available in a production environment after it has been trained and evaluated.

Question No 7:

A machine learning engineer is tired of having to install the MLflow Python library every time they start a new cluster. They ask a senior machine learning engineer how they can configure their notebooks to load the MLflow library without having to manually install it each time. The senior engineer suggests that they use Databricks Runtime for Machine Learning (Databricks Runtime ML) to make this process easier.

Which of the following approaches correctly describes how the machine learning engineer can begin using Databricks Runtime for Machine Learning?

A. They can add a line enabling Databricks Runtime ML in their init script when creating their clusters.
B. They can check the Databricks Runtime ML box when creating their clusters.
C. They can select a Databricks Runtime ML version from the Databricks Runtime Version dropdown when creating their clusters.
D. They can set the runtime-version variable in their Spark session to “ml”.

Answer: C

Explanation:

Databricks Runtime for Machine Learning (Databricks Runtime ML) is a specialized environment designed for machine learning workflows. It comes pre-installed with a variety of popular machine learning libraries like MLflow, TensorFlow, PyTorch, scikit-learn, and XGBoost, which eliminates the need for users to manually install these libraries every time they start a new cluster.

To use Databricks Runtime ML, the machine learning engineer needs to ensure that their cluster is configured with the correct runtime that includes these libraries. The simplest and most straightforward way to do this is by selecting the appropriate Databricks Runtime ML version during the cluster creation process.

Why option C is correct:
The machine learning engineer can select a Databricks Runtime ML version from the Databricks Runtime Version dropdown when creating or updating their cluster. This ensures that the cluster is pre-configured with the necessary machine learning libraries, including MLflow, and will not require any additional manual installation.

Now, let’s examine the other options:

A. Adding a line to enable Databricks Runtime ML in an init script when creating their clusters:
This is not a typical approach. While init scripts are useful for custom configurations and installations, Databricks Runtime ML is not something that needs to be manually enabled via an init script. The init script is generally used for additional customizations, not for setting the runtime itself.

B. They can check the Databricks Runtime ML box when creating their clusters:
There is no "Databricks Runtime ML" checkbox. The configuration for Databricks Runtime ML is done through selecting the appropriate runtime version from the dropdown menu, not via a checkbox.

D. They can set the runtime-version variable in their Spark session to “ml”:
This is not a valid method for specifying the runtime. The runtime-version variable in a Spark session refers to Spark configurations and does not influence the cluster's underlying runtime environment for machine learning libraries. Databricks does not use this variable to configure the runtime version for machine learning.

In conclusion, selecting a Databricks Runtime ML version from the Databricks Runtime Version dropdown during the cluster creation process is the correct and simplest approach. This ensures that the engineer has all the necessary machine learning libraries pre-installed, including MLflow, for their workflows.

Question No 8:

What is the primary purpose of using MLflow in Databricks for machine learning workflows?

A) To perform feature selection and preprocessing automatically.
B) To log and track experiments, models, and hyperparameters.
C) To perform distributed data training using Spark clusters.
D) To schedule and automate the deployment of models to production.

Answer: B

Explanation:

MLflow is an open-source platform primarily used to manage the entire machine learning lifecycle. It helps track experiments, including model performance, hyperparameters, and metrics. By using MLflow, data scientists can log their experiments and model parameters, making it easier to reproduce results and track improvements over time. It supports multiple backends for experimentation, including local and remote storage. This helps streamline the management of models, facilitating better model version control and collaboration between teams.

Question No 9:

Which type of machine learning model is typically most effective when there is a high correlation between input features and the target variable?

A) K-Nearest Neighbors
B) Decision Trees
C) Linear Regression
D) Clustering Algorithms

Answer: C

Explanation:

Linear Regression is most effective when there is a linear relationship between input features and the target variable. It works well when there is high correlation between the features (independent variables) and the target (dependent variable). This model assumes that changes in the input features result in proportional changes in the output, making it ideal for predictive analysis where such correlations exist. Models like decision trees and K-Nearest Neighbors are more suitable for handling non-linear relationships or complex feature interactions.

Question No 10:

What is the primary advantage of using Databricks AutoML over manual model development?

A) It allows for advanced hyperparameter tuning without any user input.
B) It simplifies and automates the process of building and evaluating machine learning models.
C) It requires no data preprocessing steps from the user.
D) It generates detailed feature engineering code automatically.

Answer: B

Explanation:

Databricks AutoML provides a simplified and automated process for building machine learning models. It automates tasks like data preprocessing, model selection, and hyperparameter tuning while ensuring that models are trained efficiently. AutoML enables data scientists and engineers to accelerate their workflow by eliminating the need for manually selecting and tuning every model. However, it still provides users with transparency and flexibility for fine-tuning results when needed. The system often provides automated evaluations of different models, helping users choose the one with the best performance based on metrics like accuracy or F1-score.