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

Topic	Details
Topic 1	Databricks Machine Learning: It covers sub-topics of AutoML, Databricks Runtime, Feature Store, and MLflow.
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	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.

Databricks Certified Machine Learning Associate Exam Sample Questions (Q64-Q69):

NEW QUESTION # 64
A machine learning engineer has grown tired of needing to install the MLflow Python library on each of their clusters. They ask a senior machine learning engineer how their notebooks can load the MLflow library without installing it each time. The senior machine learning engineer suggests that they use Databricks Runtime for Machine Learning.
Which of the following approaches 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 set the runtime-version variable in their Spark session to "ml".
C. They can select a Databricks Runtime ML version from the Databricks Runtime Version dropdown when creating their clusters.
D. They can check the Databricks Runtime ML box when creating their clusters.

Answer: C

Explanation:
The Databricks Runtime for Machine Learning includes pre-installed packages and libraries essential for machine learning and deep learning, including MLflow. To use it, the machine learning engineer can simply select an appropriate Databricks Runtime ML version from the "Databricks Runtime Version" dropdown menu while creating their cluster. This selection ensures that all necessary machine learning libraries, including MLflow, are pre-installed and ready for use, avoiding the need to manually install them each time.
Reference
Databricks documentation on creating clusters: https://docs.databricks.com/clusters/create.html

NEW QUESTION # 65
A team is developing guidelines on when to use various evaluation metrics for classification problems. The team needs to provide input on when to use the F1 score over accuracy.

Which of the following suggestions should the team include in their guidelines?

A. The F1 score should be utilized over accuracy when there are greater than two classes in the target variable.
B. The F1 score should be utilized over accuracy when the number of actual positive cases is identical to the number of actual negative cases.
C. The F1 score should be utilized over accuracy when identifying true positives and true negatives are equally important to the business problem.
D. The F1 score should be utilized over accuracy when there is significant imbalance between positive and negative classes and avoiding false negatives is a priority.

Answer: D

Explanation:
The F1 score is the harmonic mean of precision and recall and is particularly useful in situations where there is a significant imbalance between positive and negative classes. When there is a class imbalance, accuracy can be misleading because a model can achieve high accuracy by simply predicting the majority class. The F1 score, however, provides a better measure of the test's accuracy in terms of both false positives and false negatives.
Specifically, the F1 score should be used over accuracy when:
There is a significant imbalance between positive and negative classes.
Avoiding false negatives is a priority, meaning recall (the ability to detect all positive instances) is crucial.
In this scenario, the F1 score balances both precision (the ability to avoid false positives) and recall, providing a more meaningful measure of a model's performance under these conditions.
Reference:
Databricks documentation on classification metrics: Classification Metrics

NEW QUESTION # 66
A data scientist is using the following code block to tune hyperparameters for a machine learning model:

Which change can they make the above code block to improve the likelihood of a more accurate model?

A. Increase num_evals to 100
B. Change tpe.suggest to random.suggest
C. Change fmin() to fmax()
D. Change sparkTrials() to Trials()

Answer: A

Explanation:
To improve the likelihood of a more accurate model, the data scientist can increase num_evals to 100. Increasing the number of evaluations allows the hyperparameter tuning process to explore a larger search space and evaluate more combinations of hyperparameters, which increases the chance of finding a more optimal set of hyperparameters for the model.
Reference:
Databricks documentation on hyperparameter tuning: Hyperparameter Tuning

NEW QUESTION # 67
Which of the following machine learning algorithms typically uses bagging?

A. Random forest
B. Linear regression
C. K-means
D. Gradient boosted trees
E. Decision tree

Answer: A

Explanation:
Random Forest is a machine learning algorithm that typically uses bagging (Bootstrap Aggregating). Bagging involves training multiple models independently on different random subsets of the data and then combining their predictions. Random Forests consist of many decision trees trained on random subsets of the training data and features, and their predictions are averaged to improve accuracy and control overfitting. This method enhances model robustness and predictive performance.
Reference:
Ensemble Methods in Machine Learning (Understanding Bagging and Random Forests).

NEW QUESTION # 68
Which of the following machine learning algorithms typically uses bagging?

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

Answer: A

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 # 69
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