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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

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.

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Databricks Certified Machine Learning Associate Exam Sample Questions (Q15-Q20):

NEW QUESTION # 15
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?

A. One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms.
B. One-hot encoding is not a common strategy for representing categorical feature variables numerically.
C. One-hot encoding is computationally intensive and should only be performed on small samples of training sets for individual machine learning problems.
D. One-hot encoding is dependent on the target variable's values which differ for each apaplication.

Answer: A

Explanation:
The suggestion not to one-hot encode categorical feature variables within the feature repository is justified because one-hot encoding can be problematic for some machine learning algorithms. Specifically, one-hot encoding increases the dimensionality of the data, which can be computationally expensive and may lead to issues such as multicollinearity and overfitting. Additionally, some algorithms, such as tree-based methods, can handle categorical variables directly without requiring one-hot encoding.
Reference:
Databricks documentation on feature engineering: Feature Engineering

NEW QUESTION # 16
A data scientist has developed a linear regression model using Spark ML and computed the predictions in a Spark DataFrame preds_df with the following schema:
prediction DOUBLE
actual DOUBLE
Which of the following code blocks can be used to compute the root mean-squared-error of the model according to the data in preds_df and assign it to the rmse variable?

Answer: C

Explanation:
To compute the root mean-squared-error (RMSE) of a linear regression model using Spark ML, the RegressionEvaluator class is used. The RegressionEvaluator is specifically designed for regression tasks and can calculate various metrics, including RMSE, based on the columns containing predictions and actual values.
The correct code block to compute RMSE from the preds_df DataFrame is:
regression_evaluator = RegressionEvaluator( predictionCol="prediction", labelCol="actual", metricName="rmse" ) rmse = regression_evaluator.evaluate(preds_df) This code creates an instance of RegressionEvaluator, specifying the prediction and label columns, as well as the metric to be computed ("rmse"). It then evaluates the predictions in preds_df and assigns the resulting RMSE value to the rmse variable.
Options A and B incorrectly use BinaryClassificationEvaluator, which is not suitable for regression tasks. Option D also incorrectly uses BinaryClassificationEvaluator.
Reference:
PySpark ML Documentation

NEW QUESTION # 17
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 identifying true positives and true negatives are equally important to the business problem.
B. The F1 score should be utilized over accuracy when there are greater than two classes in the target variable.
C. The F1 score should be utilized over accuracy when the number of actual positive cases is identical to the number of actual negative cases.
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 # 18
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?

A. Iterative optimization
B. Logistic regression
C. Least-squares method
D. Singular value decomposition

Answer: A

Explanation:
For large datasets, Spark ML uses iterative optimization methods to distribute the training of a linear regression model. Specifically, Spark MLlib employs techniques like Stochastic Gradient Descent (SGD) and Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimization to iteratively update the model parameters. These methods are well-suited for distributed computing environments because they can handle large-scale data efficiently by processing mini-batches of data and updating the model incrementally.
Reference:
Databricks documentation on linear regression: Linear Regression in Spark ML

NEW QUESTION # 19
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?

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

Answer: A

Explanation:
AutoML platforms, such as the one available in Databricks Machine Learning, streamline various stages of the machine learning pipeline including feature engineering, model selection, hyperparameter tuning, and model evaluation. However, exploratory data analysis (EDA) is typically performed outside the AutoML process. EDA involves understanding the dataset, visualizing distributions, identifying anomalies, and gaining insights into data before feeding it into a machine learning pipeline. This step is crucial for ensuring that the data is clean and suitable for model training but is generally done manually by the data scientist.
Reference
Databricks documentation on AutoML: https://docs.databricks.com/applications/machine-learning/automl.html

NEW QUESTION # 20
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