Amazon AWS Certified Machine Learning - Specialty Exam Practice Questions (P. 3)

GPT-4o - Answer

Inserting an Amazon Kinesis Data Analytics stream downstream of the existing Kinesis Data Firehose setup to handle simple transformations involves minimal development effort. This approach utilizes the ongoing ingestion process, leveraging Kinesis Data Analytics' capability to perform real-time processing using SQL on streaming data, without the need for a significant alteration to existing infrastructure or processes for daily updates. This setup not only maintains data flow efficiency but also directly integrates with Amazon S3 for output.

GPT-4o - Answer

The softmax function is the correct choice because it converts the outputs of the final dense layer into probabilities that sum to one, which are interpretable as the likelihood of the input belonging to each of the 10 classes. This function is especially suitable for multi-class classification problems such as this, where each output node corresponds to a class. Other options like dropout, smooth L1 loss, and ReLU serve different purposes such as regularization and activation but do not provide output in the form of a probability distribution suitable for classification.

GPT-4o - Answer

Residual plots are the optimal choice when you need to understand if a regression model is predominantly overestimating or underestimating its target values. These plots illustrate the residuals on the vertical axis (errors between predicted and actual values) and predicted values or other variables on the horizontal axis. By visually examining these plots, any bias towards positive or negative residuals clearly indicates whether the model tends to overestimate or underestimate. Unlike RMSE, which only provides the magnitude of errors without their direction, residual plots offer detailed, directional insight, critical for refining model accuracy.

GPT-4o - Answer

For the given scenario, where a Machine Learning Specialist is looking to reconfigure hyperparameter ranges to optimize training times, consider the role of visualization types in achieving this. The t-SNE visualization in option B, although primarily a tool for reducing dimensionality and not directly for tuning hyperparameters, facilitates understanding data structure, which indirectly influences hyperparameter setting by revealing patterns in high-dimensional data. Understanding these patterns can guide adjustments in hyperparameter ranges more effectively, making B a viable choice if seen from this broader perspective. However, considering user feedback, option D focusing on the relationship between tree depth and performance metrics provides a more straightforward approach to optimizing a specific hyperparameter, though it's narrower in scope.

GPT-4o - Answer

For the data preprocessing required in a Word2Vec model, it is crucial to begin with text normalization to manage various word forms and improve model accuracy. This typically involves converting all text to lowercase, removing common words—known as stop words—that might skew the analysis, and tokenizing sentences into distinct words. These steps significantly enhance the uniformity and quality of the input data, facilitating more effective word embedding computations by focusing on the meaningful content of the text.

GPT-4o - Answer

When training and evaluating machine learning models that demonstrate fast decrement in training error but poor performance on unseen test images, this typically indicates overfitting. To address this, incorporating L2 regularization is highly effective as it places constraints on the magnitude of the parameters, effectively penalizing large weights to reduce model complexity and improve generalization to new data. Additionally, implementing data augmentation can artificially expand the size of the training dataset by generating diverse scenarios, which enhances the model's ability to generalize rather than memorize the training data. Both techniques are practical for mitigating overfitting in neural network models.