MLA-C01 Online Practice Questions

Explanation:
Correct option:
Implement a hybrid model that combines the predictions of collaborative filtering, content-based filtering, and deep learning using a weighted average, where weights are based on model performance for different customer segments
via -
https://aws.amazon.com/blogs/machine-learning/efficiently-train-tune-and-deploy-custom-ensembles-using-amazon-sagemaker/
In bagging, data scientists improve the accuracy of weak learners by training several of them at once on multiple datasets. In contrast, boosting trains weak learners one after another. Stacking involves training a meta-model on the predictions of several base models. This approach can significantly improve performance because the meta-model learns to leverage the strengths of each base model while compensating for their weaknesses.
For the given use case, a hybrid model that combines the predictions of different models using a weighted average is the most appropriate approach. By assigning weights based on each model’s performance for specific customer segments, you can ensure that the recommendation system leverages the strengths of each model, providing more accurate and personalized recommendations across all customer segments.
Incorrect options:
Use a bagging approach to train multiple instances of the deep learning model on different subsets of the data and average their predictions to improve overall performance - Bagging is typically used to reduce variance and improve stability for a single type of model, like decision trees. However, it does not directly address the need to combine different models that perform well in different scenarios, which is key for your recommendation system.
Apply boosting by sequentially training the collaborative filtering, content-based filtering, and deep learning models, where each model corrects the errors of the previous one - Boosting is useful for improving the performance of weak learners by training them sequentially, but it is not designed to combine different types of models like collaborative filtering, content-based filtering, and deep learning, each of which has strengths in different areas.
Use stacking, where the predictions from the collaborative filtering and content-based filtering models are fed into a deep learning model as inputs, allowing the deep learning model to make the final recommendation - Stacking is a powerful technique for combining models, but in this case, the deep learning model is not necessarily better suited as a meta-model for making the final recommendation. A weighted hybrid model is more effective when different models excel in different contexts, as it allows you to balance their contributions based on performance.
References:
https://aws.amazon.com/blogs/machine-learning/efficiently-train-tune-and-deploy-custom-ensembles-using-amazon-sagemaker/
https://aws.amazon.com/what-is/boosting/

Explanation:
Correct option:
The model can recreate new animal images that were not in the training dataset
Generative artificial intelligence (generative AI) is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music. AI technologies attempt to mimic human intelligence in nontraditional computing tasks like image recognition, natural language processing (NLP), and translation.
Generative models can analyze animal images to record variables like different ear shapes, eye shapes, tail features, and skin patterns. They learn features and their relations to understand what different animals look like in general. They can then recreate new animal images that were not in the training set.
via - https://aws.amazon.com/what-is/generative-ai/
Incorrect options:
The model can classify a single species of animals such as cats
The model can classify multiple species of animals such as cats, dogs, etc
Traditional machine learning models were discriminative or focused on classifying data points. They attempted to determine the relationship between known and unknown factors. For example, they look at images―known data like pixel arrangement, line, color, and shape―and map them to words―the unknown factor. Only discriminative models can act as single-class classifiers or multi-class classifiers. Therefore, both these options are incorrect.
The model can identify any image from the training dataset - This option has been added as a distractor. A generative model is not an image-matching algorithm. It cannot identify an image from the training dataset.
Reference: https://aws.amazon.com/what-is/generative-ai/

Explanation:
Correct option:
An AUC close to 1.0 indicates that the model has excellent discriminatory power, effectively distinguishing between defaulters and non-defaulters
Area Under the (Receiver Operating Characteristic) Curve (AUC) represents an industry-standard accuracy metric for binary classification models. AUC measures the ability of the model to predict a higher score for positive examples as compared to negative examples. Because it is independent of the score cut-off, you can get a sense of the prediction accuracy of your model from the AUC metric without picking a threshold.
The AUC metric returns a decimal value from 0 to 1. AUC values near 1 indicate an ML model that is highly accurate. Values near 0.5 indicate an ML model that is no better than guessing at random. Values near 0 are unusual to see, and typically indicate a problem with the data. Essentially, an AUC near 0 says that the ML model has learned the correct patterns, but is using them to make predictions that are flipped from reality ('0's are predicted as '1's and vice versa). The ROC curve is the plot of the true positive rate (TPR) against the false positive rate (FPR) at each threshold setting.
via -
https://aws.amazon.com/blogs/machine-learning/is-your-model-good-a-deep-dive-into-amazon-sagemaker-canvas-advanced-metrics/
An AUC close to 1.0 signifies that the model has excellent discriminatory power, meaning it can effectively distinguish between the positive class (defaulters) and the negative class (non-defaulters) across all thresholds. This is desirable in a classification task, especially in scenarios with class imbalance.
via - https://docs.aws.amazon.com/machine-learning/latest/dg/binary-model-insights.html
Incorrect options:
A ROC curve that is close to the diagonal line (AUC ~ 0.5) indicates that the model performs well across all thresholds - A ROC curve close to the diagonal line (AUC ~ 0.5) indicates that the model has no discriminatory power and is performing no better than random guessing. This suggests poor model performance, not that the model performs well across all thresholds.
A ROC curve that is closer to the top-left corner of the plot (AUC ~ 1) shows that the model is overfitting, and its predictions are too optimistic - A ROC curve closer to the top-left corner of the plot (AUC closer to 1.0) indicates strong model performance, not overfitting. Overfitting is typically identified by other indicators, such as a large gap between training and validation performance, not by the shape of the ROC curve alone.
An AUC close to 0 indicates that the model is highly accurate, correctly classifying almost all instances of defaulters and non-defaulters - An AUC close to 0 is problematic, as it indicates that the model is consistently making incorrect predictions (i.e., it classifies negatives as positives and vice versa). A high AUC (close to 1) is what signifies strong model performance.
References:
https://docs.aws.amazon.com/machine-learning/latest/dg/binary-model-insights.html https://aws.amazon.com/blogs/machine-learning/creating-high-quality-machine-learning-models-for-financial-services-using-amazon-sagemaker-autopilot/ https://aws.amazon.com/blogs/machine-learning/is-your-model-good-a-deep-dive-into-amazon-sagemaker-canvas-advanced-metrics/

Explanation:
Correct options:
Supervised learning algorithms train on sample data that specifies both the algorithm's input and output. For example, the data could be images of handwritten numbers that are annotated to indicate which numbers they represent. Given sufficient labeled data, the supervised learning system would eventually recognize the clusters of pixels and shapes associated with each handwritten number.
via -
https://aws.amazon.com/compare/the-difference-between-machine-learning-supervised-and-unsupervised/
Linear regression
Linear regression refers to supervised learning models that, based on one or more inputs, predict a value from a continuous scale. An example of linear regression is predicting a house price. You could predict a house’s price based on its location, age, and number of rooms after you train a model on a set of historical sales training data with those variables.
Neural network
A neural network solution is a more complex supervised learning technique. To produce a given outcome, it takes some given inputs and performs one or more layers of mathematical transformation based on adjusting data weightings. An example of a neural network technique is predicting a digit from a handwritten image.
Incorrect options:
Document classification - This is an example of semi-supervised learning. Semi-supervised learning is when you apply both supervised and unsupervised learning techniques to a common problem. This technique relies on using a small amount of labeled data and a large amount of unlabeled data to train systems. When applying categories to a large document base, there may be too many documents to physically label. For example, these could be countless reports, transcripts, or specifications. Training on the unlabeled data helps identify similar documents for labeling.
Association rule learning - This is an example of unsupervised learning. Association rule learning techniques uncover rule-based relationships between inputs in a dataset. For example, the Apriori algorithm conducts market basket analysis to identify rules like coffee and milk often being purchased together.
Clustering - Clustering is an unsupervised learning technique that groups certain data inputs, so they may be categorized as a whole. There are various types of clustering algorithms depending on the input data. An example of clustering is identifying different types of network traffic to predict potential security incidents.
References:
https://aws.amazon.com/what-is/machine-learning/
https://aws.amazon.com/compare/the-difference-between-machine-learning-supervised-and-unsupervised/

Explanation:
Correct option:
A token is a sequence of characters that a model can interpret or predict as a single unit of meaning, whereas, an embedding is a vector of numerical values that represents condensed information obtained by transforming input into that vector
Embedding C The process of condensing information by transforming input into a vector of numerical values, known as the embeddings, in order to compare the similarity between different objects by using a shared numerical representation. For example, sentences can be compared to determine the similarity in meaning, images can be compared to determine visual similarity, or text and image can be compared to see if they're relevant to each other.
Token C A sequence of characters that a model can interpret or predict as a single unit of meaning. For example, with text models, a token could correspond not just to a word, but also to a part of a word with grammatical meaning (such as "-ed"), a punctuation mark (such as "?"), or a common phrase (such as "a lot").
via - https://docs.aws.amazon.com/bedrock/latest/userguide/key-definitions.html
Incorrect options:
Both token and embedding refer to a sequence of characters that a model can interpret or predict as a single unit of meaning
An embedding is a sequence of characters that a model can interpret or predict as a single unit of meaning, whereas, a token is a vector of numerical values that represents condensed information obtained by transforming input into that vector
Both token and embedding refer to a vector of numerical values that represents condensed information obtained by transforming input into that vector
These three options contradict the explanation provided above, so these options are incorrect.