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Amazon AWS Certified Machine Learning - Specialty MLS-C01 Prüfungsfragen mit Lösungen (Q150-Q155):

150. Frage
A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant will default on a credit card payment. The company has collected data from a large number of sources with thousands of raw attributes. Early experiments to train a classification model revealed that many attributes are highly correlated, the large number of features slows down the training speed significantly, and that there are some overfitting issues.
The Data Scientist on this project would like to speed up the model training time without losing a lot of information from the original dataset.
Which feature engineering technique should the Data Scientist use to meet the objectives?

A. Cluster raw data using k-means and use sample data from each cluster to build a new dataset
B. Normalize all numerical values to be between 0 and 1
C. Run self-correlation on all features and remove highly correlated features
D. Use an autoencoder or principal component analysis (PCA) to replace original features with new features

Antwort: D

Begründung:
The best feature engineering technique to speed up the model training time without losing a lot of information from the original dataset is to use an autoencoder or principal component analysis (PCA) to replace original features with new features. An autoencoder is a type of neural network that learns a compressed representation of the input data, called the latent space, by minimizing the reconstruction error between the input and the output. PCA is a statistical technique that reduces the dimensionality of the data by finding a set of orthogonal axes, called the principal components, that capture the maximum variance of the data. Both techniques can help reduce the number of features and remove the noise and redundancy in the data, which can improve the model performance and speed up the training process. References:
AWS Machine Learning Specialty Exam Guide
AWS Machine Learning Training - Dimensionality Reduction for Machine Learning AWS Machine Learning Training - Deep Learning with Amazon SageMaker

151. Frage
A large company has developed a B1 application that generates reports and dashboards using data collected from various operational metrics The company wants to provide executives with an enhanced experience so they can use natural language to get data from the reports The company wants the executives to be able ask questions using written and spoken interlaces Which combination of services can be used to build this conversational interface? (Select THREE)

A. Amazon Lex
B. Alexa for Business
C. Amazon Transcribe
D. Amazon Connect
E. Amazon Poly
F. Amazon Comprehend

Antwort: A,C,F

Begründung:
Explanation
To build a conversational interface that can use natural language to get data from the reports, the company can use a combination of services that can handle both written and spoken inputs, understand the user's intent and query, and extract the relevant information from the reports. The services that can be used for this purpose are:
Amazon Lex: A service for building conversational interfaces into any application using voice and text. Amazon Lex can create chatbots that can interact with users using natural language, and integrate with other AWS services such as Amazon Connect, Amazon Comprehend, and Amazon Transcribe. Amazon Lex can also use lambda functions to implement the business logic and fulfill the user's requests.
Amazon Comprehend: A service for natural language processing and text analytics. Amazon Comprehend can analyze text and speech inputs and extract insights such as entities, key phrases, sentiment, syntax, and topics. Amazon Comprehend can also use custom classifiers and entity recognizers to identify specific terms and concepts that are relevant to the domain of the reports.
Amazon Transcribe: A service for speech-to-text conversion. Amazon Transcribe can transcribe audio inputs into text outputs, and add punctuation and formatting. Amazon Transcribe can also use custom vocabularies and language models to improve the accuracy and quality of the transcription for the specific domain of the reports.
Therefore, the company can use the following architecture to build the conversational interface:
Use Amazon Lex to create a chatbot that can accept both written and spoken inputs from the executives. The chatbot can use intents, utterances, and slots to capture the user's query and parameters, such as the report name, date, metric, or filter.
Use Amazon Transcribe to convert the spoken inputs into text outputs, and pass them to Amazon Lex. Amazon Transcribe can use a custom vocabulary and language model to recognize the terms and concepts related to the reports.
Use Amazon Comprehend to analyze the text inputs and outputs, and extract the relevant information from the reports. Amazon Comprehend can use a custom classifier and entity recognizer to identify the report name, date, metric, or filter from the user's query, and the corresponding data from the reports.
Use a lambda function to implement the business logic and fulfillment of the user's query, such as retrieving the data from the reports, performing calculations or aggregations, and formatting the response. The lambda function can also handle errors and validations, and provide feedback to the user.
Use Amazon Lex to return the response to the user, either in text or speech format, depending on the user's preference.
References:
What Is Amazon Lex?
What Is Amazon Comprehend?
What Is Amazon Transcribe?

152. Frage
A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may fraudulent.
How should the Specialist frame this business problem?

A. Multi-category classification
B. Regression classification
C. Streaming classification
D. Binary classification

Antwort: D

Begründung:
The business problem of predicting whether a new credit card applicant will default on a credit card payment can be framed as a binary classification problem. Binary classification is the task of predicting a discrete class label output for an example, where the class label can only take one of two possible values. In this case, the class label can be either "default" or "no default", indicating whether the applicant will or will not default on a credit card payment. A binary classification model can return the probability that a given applicant belongs to each class, and then assign the applicant to the class with the highest probability. For example, if the model predicts that an applicant has a 0.8 probability of defaulting and a 0.2 probability of not defaulting, then the model will classify the applicant as "default". Binary classification is suitable for this problem because the outcome of interest is categorical and binary, and the model needs to return the probability of each outcome.
AWS Machine Learning Specialty Exam Guide
AWS Machine Learning Training - Classification vs Regression in Machine Learning

153. Frage
A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat. The ML Specialist performs some tests and records the following results for a neural network-based image classifier:
Total number of images available = 1,000 Test set images = 100 (constant test set) The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.
Which techniques can be used by the ML Specialist to improve this specific test error?

A. Increase the dropout rate for the second-to-last layer.
B. Increase the number of layers for the neural network.
C. Increase the number of epochs for model training.
D. Increase the training data by adding variation in rotation for training images.

Antwort: D

154. Frage
A company is planning a marketing campaign to promote a new product to existing customers. The company has data (or past promotions that are similar. The company decides to try an experiment to send a more expensive marketing package to a smaller number of customers. The company wants to target the marketing campaign to customers who are most likely to buy the new product. The experiment requires that at least 90% of the customers who are likely to purchase the new product receive the marketing materials.
...company trains a model by using the linear learner algorithm in Amazon SageMaker. The model has a recall score of 80% and a precision of 75%.
...should the company retrain the model to meet these requirements?

A. Set the targetprecision hyperparameter to 90%. Set the binary classifier model selection criteria hyperparameter to precision at_jarget recall.
B. Set the target_recall hyperparameter to 90% Set the binaryclassrfier model_selection_critena hyperparameter to recall_at_target_precision.
C. Set the normalize_jabel hyperparameter to true. Set the number of classes to 2.
D. Use 90% of the historical data for training Set the number of epochs to 20.

Antwort: B

Begründung:
The best way to retrain the model to meet the requirements is to set the target_recall hyperparameter to 90% and set the binary_classifier_model_selection_criteria hyperparameter to recall_at_target_precision. This will instruct the linear learner algorithm to optimize the model for a high recall score, while maintaining a reasonable precision score. Recall is the proportion of actual positives that were identified correctly, which is important for the company's goal of reaching at least 90% of the customers who are likely to buy the new product1. Precision is the proportion of positive identifications that were actually correct, which is also relevant for the company's budget and efficiency2. By setting the target_recall to 90%, the algorithm will try to achieve a recall score of at least 90%, and by setting the binary_classifier_model_selection_criteria to recall_at_target_precision, the algorithm will select the model that has the highest recall score among those that have a precision score equal to or higher than the target precision3. The target precision is automatically set to the median of the precision scores of all the models trained in parallel4.
The other options are not correct or optimal, because they have the following drawbacks:
B: Setting the target_precision hyperparameter to 90% and setting the binary_classifier_model_selection_criteria hyperparameter to precision_at_target_recall will optimize the model for a high precision score, while maintaining a reasonable recall score. However, this is not aligned with the company's goal of reaching at least 90% of the customers who are likely to buy the new product, as precision does not reflect how well the model identifies the actual positives1. Moreover, setting the target_precision to 90% might be too high and unrealistic for the dataset, as the current precision score is only 75%4.
C: Using 90% of the historical data for training and setting the number of epochs to 20 will not necessarily improve the recall score of the model, as it does not change the optimization objective or the model selection criteria. Moreover, using more data for training might reduce the amount of data available for validation, which is needed for selecting the best model among the ones trained in parallel3. The number of epochs is also not a decisive factor for the recall score, as it depends on the learning rate, the optimizer, and the convergence of the algorithm5.
D: Setting the normalize_label hyperparameter to true and setting the number of classes to 2 will not affect the recall score of the model, as these are irrelevant hyperparameters for binary classification problems. The normalize_label hyperparameter is only applicable for regression problems, as it controls whether the label is normalized to have zero mean and unit variance3. The number of classes hyperparameter is only applicable for multiclass classification problems, as it specifies the number of output classes3.
References:
1: Classification: Precision and Recall | Machine Learning | Google for Developers
2: Precision and recall - Wikipedia
3: Linear Learner Algorithm - Amazon SageMaker
4: How linear learner works - Amazon SageMaker
5: Getting hands-on with Amazon SageMaker Linear Learner - Pluralsight

155. Frage
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