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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q133-Q138):

NEW QUESTION # 133
A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet.
How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?

A. Create a NAT gateway within the corporate VPC.
B. Create VPC peering with Amazon VPC hosting Amazon SageMaker.
C. Route Amazon SageMaker traffic through an on-premises network.
D. Create Amazon SageMaker VPC interface endpoints within the corporate VPC.

Answer: D

Explanation:
Explanation
To enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances, the company should create Amazon SageMaker VPC interface endpoints within the corporate VPC. A VPC interface endpoint is a gateway that enables private connections between the VPC and supported AWS services without requiring an internet gateway, a NAT device, a VPN connection, or an AWS Direct Connect connection. The instances in the VPC do not need to connect to the public internet in order to communicate with the Amazon SageMaker service. The VPC interface endpoint connects the VPC directly to the Amazon SageMaker service using AWS PrivateLink, which ensures that the traffic between the VPC and the service does not leave the AWS network1.
References:
1: Connect to SageMaker Within your VPC - Amazon SageMaker

NEW QUESTION # 134
A bank's Machine Learning team is developing an approach for credit card fraud detection The company has a large dataset of historical data labeled as fraudulent The goal is to build a model to take the information from new transactions and predict whether each transaction is fraudulent or not Which built-in Amazon SageMaker machine learning algorithm should be used for modeling this problem?

A. Random Cut Forest (RCF)
B. K-means
C. Seq2seq
D. XGBoost

Answer: D

Explanation:
Explanation
XGBoost is a built-in Amazon SageMaker machine learning algorithm that should be used for modeling the credit card fraud detection problem. XGBoost is an algorithm that implements a scalable and distributed gradient boosting framework, which is a popular and effective technique for supervised learning problems.
Gradient boosting is a method of combining multiple weak learners, such as decision trees, into a strong learner, by iteratively fitting new models to the residual errors of the previous models and adding them to the ensemble. XGBoost can handle various types of data, such as numerical, categorical, or text, and can perform both regression and classification tasks. XGBoost also supports various features and optimizations, such as regularization, missing value handling, parallelization, and cross-validation, that can improve the performance and efficiency of the algorithm.
XGBoost is suitable for the credit card fraud detection problem for the following reasons:
The problem is a binary classification problem, where the goal is to predict whether a transaction is fraudulent or not, based on the information from new transactions. XGBoost can perform binary classification by using a logistic regression objective function and outputting the probability of the positive class (fraudulent) for each transaction.
The problem involves a large and imbalanced dataset of historical data labeled as fraudulent. XGBoost can handle large-scale and imbalanced data by using distributed and parallel computing, as well as techniques such as weighted sampling, class weighting, or stratified sampling, to balance the classes and reduce the bias towards the majority class (non-fraudulent).
The problem requires a high accuracy and precision for detecting fraudulent transactions, as well as a low false positive rate for avoiding false alarms. XGBoost can achieve high accuracy and precision by using gradient boosting, which can learn complex and non-linear patterns from the data and reduce the variance and overfitting of the model. XGBoost can also achieve a low false positive rate by using regularization, which can reduce the complexity and noise of the model and prevent it from fitting spurious signals in the data.
The other options are not as suitable as XGBoost for the credit card fraud detection problem for the following reasons:
Seq2seq: Seq2seq is an algorithm that implements a sequence-to-sequence model, which is a type of neural network model that can map an input sequence to an output sequence. Seq2seq is mainly used for natural language processing tasks, such as machine translation, text summarization, or dialogue generation. Seq2seq is not suitable for the credit card fraud detection problem, because the problem is not a sequence-to-sequence task, but a binary classification task. The input and output of the problem are not sequences of words or tokens, but vectors of features and labels.
K-means: K-means is an algorithm that implements a clustering technique, which is a type of unsupervised learning method that can group similar data points into clusters. K-means is mainly used for exploratory data analysis, dimensionality reduction, or anomaly detection. K-means is not suitable for the credit card fraud detection problem, because the problem is not a clustering task, but a classification task. The problem requires using the labeled data to train a model that can predict the labels of new data, not finding the optimal number of clusters or the cluster memberships of the data.
Random Cut Forest (RCF): RCF is an algorithm that implements an anomaly detection technique, which is a type of unsupervised learning method that can identify data points that deviate from the normal behavior or distribution of the data. RCF is mainly used for detecting outliers, frauds, or faults in the data. RCF is not suitable for the credit card fraud detection problem, because the problem is not an anomaly detection task, but a classification task. The problem requires using the labeled data to train a model that can predict the labels of new data, not finding the anomaly scores or the anomalous data points in the data.
References:
XGBoost Algorithm
Use XGBoost for Binary Classification with Amazon SageMaker
Seq2seq Algorithm
K-means Algorithm
[Random Cut Forest Algorithm]

NEW QUESTION # 135
A finance company needs to forecast the price of a commodity. The company has compiled a dataset of historical daily prices. A data scientist must train various forecasting models on 80% of the dataset and must validate the efficacy of those models on the remaining 20% of the dataset.
What should the data scientist split the dataset into a training dataset and a validation dataset to compare model performance?

A. Starting from the earliest date in the dataset. pick eight data points for the training dataset and two data points for the validation dataset. Repeat this stratified sampling until no data points remain.
B. Sample data points randomly without replacement so that 80% of the data points are in the training dataset. Assign all the remaining data points to the validation dataset.
C. Pick a date so that 80% to the data points precede the date Assign that group of data points as the training dataset. Assign all the remaining data points to the validation dataset.
D. Pick a date so that 80% of the data points occur after the date. Assign that group of data points as the training dataset. Assign all the remaining data points to the validation dataset.

Answer: C

Explanation:
A Comprehensive Explanation: The best way to split the dataset into a training dataset and a validation dataset is to pick a date so that 80% of the data points precede the date and assign that group of data points as the training dataset. This method preserves the temporal order of the data and ensures that the validation dataset reflects the most recent trends and patterns in the commodity price. This is important for forecasting models that rely on time series analysis and sequential data. The other methods would either introduce bias or lose information by ignoring the temporal structure of the data.
References:
Time Series Forecasting - Amazon SageMaker
Time Series Splitting - scikit-learn
Time Series Forecasting - Towards Data Science

NEW QUESTION # 136
A company that runs an online library is implementing a chatbot using Amazon Lex to provide book recommendations based on category. This intent is fulfilled by an AWS Lambda function that queries an Amazon DynamoDB table for a list of book titles, given a particular category. For testing, there are only three categories implemented as the custom slot types: "comedy," "adventure," and "documentary." A machine learning (ML) specialist notices that sometimes the request cannot be fulfilled because Amazon Lex cannot understand the category spoken by users with utterances such as "funny," "fun," and "humor." The ML specialist needs to fix the problem without changing the Lambda code or data in DynamoDB.
How should the ML specialist fix the problem?

A. Add the unrecognized words as synonyms in the custom slot type.
B. Add the unrecognized words in the enumeration values list as new values in the slot type.
C. Use the AMAZON.SearchQuery built-in slot types for custom searches in the database.
D. Create a new custom slot type, add the unrecognized words to this slot type as enumeration values, and use this slot type for the slot.

Answer: A

Explanation:
The best way to fix the problem without changing the Lambda code or data in DynamoDB is to add the unrecognized words as synonyms in the custom slot type. This way, Amazon Lex can resolve the synonyms to the corresponding slot values and pass them to the Lambda function. For example, if the slot type has a value "comedy" with synonyms "funny", "fun", and "humor", then any of these words entered by the user will be resolved to "comedy" and the Lambda function can query the DynamoDB table for the book titles in that category. Adding synonyms to the custom slot type can be done easily using the Amazon Lex console or API, and does not require any code changes.
The other options are not correct because:
Option A: Adding the unrecognized words in the enumeration values list as new values in the slot type would not fix the problem, because the Lambda function and the DynamoDB table are not aware of these new values. The Lambda function would not be able to query the DynamoDB table for the book titles in the new categories, and the request would still fail. Moreover, adding new values to the slot type would increase the complexity and maintenance of the chatbot, as the Lambda function and the DynamoDB table would have to be updated accordingly.
Option B: Creating a new custom slot type, adding the unrecognized words to this slot type as enumeration values, and using this slot type for the slot would also not fix the problem, for the same reasons as option A. The Lambda function and the DynamoDB table would not be able to handle the new slot type and its values, and the request would still fail. Furthermore, creating a new slot type would require more effort and time than adding synonyms to the existing slot type.
Option C: Using the AMAZON.SearchQuery built-in slot types for custom searches in the database is not a suitable approach for this use case. The AMAZON.SearchQuery slot type is used to capture free-form user input that corresponds to a search query. However, this slot type does not perform any validation or resolution of the user input, and passes the raw input to the Lambda function. This means that the Lambda function would have to handle the logic of parsing and matching the user input to the DynamoDB table, which would require changing the Lambda code and adding more complexity to the solution.
References:
Custom slot type - Amazon Lex
Using Synonyms - Amazon Lex
Built-in Slot Types - Amazon Lex

NEW QUESTION # 137
A Machine Learning Specialist is training a model to identify the make and model of vehicles in images The Specialist wants to use transfer learning and an existing model trained on images of general objects The Specialist collated a large custom dataset of pictures containing different vehicle makes and models

A. Initialize the model with random weights in all layers including the last fully connected layer
B. Initialize the model with random weights in all layers and replace the last fully connected layer
C. Initialize the model with pre-trained weights in all layers including the last fully connected layer
D. Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.

Answer: C

NEW QUESTION # 138
......

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