Question 1

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?

Options :

A : Seq2seq

B : XGBoost

C : K-means

D : Random Cut Forest (RCF)

Answer: B

Question 2

A company supplies wholesale clothing to thousands of retail stores. A data scientist must create a model that predicts the daily sales volume for each item for each store. The data scientist discovers that more than half of the stores have been in business for less than 6 months. Sales data is highly consistent from week to week. Daily data from the database has been aggregated weekly, and weeks with no sales are omitted from the current dataset. Five years (100 MB) of sales data is available in Amazon S3.

Which factors will adversely impact the performance of the forecast model to be developed, and which actions should the data scientist take to mitigate them? (Choose two.)

Options :

A : Detecting seasonality for the majority of stores will be an issue. Request categorical data to relate new stores with similar stores that have more historical data.

B : The sales data does not have enough variance. Request external sales data from other industries to improve the model-s ability to generalize.

C : Sales data is aggregated by week. Request daily sales data from the source database to enable building a daily model.

D : The sales data is missing zero entries for item sales. Request that item sales data from the source database include zero entries to enable building the model.

E : Only 100 MB of sales data is available in Amazon S3. Request 10 years of sales data, which would provide 200 MB of training data for the model.

Answer: C,D

Question 3

A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences and trends to enhance the website for better service and smart recommendations.

Which solution should the Specialist recommend?

Options :

A : Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.

B : A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database

C : Collaborative filtering based on user interactions and correlations to identify patterns in the customer database

D : Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database

Answer: C

Question 4

A company wants to conduct targeted marketing to sell solar panels to homeowners. The company wants to use machine learning (ML) technologies to identify which houses already have solar panels. The company has collected 8,000 satellite images as training data and will use Amazon SageMaker Ground Truth to label the data. The company has a small internal team that is working on the project. The internal team has no ML expertise and no ML experience. Which solution will meet these requirements with the LEAST amount of effort from the internal team?

Options :

A :

Set up a private workforce that consists of the internal team. Use the private workforce and the SageMaker Ground Truth active learning feature to label the data. Use Amazon Rekognition Custom Labels for model training and hosting.

B :

Set up a private workforce that consists of the internal team. Use the private workforce to label the data. Use Amazon Rekognition Custom Labels for model training and hosting

C :

Set up a private workforce that consists of the internal team. Use the private workforce and the SageMaker Ground Truth active learning feature to label the data. Use the SageMaker Object Detection algorithm to train a model. Use SageMaker batch transform for inference

D :

Set up a public workforce. Use the public workforce to label the data. Use the SageMaker Object Detection algorithm to train a model. Use SageMaker batch transform for inference.

Answer: A

Question 5

A manufacturing company has a large set of labeled historical sales data The manufacturer would like to predict how many units of a particular part should be produced each quarter Which machine learning approach should be used to solve this problem?

Options :

A : Logistic regression

B : Random Cut Forest (RCF)

C : Principal component analysis (PCA)

D : Linear regression

Answer: D

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