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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q132-Q137):
NEW QUESTION # 132
A company wants to detect credit card fraud. The company has observed that an average of 2% of credit card transactions are fraudulent. A data scientist trains a classifier on a year's worth of credit card transaction data.
The classifier needs to identify the fraudulent transactions. The company wants to accurately capture as many fraudulent transactions as possible.
Which metrics should the data scientist use to optimize the classifier? (Select TWO.)
Answer: D,E
Explanation:
The F1 score is a measure of the harmonic mean of precision and recall, which are both important for fraud detection. Precision is the ratio of true positives to all predicted positives, and recall is the ratio of true positives to all actual positives. A high F1 score indicates that the classifier can correctly identify fraudulent transactions and avoid false negatives. The true positive rate is another name for recall, and it measures the proportion of fraudulent transactions that are correctly detected by the classifier. A high true positive rate means that the classifier can capture as many fraudulent transactions as possible.
References:
* Fraud Detection Using Machine Learning | Implementations | AWS Solutions
* Detect fraudulent transactions using machine learning with Amazon SageMaker | AWS Machine Learning Blog
* 1. Introduction - Reproducible Machine Learning for Credit Card Fraud Detection
NEW QUESTION # 133
An obtain relator collects the following data on customer orders: demographics, behaviors, location, shipment progress, and delivery time. A data scientist joins all the collected datasets. The result is a single dataset that includes 980 variables.
The data scientist must develop a machine learning (ML) model to identify groups of customers who are likely to respond to a marketing campaign.
Which combination of algorithms should the data scientist use to meet this requirement? (Select TWO.)
Answer: A,C
Explanation:
Explanation
The data scientist should use K-means and principal component analysis (PCA) to meet this requirement.
K-means is a clustering algorithm that can group customers based on their similarity in the feature space. PCA is a dimensionality reduction technique that can transform the original 980 variables into a smaller set of uncorrelated variables that capture most of the variance in the data. This can help reduce the computational cost and noise in the data, and improve the performance of the clustering algorithm.
References:
Clustering - Amazon SageMaker
Dimensionality Reduction - Amazon SageMaker
NEW QUESTION # 134
An agricultural company is interested in using machine learning to detect specific types of weeds in a 100- acre grassland field. Currently, the company uses tractor-mounted cameras to capture multiple images of the field as 10 × 10 grids. The company also has a large training dataset that consists of annotated images of popular weed classes like broadleaf and non-broadleaf docks.
The company wants to build a weed detection model that will detect specific types of weeds and the location of each type within the field. Once the model is ready, it will be hosted on Amazon SageMaker endpoints.
The model will perform real-time inferencing using the images captured by the cameras.
Which approach should a Machine Learning Specialist take to obtain accurate predictions?
Answer: D
Explanation:
The problem of detecting specific types of weeds and their location within the field is an example of object detection, which is a type of machine learning model that identifies and localizes objects in an image.
Amazon SageMaker provides a built-in object detection algorithm that uses a single-shot multibox detector (SSD) to perform real-time inference on streaming images. The SSD algorithm can handle multiple objects of varying sizes and scales in an image, and generate bounding boxes and scores for each object category.
Therefore, option C is the best approach to obtain accurate predictions.
Option A is incorrect because image classification is a type of machine learning model that assigns a label to an image based on predefined categories. Image classification is not suitable for localizing objects within an image, as it does not provide bounding boxes or scores for each object. Option B is incorrect because Apache Parquet is a columnar storage format that is optimized for analytical queries. Apache Parquet is not suitable for storing images, as it does not preserve the spatial information of the pixels. Option D is incorrect because it combines the wrong format (Apache Parquet) and the wrong algorithm (image classification) for the given problem, as explained in options A and B.
Object Detection algorithm now available in Amazon SageMaker
Image classification and object detection using Amazon Rekognition Custom Labels and Amazon SageMaker JumpStart Object Detection with Amazon SageMaker - W3Schools aws-samples/amazon-sagemaker-tensorflow-object-detection-api
NEW QUESTION # 135
A company has video feeds and images of a subway train station. The company wants to create a deep learning model that will alert the station manager if any passenger crosses the yellow safety line when there is no train in the station. The alert will be based on the video feeds. The company wants the model to detect the yellow line, the passengers who cross the yellow line, and the trains in the video feeds. This task requires labeling. The video data must remain confidential.
A data scientist creates a bounding box to label the sample data and uses an object detection model. However, the object detection model cannot clearly demarcate the yellow line, the passengers who cross the yellow line, and the trains.
Which labeling approach will help the company improve this model?
Answer: A
NEW QUESTION # 136
A Machine Learning Specialist is preparing data for training on Amazon SageMaker The Specialist is transformed into a numpy .array, which appears to be negatively affecting the speed of the training What should the Specialist do to optimize the data for training on SageMaker'?
Answer: A
NEW QUESTION # 137
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