RELIABLE MLS-C01 TEST PATTERN, NEW MLS-C01 TEST PREPARATION

Reliable MLS-C01 Test Pattern, New MLS-C01 Test Preparation

Reliable MLS-C01 Test Pattern, New MLS-C01 Test Preparation

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This format is for candidates who do not have the time or energy to use a computer or laptop for preparation. Amazon MLS-C01 PDF file includes real Amazon MLS-C01 questions, and they can be easily printed and studied at any time. DumpsValid regularly updates its PDF file to ensure that its readers have access to the updated questions.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q214-Q219):

NEW QUESTION # 214
A library is developing an automatic book-borrowing system that uses Amazon Rekognition. Images of library members' faces are stored in an Amazon S3 bucket. When members borrow books, the Amazon Rekognition CompareFaces API operation compares real faces against the stored faces in Amazon S3.
The library needs to improve security by making sure that images are encrypted at rest. Also, when the images are used with Amazon Rekognition. they need to be encrypted in transit. The library also must ensure that the images are not used to improve Amazon Rekognition as a service.
How should a machine learning specialist architect the solution to satisfy these requirements?

  • A. Switch to using the AWS GovCloud (US) Region for Amazon S3 to store images and for Amazon Rekognition to compare faces. Set up a VPN connection and only call the Amazon Rekognition API operations through the VPN.
  • B. Switch to using an Amazon Rekognition collection to store the images. Use the IndexFaces and SearchFacesByImage API operations instead of the CompareFaces API operation.
  • C. Enable server-side encryption on the S3 bucket. Submit an AWS Support ticket to opt out of allowing images to be used for improving the service, and follow the process provided by AWS Support.
  • D. Enable client-side encryption on the S3 bucket. Set up a VPN connection and only call the Amazon Rekognition API operations through the VPN.

Answer: B


NEW QUESTION # 215
An ecommerce company wants to use machine learning (ML) to monitor fraudulent transactions on its website. The company is using Amazon SageMaker to research, train, deploy, and monitor the ML models.
The historical transactions data is in a .csv file that is stored in Amazon S3 The data contains features such as the user's IP address, navigation time, average time on each page, and the number of clicks for ....session. There is no label in the data to indicate if a transaction is anomalous.
Which models should the company use in combination to detect anomalous transactions? (Select TWO.)

  • A. Linear learner with a logistic function
  • B. IP Insights
  • C. Random Cut Forest (RCF)
  • D. K-nearest neighbors (k-NN)
  • E. XGBoost

Answer: C,E

Explanation:
To detect anomalous transactions, the company can use a combination of Random Cut Forest (RCF) and XGBoost models. RCF is an unsupervised algorithm that can detect outliers in the data by measuring the depth of each data point in a collection of random decision trees. XGBoost is a supervised algorithm that can learn from the labeled data points generated by RCF and classify them as normal or anomalous. RCF can also provide anomaly scores that can be used as features for XGBoost to improve the accuracy of the classification. References:
1: Amazon SageMaker Random Cut Forest
2: Amazon SageMaker XGBoost Algorithm
3: Anomaly Detection with Amazon SageMaker Random Cut Forest and Amazon SageMaker XGBoost


NEW QUESTION # 216
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. Regression classification
  • B. Multi-category classification
  • C. Streaming classification
  • D. Binary classification

Answer: B


NEW QUESTION # 217
A company wants to segment a large group of customers into subgroups based on shared characteristics. The company's data scientist is planning to use the Amazon SageMaker built-in k-means clustering algorithm for this task. The data scientist needs to determine the optimal number of subgroups (k) to use.
Which data visualization approach will MOST accurately determine the optimal value of k?

  • A. Calculate the principal component analysis (PCA) components. Run the k-means clustering algorithm for a range of k by using only the first two PCA components. For each value of k, create a scatter plot with a different color for each cluster. The optimal value of k is the value where the clusters start to look reasonably separated.
  • B. Create a t-distributed stochastic neighbor embedding (t-SNE) plot for a range of perplexity values. The optimal value of k is the value of perplexity, where the clusters start to look reasonably separated.
  • C. Calculate the principal component analysis (PCA) components. Create a line plot of the number of components against the explained variance. The optimal value of k is the number of PCA components after which the curve starts decreasing in a linear fashion.
  • D. Run the k-means clustering algorithm for a range of k. For each value of k, calculate the sum of squared errors (SSE). Plot a line chart of the SSE for each value of k. The optimal value of k is the point after which the curve starts decreasing in a linear fashion.

Answer: D

Explanation:
Explanation
The solution D is the best data visualization approach to determine the optimal value of k for the k-means clustering algorithm. The solution D involves the following steps:
Run the k-means clustering algorithm for a range of k. For each value of k, calculate the sum of squared errors (SSE). The SSE is a measure of how well the clusters fit the data. It is calculated by summing the squared distances of each data point to its closest cluster center. A lower SSE indicates a better fit, but it will always decrease as the number of clusters increases. Therefore, the goal is to find the smallest value of k that still has a low SSE1.
Plot a line chart of the SSE for each value of k. The line chart will show how the SSE changes as the value of k increases. Typically, the line chart will have a shape of an elbow, where the SSE drops rapidly at first and then levels off. The optimal value of k is the point after which the curve starts decreasing in a linear fashion. This point is also known as the elbow point, and it represents the balance between the number of clusters and the SSE1.
The other options are not suitable because:
Option A: Calculating the principal component analysis (PCA) components, running the k-means clustering algorithm for a range of k by using only the first two PCA components, and creating a scatter plot with a different color for each cluster will not accurately determine the optimal value of k. PCA is a technique that reduces the dimensionality of the data by transforming it into a new set of features that capture the most variance in the data. However, PCA may not preserve the original structure and distances of the data, and it may lose some information in the process. Therefore, running the k-means clustering algorithm on the PCA components may not reflect the true clusters in the data. Moreover, using only the first two PCA components may not capture enough variance to represent the data well. Furthermore, creating a scatter plot may not be reliable, as it depends on the subjective judgment of the data scientist to decide when the clusters look reasonably separated2.
Option B: Calculating the PCA components and creating a line plot of the number of components against the explained variance will not determine the optimal value of k. This approach is used to determine the optimal number of PCA components to use for dimensionality reduction, not for clustering. The explained variance is the ratio of the variance of each PCA component to the total variance of the data. The optimal number of PCA components is the point where adding more components does not significantly increase the explained variance. However, this number may not correspond to the optimal number of clusters, as PCA and k-means clustering have different objectives and assumptions2.
Option C: Creating a t-distributed stochastic neighbor embedding (t-SNE) plot for a range of perplexity values will not determine the optimal value of k. t-SNE is a technique that reduces the dimensionality of the data by embedding it into a lower-dimensional space, such as a two-dimensional plane. t-SNE preserves the local structure and distances of the data, and it can reveal clusters and patterns in the data.
However, t-SNE does not assign labels or centroids to the clusters, and it does not provide a measure of how well the clusters fit the data. Therefore, t-SNE cannot determine the optimal number of clusters, as it only visualizes the data. Moreover, t-SNE depends on the perplexity parameter, which is a measure of how many neighbors each point considers. The perplexity parameter can affect the shape and size of the clusters, and there is no optimal value for it. Therefore, creating a t-SNE plot for a range of perplexity values may not be consistent or reliable3.
References:
1: How to Determine the Optimal K for K-Means?
2: Principal Component Analysis
3: t-Distributed Stochastic Neighbor Embedding


NEW QUESTION # 218
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 be fraudulent How should the Specialist frame this business problem'?

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

Answer: A

Explanation:
Explanation
Binary classification is a type of supervised learning problem where the goal is to predict a categorical label that has only two possible values, such as Yes or No, True or False, Positive or Negative. In this case, the label is whether a transaction is fraudulent or not, which is a binary outcome. Binary classification can be used to estimate the probability of an observation belonging to a certain class, such as the probability of a transaction being fraudulent. This can help the business to make decisions based on the risk level of each transaction.
References:
Binary Classification - Amazon Machine Learning
AWS Certified Machine Learning - Specialty Sample Questions


NEW QUESTION # 219
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