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100% Free MLS-C01–100% Free Exam Revision Plan | Accurate AWS Certified Machine Learning - Specialty Valid Dumps Questions

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Amazon AWS Certified Machine Learning - Specialty certification exam is designed for individuals who want to demonstrate their expertise in building, deploying, and managing machine learning solutions on the Amazon Web Services (AWS) platform. AWS Certified Machine Learning - Specialty certification validates the skills required to design, implement, deploy, and maintain machine learning solutions that are scalable, secure, and highly available. MLS-C01 Exam Tests candidates’ knowledge of various AWS services, including Amazon SageMaker, Amazon S3, Amazon EC2, and Amazon RDS.

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Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) Certification Exam is a comprehensive and challenging test designed for individuals who want to demonstrate their expertise in machine learning on the Amazon Web Services (AWS) platform. MLS-C01 exam validates your knowledge and skills in designing, implementing, and maintaining machine learning solutions using AWS services. AWS Certified Machine Learning - Specialty certification is suitable for professionals who have experience in data science, software engineering, and cloud computing.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q85-Q90):

NEW QUESTION # 85
A law firm handles thousands of contracts every day. Every contract must be signed. Currently, a lawyer manually checks all contracts for signatures.
The law firm is developing a machine learning (ML) solution to automate signature detection for each contract. The ML solution must also provide a confidence score for each contract page.
Which Amazon Textract API action can the law firm use to generate a confidence score for each page of each contract?

A. Use the StartDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page.
B. Use the GetDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page
C. Use the AnalyzeDocument API action. Set the FeatureTypes parameter to SIGNATURES. Return the confidence scores for each page.
D. Use the Prediction API call on the documents. Return the signatures and confidence scores for each page.

Answer: C

Explanation:
The AnalyzeDocument API action is the best option to generate a confidence score for each page of each contract. This API action analyzes an input document for relationships between detected items. The input document can be an image file in JPEG or PNG format, or a PDF file. The output is a JSON structure that contains the extracted data from the document. The FeatureTypes parameter specifies the types of analysis to perform on the document. The available feature types are TABLES, FORMS, and SIGNATURES. By setting the FeatureTypes parameter to SIGNATURES, the API action will detect and extract information about signatures from the document. The output will include a list of SignatureDetection objects, each containing information about a detected signature, such as its location and confidence score. The confidence score is a value between 0 and 100 that indicates the probability that the detected signature is correct. The output will also include a list of Block objects, each representing a document page. Each Block object will have a Page attribute that contains the page number and a Confidence attribute that contains the confidence score for the page. The confidence score for the page is the average of the confidence scores of the blocks that are detected on the page. The law firm can use the AnalyzeDocument API action to generate a confidence score for each page of each contract by using the SIGNATURES feature type and returning the confidence scores from the SignatureDetection and Block objects.
The other options are not suitable for generating a confidence score for each page of each contract. The Prediction API call is not an Amazon Textract API action, but a generic term for making inference requests to a machine learning model. The StartDocumentAnalysis API action is used to start an asynchronous job to analyze a document. The output is a job identifier (JobId) that is used to get the results of the analysis with the GetDocumentAnalysis API action. The GetDocumentAnalysis API action is used to get the results of a document analysis started by the StartDocumentAnalysis API action. The output is a JSON structure that contains the extracted data from the document. However, both the StartDocumentAnalysis and the GetDocumentAnalysis API actions do not support the SIGNATURES feature type, and therefore cannot detect signatures or provide confidence scores for them.

NEW QUESTION # 86
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. Enable client-side encryption on the S3 bucket. Set up a VPN connection and only call the Amazon Rekognition API operations through the VPN.
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. Switch to using an Amazon Rekognition collection to store the images. Use the IndexFaces and SearchFacesByImage API operations instead of the CompareFaces API operation.

Answer: C

Explanation:
The best solution for encrypting images at rest and in transit, and opting out of data usage for service improvement, is to use the following steps:
Enable server-side encryption on the S3 bucket. This will encrypt the images stored in the bucket using AWS Key Management Service (AWS KMS) customer master keys (CMKs). This will protect the data at rest from unauthorized access1 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. This will prevent AWS from storing or using the images processed by Amazon Rekognition for service development or enhancement purposes. This will protect the data privacy and ownership2 Use HTTPS to call the Amazon Rekognition CompareFaces API operation. This will encrypt the data in transit between the client and the server using SSL/TLS protocols. This will protect the data from interception or tampering3 The other options are incorrect because they either do not encrypt the images at rest or in transit, or do not opt out of data usage for service improvement. For example:
Option B switches to using an Amazon Rekognition collection to store the images. A collection is a container for storing face vectors that are calculated by Amazon Rekognition. It does not encrypt the images at rest or in transit, and it does not opt out of data usage for service improvement. It also requires changing the API operations from CompareFaces to IndexFaces and SearchFacesByImage, which may not have the same functionality or performance4 Option C switches to using the AWS GovCloud (US) Region for Amazon S3 and Amazon Rekognition. The AWS GovCloud (US) Region is an isolated AWS Region designed to host sensitive data and regulated workloads in the cloud. It does not automatically encrypt the images at rest or in transit, and it does not opt out of data usage for service improvement. It also requires migrating the data and the application to a different Region, which may incur additional costs and complexity5 Option D enables client-side encryption on the S3 bucket. This means that the client is responsible for encrypting and decrypting the images before uploading or downloading them from the bucket. This adds extra overhead and complexity to the client application, and it does not encrypt the data in transit when calling the Amazon Rekognition API. It also does not opt out of data usage for service improvement.
1: Protecting Data Using Server-Side Encryption with AWS KMS-Managed Keys (SSE-KMS) - Amazon Simple Storage Service
2: Opting Out of Content Storage and Use for Service Improvements - Amazon Rekognition
3: HTTPS - Wikipedia
4: Working with Stored Faces - Amazon Rekognition
5: AWS GovCloud (US) - Amazon Web Services
Protecting Data Using Client-Side Encryption - Amazon Simple Storage Service

NEW QUESTION # 87
A Machine Learning Specialist is given a structured dataset on the shopping habits of a company's customer base. The dataset contains thousands of columns of data and hundreds of numerical columns for each customer. The Specialist wants to identify whether there are natural groupings for these columns across all customers and visualize the results as quickly as possible.
What approach should the Specialist take to accomplish these tasks?

A. Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a line graph.
B. Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a scatter plot.
C. Run k-means using the Euclidean distance measure for different values of k and create box plots for each numerical column within each cluster.
D. Run k-means using the Euclidean distance measure for different values of k and create an elbow plot.

Answer: D

NEW QUESTION # 88
This graph shows the training and validation loss against the epochs for a neural network The network being trained is as follows
* Two dense layers one output neuron
* 100 neurons in each layer
* 100 epochs
* Random initialization of weights
Which technique can be used to improve model performance in terms of accuracy in the validation set?

A. Adding another layer with the 100 neurons
B. Early stopping
C. Increasing the number of epochs
D. Random initialization of weights with appropriate seed

Answer: B

Explanation:
Early stopping is a technique that can be used to prevent overfitting and improve model performance on the validation set. Overfitting occurs when the model learns the training data too well and fails to generalize to new and unseen data. This can be seen in the graph, where the training loss keeps decreasing, but the validation loss starts to increase after some point. This means that the model is fitting the noise and patterns in the training data that are not relevant for the validation data. Early stopping is a way of stopping the training process before the model overfits the training data. It works by monitoring the validation loss and stopping the training when the validation loss stops decreasing or starts increasing. This way, the model is saved at the point where it has the best performance on the validation set. Early stopping can also save time and resources by reducing the number of epochs needed for training. References:
* Early Stopping
* How to Stop Training Deep Neural Networks At the Right Time Using Early Stopping

NEW QUESTION # 89
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.)

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. Sales data is aggregated by week. Request daily sales data from the source database to enable building a daily model.
C. 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.
D. The sales data does not have enough variance. Request external sales data from other industries to improve the model's ability to generalize.
E. 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.

Answer: B,E

Explanation:
The factors that will adversely impact the performance of the forecast model are:
* Sales data is aggregated by week. This will reduce the granularity and resolution of the data, and make it harder to capture the daily patterns and variations in sales volume. The data scientist should request daily sales data from the source database to enable building a daily model, which will be more accurate and useful for the prediction task.
* Sales data is missing zero entries for item sales. This will introduce bias and incompleteness in the data, and make it difficult to account for the items that have no demand or are out of stock. The data scientist should request that item sales data from the source database include zero entries to enable building the model, which will be more robust and realistic.
The other options are not valid because:
* Detecting seasonality for the majority of stores will not be an issue, as sales data is highly consistent from week to week. Requesting categorical data to relate new stores with similar stores that have more historical data may not improve the model performance significantly, and may introduce unnecessary complexity and noise.
* The sales data does not need to have more variance, as it reflects the actual demand and behavior of the customers. Requesting external sales data from other industries will not improve the model's ability to generalize, but may introduce irrelevant and misleading information.
* Only 100 MB of sales data is not a problem, as it is sufficient to train a forecast model with Amazon S3 and Amazon Forecast. Requesting 10 years of sales data will not provide much benefit, as it may contain outdated and obsolete information that does not reflect the current market trends and customer preferences.
Amazon Forecast
Forecasting: Principles and Practice

NEW QUESTION # 90
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