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Pass Guaranteed Quiz Amazon - MLS-C01 - AWS Certified Machine Learning - Specialty–Valid Valid Exam Papers

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It takes a lot of effort and hard work to get the results. The first step is to download real AWS Certified Machine Learning - Specialty (MLS-C01) Exam Questions of TrainingDump. These AWS Certified Machine Learning - Specialty (MLS-C01) exam questions are available in PDF, desktop practice test software, and web-based practice exam. If you are already an employee or busy in your routine, you can prepare MLS-C01 Exam quickly with TrainingDump pdf questions. MLS-C01 pdf exam questions help applicants study for the AWS Certified Machine Learning - Specialty (MLS-C01) exam at any time from any location. With the pdf questions, it will be easy for you to complete the AWS Certified Machine Learning - Specialty (MLS-C01) exam preparation in a short time.

To appear for the AWS-Certified-Machine-Learning-Specialty exam, candidates must have a minimum of one year of experience using AWS services to design and implement machine learning solutions. They should also have a solid understanding of machine learning concepts, such as supervised and unsupervised learning, deep learning, and neural networks.

What Exam Is Necessary for AWS Machine Learning – Specialty?

The only test necessary to take the AWS Machine Learning – Specialty certification has the code MLS-C This is a specialty exam and it is delivered in English, Japanese, Korean, and Simplified Chinese. Candidates can use two types of delivery methods:

In testing centers;
Online using a proctored exam platform.

The registration fee for the AWS MLS-C01 Exam is $300. In case candidates want to enroll in doing a practice exam, they should pay another $40. MLS-C01 test includes two types of questions. Candidates will have to answer both multiple-choice and multiple-answer items. Besides, the passing score range goes from 100 to 1,000 points. A candidate will be successful only when he/she gets a minimum score of 750 points. After successfully completing this MLS-C01 exam, you will be awarded the AWS Certified Machine Learning Specialty certification. If you add this certificate to your resume and social network, your chances to get better salary offers are higher. Also, this certification is valid for three years. But once its validity expires, you will need to check the vendor's official site for recertification.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q99-Q104):

NEW QUESTION # 99
A machine learning (ML) specialist is developing a model for a company. The model will classify and predict sequences of objects that are displayed in a video. The ML specialist decides to use a hybrid architecture that consists of a convolutional neural network (CNN) followed by a classifier three-layer recurrent neural network (RNN).
The company developed a similar model previously but trained the model to classify a different set of objects.
The ML specialist wants to save time by using the previously trained model and adapting the model for the current use case and set of objects.
Which combination of steps will accomplish this goal with the LEAST amount of effort? (Select TWO.)

A. Reinitialize the weights of the last fully connected layer of the CNN. Retrain the CNN on the classification task by using the new set of objects.
B. Reinitialize the weights of the entire RNN. Retrain the entire model on the prediction task by using the new set of objects.
C. Reinitialize the weights of the entire network. Retrain the entire network on the prediction task by using the new set of objects.
D. Reinitialize the weights of the entire CNN. Retrain the CNN on the classification task by using the new set of objects.
E. Reinitialize the weights of the last layer of the RNN. Retrain the entire model on the prediction task by using the new set of objects.

Answer: A,E

Explanation:
To adapt a previously trained model to a new but related task efficiently, the best practice is to leverage transfer learning. This involves retaining the learned features from the earlier model and only retraining the final layers to accommodate the new classification categories.
In the context of a hybrid architecture combining a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN):
* CNN Component: The CNN is responsible for extracting spatial features from video frames. Since the early layers of a CNN capture generic features like edges and textures, they are often transferable across tasks. Therefore, only the last fully connected layer, which maps these features to specific object classes, needs to be reinitialized and retrained for the new set of objects.
* RNN Component: The RNN handles the temporal dynamics of the sequence data. Similar to the CNN, the earlier layers of the RNN capture general sequence patterns. Thus, reinitializing and retraining only the last layer of the RNN allows the model to adapt to the new prediction task without the need to retrain the entire network.
This approach minimizes training time and computational resources while effectively adapting the model to new tasks.

NEW QUESTION # 100
A Machine Learning Specialist is working with a media company to perform classification on popular articles from the company's website. The company is using random forests to classify how popular an article will be before it is published A sample of the data being used is below.
Given the dataset, the Specialist wants to convert the Day-Of_Week column to binary values.
What technique should be used to convert this column to binary values.

A. One-hot encoding
B. Normalization transformation
C. Tokenization
D. Binarization

Answer: A

Explanation:
Explanation
One-hot encoding is a technique that can be used to convert a categorical variable, such as the Day-Of_Week column, to binary values. One-hot encoding creates a new binary column for each unique value in the original column, and assigns a value of 1 to the column that corresponds to the value in the original column, and 0 to the rest. For example, if the original column has values Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, and Sunday, one-hot encoding will create seven new columns, each representing one day of the week. If the value in the original column is Tuesday, then the column for Tuesday will have a value of 1, and the other columns will have a value of 0. One-hot encoding can help improve the performance of machine learning models, as it eliminates the ordinal relationship between the values and creates a more informative and sparse representation of the data.
References:
One-Hot Encoding - Amazon SageMaker
One-Hot Encoding: A Simple Guide for Beginners | by Jana Schmidt ...
One-Hot Encoding in Machine Learning | by Nishant Malik | Towards ...

NEW QUESTION # 101
A company is using Amazon Textract to extract textual data from thousands of scanned text-heavy legal documents daily. The company uses this information to process loan applications automatically. Some of the documents fail business validation and are returned to human reviewers, who investigate the errors. This activity increases the time to process the loan applications.
What should the company do to reduce the processing time of loan applications?

A. Use an Amazon Textract synchronous operation instead of an asynchronous operation.
B. Configure Amazon Textract to route low-confidence predictions to Amazon SageMaker Ground Truth. Perform a manual review on those words before performing a business validation.
C. Configure Amazon Textract to route low-confidence predictions to Amazon Augmented AI (Amazon A2I). Perform a manual review on those words before performing a business validation.
D. Use Amazon Rekognition's feature to detect text in an image to extract the data from scanned images. Use this information to process the loan applications.

Answer: C

Explanation:
The company should configure Amazon Textract to route low-confidence predictions to Amazon Augmented AI (Amazon A2I). Amazon A2I is a service that allows you to implement human review of machine learning (ML) predictions. It also comes integrated with some of the Artificial Intelligence (AI) services such as Amazon Textract. By using Amazon A2I, the company can perform a manual review on those words that have low confidence scores before performing a business validation. This will help reduce the processing time of loan applications by avoiding errors and rework.
Option A is incorrect because Amazon SageMaker Ground Truth is not a suitable service for human review of Amazon Textract predictions. Amazon SageMaker Ground Truth is a service that helps you build highly accurate training datasets for machine learning. It allows you to label your own data or use a workforce of human labelers. However, it does not provide an easy way to integrate with Amazon Textract and route low-confidence predictions for human review.
Option B is incorrect because using an Amazon Textract synchronous operation instead of an asynchronous operation will not reduce the processing time of loan applications. A synchronous operation is a request-response operation that returns the results immediately. An asynchronous operation is a start-and-check operation that returns a job identifier that you can use to check the status and results later. The choice of operation depends on the size and complexity of the document, not on the confidence of the predictions.
Option D is incorrect because using Amazon Rekognition's feature to detect text in an image to extract the data from scanned images is not a better alternative than using Amazon Textract. Amazon Rekognition is a service that provides computer vision capabilities, such as face recognition, object detection, and scene analysis. It can also detect text in an image, but it does not provide the same level of accuracy and functionality as Amazon Textract. Amazon Textract can not only detect text, but also extract data from tables and forms, and understand the layout and structure of the document.
References:
Amazon Augmented AI
Amazon SageMaker Ground Truth
Amazon Textract Operations
Amazon Rekognition

NEW QUESTION # 102
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. XGBoost
D. Random Cut Forest (RCF)
E. K-nearest neighbors (k-NN)

Answer: B,C

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 # 103
During mini-batch training of a neural network for a classification problem, a Data Scientist notices that training accuracy oscillates What is the MOST likely cause of this issue?

A. The batch size is too big
B. The class distribution in the dataset is imbalanced
C. The learning rate is very high
D. Dataset shuffling is disabled

Answer: C

NEW QUESTION # 104
......

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