PRACTICE MLS-C01 TEST ONLINE & RELIABLE MLS-C01 BRAINDUMPS FREE

Practice MLS-C01 Test Online & Reliable MLS-C01 Braindumps Free

Practice MLS-C01 Test Online & Reliable MLS-C01 Braindumps Free

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Professionals who earn the Amazon MLS-C01 certification are highly sought after by employers because of their ability to design and implement machine learning solutions on AWS. AWS Certified Machine Learning - Specialty certification is designed for individuals who have experience working with AWS services and who want to specialize in machine learning. It is an excellent choice for data scientists, data engineers, software developers, and IT professionals who want to advance their careers in the field of AI and machine learning.

The AWS-Certified-Machine-Learning-Specialty exam consists of 65 multiple-choice and multiple-response questions, and candidates have 180 minutes to complete the exam. MLS-C01 Exam covers a wide range of topics, including data preparation, feature engineering, model selection and evaluation, and deployment and implementation.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q191-Q196):

NEW QUESTION # 191
Amazon Connect has recently been tolled out across a company as a contact call center The solution has been configured to store voice call recordings on Amazon S3 The content of the voice calls are being analyzed for the incidents being discussed by the call operators Amazon Transcribe is being used to convert the audio to text, and the output is stored on Amazon S3 Which approach will provide the information required for further analysis?

  • A. Use Amazon Translate with the transcribed files to train and build a model for the key topics
  • B. Use Amazon Comprehend with the transcribed files to build the key topics
  • C. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the transcribed files to generate a word embeddings dictionary for the key topics
  • D. Use the AWS Deep Learning AMI with Gluon Semantic Segmentation on the transcribed files to train and build a model for the key topics

Answer: B

Explanation:
Explanation
Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. It can analyze text documents and identify the key topics, entities, sentiments, languages, and more. In this case, Amazon Comprehend can be used with the transcribed files from Amazon Transcribe to extract the main topics that are being discussed by the call operators. This can help to understand the common issues and concerns of the customers, and provide insights for further analysis and improvement. References:
Amazon Comprehend - Amazon Web Services
AWS Certified Machine Learning - Specialty Sample Questions


NEW QUESTION # 192
A Machine Learning Specialist is building a convolutional neural network (CNN) that will classify 10 types of animals. The Specialist has built a series of layers in a neural network that will take an input image of an animal, pass it through a series of convolutional and pooling layers, and then finally pass it through a dense and fully connected layer with 10 nodes The Specialist would like to get an output from the neural network that is a probability distribution of how likely it is that the input image belongs to each of the 10 classes Which function will produce the desired output?

  • A. Rectified linear units (ReLU)
  • B. Dropout
  • C. Softmax
  • D. Smooth L1 loss

Answer: C


NEW QUESTION # 193
A Machine Learning team runs its own training algorithm on Amazon SageMaker. The training algorithm requires external assets. The team needs to submit both its own algorithm code and algorithm-specific parameters to Amazon SageMaker.
What combination of services should the team use to build a custom algorithm in Amazon SageMaker?
(Choose two.)

  • A. Amazon ECS
  • B. AWS Secrets Manager
  • C. Amazon S3
  • D. Amazon ECR
  • E. AWS CodeStar

Answer: C,D

Explanation:
The Machine Learning team wants to use its own training algorithm on Amazon SageMaker, and submit both its own algorithm code and algorithm-specific parameters. The best combination of services to build a custom algorithm in Amazon SageMaker are Amazon ECR and Amazon S3.
Amazon ECR is a fully managed container registry service that allows you to store, manage, and deploy Docker container images. You can use Amazon ECR to create a Docker image that contains your training algorithm code and any dependencies or libraries that it requires. You can also use Amazon ECR to push, pull, and manage your Docker images securely and reliably.
Amazon S3 is a durable, scalable, and secure object storage service that can store any amount and type of data. You can use Amazon S3 to store your training data, model artifacts, and algorithm-specific parameters.
You can also use Amazon S3 to access your data and parameters from your training algorithm code, and to write your model output to a specified location.
Therefore, the Machine Learning team can use the following steps to build a custom algorithm in Amazon SageMaker:
Write the training algorithm code in Python, using the Amazon SageMaker Python SDK or the Amazon SageMaker Containers library to interact with the Amazon SageMaker service. The code should be able to read the input data and parameters from Amazon S3, and write the model output to Amazon S3.
Create a Dockerfile that defines the base image, the dependencies, the environment variables, and the commands to run the training algorithm code. The Dockerfile should also expose the ports that Amazon SageMaker uses to communicate with the container.
Build the Docker image using the Dockerfile, and tag it with a meaningful name and version.
Push the Docker image to Amazon ECR, and note the registry path of the image.
Upload the training data, model artifacts, and algorithm-specific parameters to Amazon S3, and note the S3 URIs of the objects.
Create an Amazon SageMaker training job, using the Amazon SageMaker Python SDK or the AWS CLI.
Specify the registry path of the Docker image, the S3 URIs of the input and output data, the algorithm- specific parameters, and other configuration options, such as the instance type, the number of instances, the IAM role, and the hyperparameters.
Monitor the status and logs of the training job, and retrieve the model output from Amazon S3.
Use Your Own Training Algorithms
Amazon ECR - Amazon Web Services
Amazon S3 - Amazon Web Services


NEW QUESTION # 194
A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.

Based on this information, which model would have the HIGHEST recall with respect to the fraudulent class?

  • A. Single Perceptron with sigmoidal activation function
  • B. Linear support vector machine (SVM)
  • C. Decision tree
  • D. Naive Bayesian classifier

Answer: C

Explanation:
Based on the figure provided, a decision tree would have the highest recall with respect to the fraudulent class. Recall is a model evaluation metric that measures the proportion of actual positive instances that are correctly classified by the model. Recall is calculated as follows:
Recall = True Positives / (True Positives + False Negatives)
A decision tree is a type of machine learning model that can perform classification tasks by splitting the data into smaller and purer subsets based on a series of rules or conditions. A decision tree can handle both linear and non-linear data, and can capture complex patterns and interactions among the features. A decision tree can also be easily visualized and interpreted1 In this case, the data is not linearly separable, and has a clear pattern of seasonality. The fraudulent class forms a large circle in the center of the plot, while the normal class is scattered around the edges. A decision tree can use the transaction month and the age of account as the splitting criteria, and create a circular boundary that separates the fraudulent class from the normal class. A decision tree can achieve a high recall for the fraudulent class, as it can correctly identify most of the black dots as positive instances, and minimize the number of false negatives. A decision tree can also adjust the depth and complexity of the tree to balance the trade-off between recall and precision23 The other options are not valid or suitable for achieving a high recall for the fraudulent class. A linear support vector machine (SVM) is a type of machine learning model that can perform classification tasks by finding a linear hyperplane that maximizes the margin between the classes. A linear SVM can handle linearly separable data, but not non-linear data. A linear SVM cannot capture the circular pattern of the fraudulent class, and may misclassify many of the black dots as negative instances, resulting in a low recall4 A naive Bayesian classifier is a type of machine learning model that can perform classification tasks by applying the Bayes' theorem and assuming conditional independence among the features. A naive Bayesian classifier can handle both linear and non-linear data, and can incorporate prior knowledge and probabilities into the model. However, a naive Bayesian classifier may not perform well when the features are correlated or dependent, as in this case. A naive Bayesian classifier may not capture the circular pattern of the fraudulent class, and may misclassify many of the black dots as negative instances, resulting in a low recall5 A single perceptron with sigmoidal activation function is a type of machine learning model that can perform classification tasks by applying a weighted linear combination of the features and a non-linear activation function. A single perceptron with sigmoidal activation function can handle linearly separable data, but not non-linear data. A single perceptron with sigmoidal activation function cannot capture the circular pattern of the fraudulent class, and may misclassify many of the black dots as negative instances, resulting in a low recall.


NEW QUESTION # 195
A Machine Learning Specialist has completed a proof of concept for a company using a small data sample, and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker. The historical training data is stored in Amazon RDS.
Which approach should the Specialist use for training a model using that data?

  • A. Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.
  • B. Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in.
  • C. Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
  • D. Write a direct connection to the SQL database within the notebook and pull data in

Answer: C


NEW QUESTION # 196
......

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