HIGH PASS-RATE LATEST MLS-C01 TEST BLUEPRINT - PASS MLS-C01 EXAM

High Pass-Rate Latest MLS-C01 Test Blueprint - Pass MLS-C01 Exam

High Pass-Rate Latest MLS-C01 Test Blueprint - Pass MLS-C01 Exam

Blog Article

Tags: Latest MLS-C01 Test Blueprint, New MLS-C01 Dumps Book, MLS-C01 Exam Collection, Real MLS-C01 Torrent, Free MLS-C01 Updates

BONUS!!! Download part of Prep4away MLS-C01 dumps for free: https://drive.google.com/open?id=1xbtdJkoKkv0qgiv_X9hzoaT8aQyeF3z9

Prep4away AWS Certified Machine Learning - Specialty (MLS-C01) practice test has real AWS Certified Machine Learning - Specialty (MLS-C01) exam questions. You can change the difficulty of these questions, which will help you determine what areas appertain to more study before taking your Amazon MLS-C01 Exam Dumps. Here we listed some of the most important benefits you can get from using our Amazon MLS-C01 practice questions.

The Amazon MLS-C01 exam covers a range of topics related to machine learning on AWS, including data preparation, feature engineering, model selection and validation, machine learning algorithms, and deployment and operationalization of machine learning models. Candidates are also expected to have a good understanding of AWS services such as Amazon SageMaker, Amazon S3, AWS Lambda, and Amazon DynamoDB, and be able to use these services to build and deploy machine learning applications. The MLS-C01 Certification Exam is a valuable credential for professionals who are looking to demonstrate their expertise in machine learning on AWS and advance their careers in this field.

>> Latest MLS-C01 Test Blueprint <<

New Amazon MLS-C01 Dumps Book | MLS-C01 Exam Collection

As a responsible company with great reputation among the market, we trained our staff and employees with strict beliefs to help you with any problems about our MLS-C01 Learning materials 24/7. Even you have finished buying our MLS-C01 Study Guide with us, we still be around you with considerate services. In a word, our service will offer you the best help on Our MLS-C01 exam quiz. Just click on the contact button, you will receive our service.

The AWS-Certified-Machine-Learning-Specialty exam covers various topics, such as data engineering, exploratory data analysis, modeling, machine learning implementation and operations, and ethical and legal considerations. Candidates should be well-versed in these topics and should have hands-on experience using AWS services, such as Amazon SageMaker, Amazon S3, Amazon EC2, and Amazon Comprehend.

What is the duration of the AWS Certified Machine Learning - Specialty

  • Passing Score: 720
  • Length of Examination: 130 minutes
  • Number of Questions: 54
  • Format: Multiple choices, multiple answers
  • Language : English, Japanese, Korean, and Simplified Chinese

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q156-Q161):

NEW QUESTION # 156
A data scientist uses Amazon SageMaker Data Wrangler to analyze and visualize data. The data scientist wants to refine a training dataset by selecting predictor variables that are strongly predictive of the target variable. The target variable correlates with other predictor variables.
The data scientist wants to understand the variance in the data along various directions in the feature space.
Which solution will meet these requirements?

  • A. Use the SageMaker Data Wrangler Data Quality and Insights Report feature to review features by their predictive power.
  • B. Use the SageMaker Data Wrangler multicollinearity measurement features with the principal component analysis (PCA) algorithm to provide a feature space that includes all of the predictor variables.
  • C. Use the SageMaker Data Wrangler multicollinearity measurement features with a variance inflation factor (VIF) score. Use the VIF score as a measurement of how closely the variables are related to each other.
  • D. Use the SageMaker Data Wrangler Data Quality and Insights Report quick model visualization to estimate the expected quality of a model that is trained on the data.

Answer: B

Explanation:
Principal Component Analysis (PCA) is a dimensionality reduction technique that captures the variance within the feature space, helping to understand the directions in which data varies most. In SageMaker Data Wrangler, the multicollinearity measurement and PCA features allow the data scientist to analyze interdependencies between predictor variables while reducing redundancy. PCA transforms correlated features into a set of uncorrelated components, helping to simplify the dataset without significant loss of information, making it ideal for refining features based on variance.
Options A and D offer methods to understand feature relevance but are less effective for managing multicollinearity and variance representation in the data.


NEW QUESTION # 157
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Select THREE.)

  • A. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.
  • B. The validation channel identifying the location of validation data on an Amazon S3 bucket.
  • C. The training channel identifying the location of training data on an Amazon S3 bucket.
  • D. The output path specifying where on an Amazon S3 bucket the trained model will persist.
  • E. Hyperparameters in a JSON array as documented for the algorithm used.
  • F. The 1AM role that Amazon SageMaker can assume to perform tasks on behalf of the users.

Answer: C,D,F

Explanation:
Explanation
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, the common parameters that must be specified are:
The training channel identifying the location of training data on an Amazon S3 bucket. This parameter tells SageMaker where to find the input data for the algorithm and what format it is in. For example, TrainingInputMode: File means that the input data is in files stored in S3.
The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users. This parameter grants SageMaker the necessary permissions to access the S3 buckets, ECR repositories, and other AWS resources needed for the training job. For example, RoleArn:
arn:aws:iam::123456789012:role/service-role/AmazonSageMaker-ExecutionRole-20200303T150948 mea that SageMaker will use the specified role to run the training job.
The output path specifying where on an Amazon S3 bucket the trained model will persist. This parameter tells SageMaker where to save the model artifacts, such as the model weights and parameters, after the training job is completed. For example, OutputDataConfig: {S3OutputPath:
s3://my-bucket/my-training-job} means that SageMaker will store the model artifacts in the specified S3 location.
The validation channel identifying the location of validation data on an Amazon S3 bucket is an optional parameter that can be used to provide a separate dataset for evaluating the model performance during the training process. This parameter is not required for all algorithms and can be omitted if the validation data is not available or not needed.
The hyperparameters in a JSON array as documented for the algorithm used is another optional parameter that can be used to customize the behavior and performance of the algorithm. This parameter is specific to each algorithm and can be used to tune the model accuracy, speed, complexity, and other aspects. For example, HyperParameters: {num_round: "10", objective: "binary:logistic"} means that the XGBoost algorithm will use 10 boosting rounds and the logistic loss function for binary classification.
The Amazon EC2 instance class specifying whether training will be run using CPU or GPU is not a parameter that is specified when submitting a training job using a built-in algorithm. Instead, this parameter is specified when creating a training instance, which is a containerized environment that runs the training code and algorithm. For example, ResourceConfig: {InstanceType: ml.m5.xlarge, InstanceCount: 1, VolumeSizeInGB:
10} means that SageMaker will use one m5.xlarge instance with 10 GB of storage for the training instance.
References:
Train a Model with Amazon SageMaker
Use Amazon SageMaker Built-in Algorithms or Pre-trained Models
CreateTrainingJob - Amazon SageMaker Service


NEW QUESTION # 158
A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only.
What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?

  • A. Build the Docker image with the inference code. Tag the Docker image with the registry hostname andupload it to Amazon ECR.
  • B. Serialize the trained model so the format is compressed for deployment. Tag the Docker image with theregistry hostname and upload it to Amazon S3.
  • C. Serialize the trained model so the format is compressed for deployment. Build the image and upload it toDocker Hub.
  • D. Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.

Answer: A

Explanation:
To deploy a model that was trained locally to Amazon SageMaker, the steps are:
* Build the Docker image with the inference code. The inference code should include the model loading, data preprocessing, prediction, and postprocessing logic. The Docker image should also include the dependencies and libraries required by the inference code and the model.
* Tag the Docker image with the registry hostname and upload it to Amazon ECR. Amazon ECR is a fully managed container registry that makes it easy to store, manage, and deploy container images. The registry hostname is the Amazon ECR registry URI for your account and Region. You can use the AWS CLI or the Amazon ECR console to tag and push the Docker image to Amazon ECR.
* Create a SageMaker model entity that points to the Docker image in Amazon ECR and the model artifacts in Amazon S3. The model entity is a logical representation of the model that contains the information needed to deploy the model for inference. The model artifacts are the files generated by the model training process, such as the model parameters and weights. You can use the AWS CLI, the SageMaker Python SDK, or the SageMaker console to create the model entity.
* Create an endpoint configuration that specifies the instance type and number of instances to use for hosting the model. The endpoint configuration also defines the production variants, which are the different versions of the model that you want to deploy. You can use the AWS CLI, the SageMaker Python SDK, or the SageMaker console to create the endpoint configuration.
* Create an endpoint that uses the endpoint configuration to deploy the model. The endpoint is a web service that exposes an HTTP API for inference requests. You can use the AWS CLI, the SageMaker Python SDK, or the SageMaker console to create the endpoint.
AWS Machine Learning Specialty Exam Guide
AWS Machine Learning Training - Deploy a Model on Amazon SageMaker
AWS Machine Learning Training - Use Your Own Inference Code with Amazon SageMaker Hosting Services


NEW QUESTION # 159
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Choose three.)

  • A. The validation channel identifying the location of validation data on an Amazon S3 bucket.
  • B. The training channel identifying the location of training data on an Amazon S3 bucket.
  • C. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.
  • D. The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users.
  • E. The output path specifying where on an Amazon S3 bucket the trained model will persist.
  • F. Hyperparameters in a JSON array as documented for the algorithm used.

Answer: B,C,E


NEW QUESTION # 160
A bank's Machine Learning team is developing an approach for credit card fraud detection The company has a large dataset of historical data labeled as fraudulent The goal is to build a model to take the information from new transactions and predict whether each transaction is fraudulent or not Which built-in Amazon SageMaker machine learning algorithm should be used for modeling this problem?

  • A. Random Cut Forest (RCF)
  • B. Seq2seq
  • C. XGBoost
  • D. K-means

Answer: C

Explanation:
XGBoost is a built-in Amazon SageMaker machine learning algorithm that should be used for modeling the credit card fraud detection problem. XGBoost is an algorithm that implements a scalable and distributed gradient boosting framework, which is a popular and effective technique for supervised learning problems. Gradient boosting is a method of combining multiple weak learners, such as decision trees, into a strong learner, by iteratively fitting new models to the residual errors of the previous models and adding them to the ensemble. XGBoost can handle various types of data, such as numerical, categorical, or text, and can perform both regression and classification tasks. XGBoost also supports various features and optimizations, such as regularization, missing value handling, parallelization, and cross-validation, that can improve the performance and efficiency of the algorithm.
XGBoost is suitable for the credit card fraud detection problem for the following reasons:
The problem is a binary classification problem, where the goal is to predict whether a transaction is fraudulent or not, based on the information from new transactions. XGBoost can perform binary classification by using a logistic regression objective function and outputting the probability of the positive class (fraudulent) for each transaction.
The problem involves a large and imbalanced dataset of historical data labeled as fraudulent. XGBoost can handle large-scale and imbalanced data by using distributed and parallel computing, as well as techniques such as weighted sampling, class weighting, or stratified sampling, to balance the classes and reduce the bias towards the majority class (non-fraudulent).
The problem requires a high accuracy and precision for detecting fraudulent transactions, as well as a low false positive rate for avoiding false alarms. XGBoost can achieve high accuracy and precision by using gradient boosting, which can learn complex and non-linear patterns from the data and reduce the variance and overfitting of the model. XGBoost can also achieve a low false positive rate by using regularization, which can reduce the complexity and noise of the model and prevent it from fitting spurious signals in the data.
The other options are not as suitable as XGBoost for the credit card fraud detection problem for the following reasons:
Seq2seq: Seq2seq is an algorithm that implements a sequence-to-sequence model, which is a type of neural network model that can map an input sequence to an output sequence. Seq2seq is mainly used for natural language processing tasks, such as machine translation, text summarization, or dialogue generation. Seq2seq is not suitable for the credit card fraud detection problem, because the problem is not a sequence-to-sequence task, but a binary classification task. The input and output of the problem are not sequences of words or tokens, but vectors of features and labels.
K-means: K-means is an algorithm that implements a clustering technique, which is a type of unsupervised learning method that can group similar data points into clusters. K-means is mainly used for exploratory data analysis, dimensionality reduction, or anomaly detection. K-means is not suitable for the credit card fraud detection problem, because the problem is not a clustering task, but a classification task. The problem requires using the labeled data to train a model that can predict the labels of new data, not finding the optimal number of clusters or the cluster memberships of the data.
Random Cut Forest (RCF): RCF is an algorithm that implements an anomaly detection technique, which is a type of unsupervised learning method that can identify data points that deviate from the normal behavior or distribution of the data. RCF is mainly used for detecting outliers, frauds, or faults in the data. RCF is not suitable for the credit card fraud detection problem, because the problem is not an anomaly detection task, but a classification task. The problem requires using the labeled data to train a model that can predict the labels of new data, not finding the anomaly scores or the anomalous data points in the data.
References:
XGBoost Algorithm
Use XGBoost for Binary Classification with Amazon SageMaker
Seq2seq Algorithm
K-means Algorithm
[Random Cut Forest Algorithm]


NEW QUESTION # 161
......

New MLS-C01 Dumps Book: https://www.prep4away.com/Amazon-certification/braindumps.MLS-C01.ete.file.html

What's more, part of that Prep4away MLS-C01 dumps now are free: https://drive.google.com/open?id=1xbtdJkoKkv0qgiv_X9hzoaT8aQyeF3z9

Report this page