MLS-C01 test torrent, exam questions and answers for AWS Certified Machine Learning - Specialty

P.S. Free & New MLS-C01 dumps are available on Google Drive shared by VCE4Dumps: https://drive.google.com/open?id=1uhz3IpW6aEX1xSIldemlYIMWIqw4g4wj

Our MLS-C01 test questions provide free trial services for all customers so that you can better understand our products. You can experience the effects of outside products in advance by downloading clue versions of our MLS-C01 exam torrent. In addition, it has simple procedure to buy our learning materials. After your payment is successful, you will receive an e-mail from our company within 10 minutes. After you click on the link and log in, you can start learning using our MLS-C01 test material. You can download our MLS-C01 test questions at any time.

Achieving the Amazon MLS-C01 certification demonstrates an individual's proficiency in machine learning and their ability to design and implement machine learning solutions using AWS services. It is a valuable certification for professionals looking to advance their careers in the field of machine learning and work with cutting-edge technologies. AWS Certified Machine Learning - Specialty certification validates an individual's skills and knowledge in the field of machine learning and is recognized by employers worldwide.

The MLS-C01 Exam is a specialty certification that focuses on machine learning concepts and practices. It is designed for professionals who have a background in data science, computer science, or software engineering and want to specialize in machine learning. MLS-C01 exam is designed to test the candidate's ability to apply machine learning algorithms to solve real-world problems and build scalable solutions that can handle large data sets.

>> MLS-C01 Exam Questions Answers <<

From MLS-C01 Exam Questions Answers to AWS Certified Machine Learning - Specialty, Convenient to Pass

For exam applicants VCE4Dumps offers real Amazon MLS-C01 exam questions. There are three formats of the AWS Certified Machine Learning - Specialty (MLS-C01) practice material. These formats are PDF, desktop practice exam software, and web-based AWS Certified Machine Learning - Specialty (MLS-C01) practice exam. With these questions, you can crack the Amazon MLS-C01 certification exam and save your time and money.

Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) certification exam is designed to validate the candidate’s skills and knowledge in building, designing, deploying, and maintaining machine learning (ML) solutions using Amazon Web Services (AWS). AWS Certified Machine Learning - Specialty certification exam is ideal for professionals who are interested in pursuing a career in the field of AI and ML, or for those who want to enhance their existing skills in the field. The AWS Certified Machine Learning - Specialty certification is recognized globally and is a testament to the candidate’s expertise in the field of ML.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q30-Q35):

NEW QUESTION # 30
A financial services company wants to adopt Amazon SageMaker as its default data science environment. The company's data scientists run machine learning (ML) models on confidential financial dat a. The company is worried about data egress and wants an ML engineer to secure the environment.
Which mechanisms can the ML engineer use to control data egress from SageMaker? (Choose three.)

A. Restrict notebook presigned URLs to specific IPs used by the company.
B. Use SCPs to restrict access to SageMaker.
C. Enable network isolation for training jobs and models.
D. Disable root access on the SageMaker notebook instances.
E. Connect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink.
F. Protect data with encryption at rest and in transit. Use AWS Key Management Service (AWS KMS) to manage encryption keys.

Answer: C,E,F

Explanation:
To control data egress from SageMaker, the ML engineer can use the following mechanisms:
Connect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink. This allows the ML engineer to access SageMaker services and resources without exposing the traffic to the public internet. This reduces the risk of data leakage and unauthorized access1 Enable network isolation for training jobs and models. This prevents the training jobs and models from accessing the internet or other AWS services. This ensures that the data used for training and inference is not exposed to external sources2 Protect data with encryption at rest and in transit. Use AWS Key Management Service (AWS KMS) to manage encryption keys. This enables the ML engineer to encrypt the data stored in Amazon S3 buckets, SageMaker notebook instances, and SageMaker endpoints. It also allows the ML engineer to encrypt the data in transit between SageMaker and other AWS services. This helps protect the data from unauthorized access and tampering3 The other options are not effective in controlling data egress from SageMaker:
Use SCPs to restrict access to SageMaker. SCPs are used to define the maximum permissions for an organization or organizational unit (OU) in AWS Organizations. They do not control the data egress from SageMaker, but rather the access to SageMaker itself4 Disable root access on the SageMaker notebook instances. This prevents the users from installing additional packages or libraries on the notebook instances. It does not prevent the data from being transferred out of the notebook instances.
Restrict notebook presigned URLs to specific IPs used by the company. This limits the access to the notebook instances from certain IP addresses. It does not prevent the data from being transferred out of the notebook instances.
References:
1: Amazon SageMaker Interface VPC Endpoints (AWS PrivateLink) - Amazon SageMaker
2: Network Isolation - Amazon SageMaker
3: Encrypt Data at Rest and in Transit - Amazon SageMaker
4: Using Service Control Policies - AWS Organizations
5: Disable Root Access - Amazon SageMaker
6: Create a Presigned Notebook Instance URL - Amazon SageMaker

NEW QUESTION # 31
A retail company is ingesting purchasing records from its network of 20,000 stores to Amazon S3 by using Amazon Kinesis Data Firehose. The company uses a small, server-based application in each store to send the data to AWS over the internet. The company uses this data to train a machine learning model that is retrained each day. The company's data science team has identified existing attributes on these records that could be combined to create an improved model.
Which change will create the required transformed records with the LEAST operational overhead?

A. Deploy an Amazon EMR cluster that runs Apache Spark and includes the transformation logic. Use Amazon EventBridge (Amazon CloudWatch Events) to schedule an AWS Lambda function to launch the cluster each day and transform the records that accumulate in Amazon S3. Deliver the transformed records to Amazon S3.
B. Create an AWS Lambda function that can transform the incoming records. Enable data transformation on the ingestion Kinesis Data Firehose delivery stream. Use the Lambda function as the invocation target.
C. Launch a fleet of Amazon EC2 instances that include the transformation logic. Configure the EC2 instances with a daily cron job to transform the records that accumulate in Amazon S3. Deliver the transformed records to Amazon S3.
D. Deploy an Amazon S3 File Gateway in the stores. Update the in-store software to deliver data to the S3 File Gateway. Use a scheduled daily AWS Glue job to transform the data that the S3 File Gateway delivers to Amazon S3.

Answer: B

Explanation:
The solution A will create the required transformed records with the least operational overhead because it uses AWS Lambda and Amazon Kinesis Data Firehose, which are fully managed services that can provide the desired functionality. The solution A involves the following steps:
Create an AWS Lambda function that can transform the incoming records. AWS Lambda is a service that can run code without provisioning or managing servers. AWS Lambda can execute the transformation logic on the purchasing records and add the new attributes to the records1.
Enable data transformation on the ingestion Kinesis Data Firehose delivery stream. Use the Lambda function as the invocation target. Amazon Kinesis Data Firehose is a service that can capture, transform, and load streaming data into AWS data stores. Amazon Kinesis Data Firehose can enable data transformation and invoke the Lambda function to process the incoming records before delivering them to Amazon S3. This can reduce the operational overhead of managing the transformation process and the data storage2.
The other options are not suitable because:
Option B: Deploying an Amazon EMR cluster that runs Apache Spark and includes the transformation logic, using Amazon EventBridge (Amazon CloudWatch Events) to schedule an AWS Lambda function to launch the cluster each day and transform the records that accumulate in Amazon S3, and delivering the transformed records to Amazon S3 will incur more operational overhead than using AWS Lambda and Amazon Kinesis Data Firehose. The company will have to manage the Amazon EMR cluster, the Apache Spark application, the AWS Lambda function, and the Amazon EventBridge rule. Moreover, this solution will introduce a delay in the transformation process, as it will run only once a day3.
Option C: Deploying an Amazon S3 File Gateway in the stores, updating the in-store software to deliver data to the S3 File Gateway, and using a scheduled daily AWS Glue job to transform the data that the S3 File Gateway delivers to Amazon S3 will incur more operational overhead than using AWS Lambda and Amazon Kinesis Data Firehose. The company will have to manage the S3 File Gateway, the in-store software, and the AWS Glue job. Moreover, this solution will introduce a delay in the transformation process, as it will run only once a day4.
Option D: Launching a fleet of Amazon EC2 instances that include the transformation logic, configuring the EC2 instances with a daily cron job to transform the records that accumulate in Amazon S3, and delivering the transformed records to Amazon S3 will incur more operational overhead than using AWS Lambda and Amazon Kinesis Data Firehose. The company will have to manage the EC2 instances, the transformation code, and the cron job. Moreover, this solution will introduce a delay in the transformation process, as it will run only once a day5.
References:
1: AWS Lambda
2: Amazon Kinesis Data Firehose
3: Amazon EMR
4: Amazon S3 File Gateway
5: Amazon EC2

NEW QUESTION # 32
A health care company is planning to use neural networks to classify their X-ray images into normal and abnormal classes. The labeled data is divided into a training set of 1,000 images and a test set of 200 images.
The initial training of a neural network model with 50 hidden layers yielded 99% accuracy on the training set, but only 55% accuracy on the test set.
What changes should the Specialist consider to solve this issue? (Choose three.)

A. Enable dropout
B. Choose a smaller learning rate
C. Enable early stopping
D. Choose a higher number of layers
E. Choose a lower number of layers
F. Include all the images from the test set in the training set

Answer: A,C,E

Explanation:
The problem described in the question is a case of overfitting, where the neural network model performs well on the training data but poorly on the test data. This means that the model has learned the noise and specific patterns of the training data, but cannot generalize to new and unseen data. To solve this issue, the Specialist should consider the following changes:
Choose a lower number of layers: Reducing the number of layers can reduce the complexity and capacity of the neural network model, making it less prone to overfitting. A model with 50 hidden layers is likely too deep for the given data size and task. A simpler model with fewer layers can learn the essential features of the data without memorizing the noise.
Enable dropout: Dropout is a regularization technique that randomly drops out some units in the neural network during training. This prevents the units from co-adapting too much and forces the model to learn more robust features. Dropout can improve the generalization and test performance of the model by reducing overfitting.
Enable early stopping: Early stopping is another regularization technique that monitors the validation error during training and stops the training process when the validation error stops decreasing or starts increasing.
This prevents the model from overtraining on the training data and reduces overfitting.
Deep Learning - Machine Learning Lens
How to Avoid Overfitting in Deep Learning Neural Networks
How to Identify Overfitting Machine Learning Models in Scikit-Learn

NEW QUESTION # 33
A data scientist at a financial services company used Amazon SageMaker to train and deploy a model that predicts loan defaults. The model analyzes new loan applications and predicts the risk of loan default. To train the model, the data scientist manually extracted loan data from a database. The data scientist performed the model training and deployment steps in a Jupyter notebook that is hosted on SageMaker Studio notebooks. The model's prediction accuracy is decreasing over time. Which combination of slept in the MOST operationally efficient way for the data scientist to maintain the model's accuracy? (Select TWO.)

A. Use SageMaker Pipelines to create an automated workflow that extracts fresh data, trains the model, and deploys a new version of the model.
B. Store the model predictions in Amazon S3 Create a daily SageMaker Processing job that reads the predictions from Amazon S3, checks for changes in model prediction accuracy, and sends an email notification if a significant change is detected.
C. Configure SageMaker Model Monitor with an accuracy threshold to check for model drift. Initiate an Amazon CloudWatch alarm when the threshold is exceeded. Connect the workflow in SageMaker Pipelines with the CloudWatch alarm to automatically initiate retraining.
D. Export the training and deployment code from the SageMaker Studio notebooks into a Python script. Package the script into an Amazon Elastic Container Service (Amazon ECS) task that an AWS Lambda function can initiate.
E. Rerun the steps in the Jupyter notebook that is hosted on SageMaker Studio notebooks to retrain the model and redeploy a new version of the model.

Answer: A,C

Explanation:
Option A is correct because SageMaker Pipelines is a service that enables you to create and manage automated workflows for your machine learning projects. You can use SageMaker Pipelines to orchestrate the steps of data extraction, model training, and model deployment in a repeatable and scalable way1.
Option B is correct because SageMaker Model Monitor is a service that monitors the quality of your models in production and alerts you when there are deviations in the model quality. You can use SageMaker Model Monitor to set an accuracy threshold for your model and configure a CloudWatch alarm that triggers when the threshold is exceeded. You can then connect the alarm to the workflow in SageMaker Pipelines to automatically initiate retraining and deployment of a new version of the model2.
Option C is incorrect because it is not the most operationally efficient way to maintain the model's accuracy. Creating a daily SageMaker Processing job that reads the predictions from Amazon S3 and checks for changes in model prediction accuracy is a manual and time-consuming process. It also requires you to write custom code to perform the data analysis and send the email notification. Moreover, it does not automatically retrain and deploy the model when the accuracy drops.
Option D is incorrect because it is not the most operationally efficient way to maintain the model's accuracy. Rerunning the steps in the Jupyter notebook that is hosted on SageMaker Studio notebooks to retrain the model and redeploy a new version of the model is a manual and error-prone process. It also requires you to monitor the model's performance and initiate the retraining and deployment steps yourself. Moreover, it does not leverage the benefits of SageMaker Pipelines and SageMaker Model Monitor to automate and streamline the workflow.
Option E is incorrect because it is not the most operationally efficient way to maintain the model's accuracy. Exporting the training and deployment code from the SageMaker Studio notebooks into a Python script and packaging the script into an Amazon ECS task that an AWS Lambda function can initiate is a complex and cumbersome process. It also requires you to manage the infrastructure and resources for the Amazon ECS task and the AWS Lambda function. Moreover, it does not leverage the benefits of SageMaker Pipelines and SageMaker Model Monitor to automate and streamline the workflow.
References:
1: SageMaker Pipelines - Amazon SageMaker
2: Monitor data and model quality - Amazon SageMaker

NEW QUESTION # 34
A Machine Learning Specialist needs to create a data repository to hold a large amount of time-based training data for a new model. In the source system, new files are added every hour Throughout a single 24-hour period, the volume of hourly updates will change significantly. The Specialist always wants to train on the last 24 hours of the data Which type of data repository is the MOST cost-effective solution?

A. An Amazon EMR cluster with hourly hive partitions on Amazon EBS volumes
B. An Amazon S3 data lake with hourly object prefixes
C. An Amazon EBS-backed Amazon EC2 instance with hourly directories
D. An Amazon RDS database with hourly table partitions

Answer: B

Explanation:
An Amazon S3 data lake is a cost-effective solution for storing and analyzing large amounts of time-based training data for a new model. Amazon S3 is a highly scalable, durable, and secure object storage service that can store any amount of data in any format. Amazon S3 also offers low-cost storage classes, such as S3 Standard-IA and S3 One Zone-IA, that can reduce the storage costs for infrequently accessed data. By using hourly object prefixes, the Machine Learning Specialist can organize the data into logical partitions based on the time of ingestion. This can enable efficient data access and management, as well as support incremental updates and deletes. The Specialist can also use Amazon S3 lifecycle policies to automatically transition the data to lower-cost storage classes or delete the data after a certain period of time. This way, the Specialist can always train on the last 24 hours of the data and optimize the storage costs.
References:
What is a data lake? - Amazon Web Services
Amazon S3 Storage Classes - Amazon Simple Storage Service
Managing your storage lifecycle - Amazon Simple Storage Service
Best Practices Design Patterns: Optimizing Amazon S3 Performance

NEW QUESTION # 35
......

MLS-C01 Exam Bible: https://www.vce4dumps.com/MLS-C01-valid-torrent.html

BTW, DOWNLOAD part of VCE4Dumps MLS-C01 dumps from Cloud Storage: https://drive.google.com/open?id=1uhz3IpW6aEX1xSIldemlYIMWIqw4g4wj

Tom Harris Tom Harris

Biography

MLS-C01 test torrent, exam questions and answers for AWS Certified Machine Learning - Specialty

From MLS-C01 Exam Questions Answers to AWS Certified Machine Learning - Specialty, Convenient to Pass

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q30-Q35):

Newsletter Subscribe

Dive deep into your data with advanced analytics that reveal trends, patterns, and opportunities.

conatct

quick links

Course