What is AWS Certified Machine Learning - Specialty MLS-C01 Exam ?
The AWS Certified Machine Learning - Specialty (MLS-C01) Exam is a certification exam offered by Amazon Web Services (AWS) that validates an individual's expertise in designing, implementing, deploying, and maintaining machine learning solutions on AWS.
This exam is intended for individuals who have a deep understanding of machine learning technologies and at least two years of experience with AWS. It covers a range of topics including data engineering, exploratory data analysis, modeling, machine learning algorithms, model evaluation and optimization, and deployment and operationalization of machine learning models.
To pass the exam, candidates must demonstrate their ability to design and implement scalable, reliable, and cost-effective machine learning solutions on AWS, as well as optimize and deploy machine learning models to meet business needs.
Achieving this certification can help individuals demonstrate their skills and knowledge to potential employers and enhance their career prospects in the cloud computing industry. Additionally, this certification can help machine learning professionals differentiate themselves in the market and demonstrate their ability to design and manage advanced machine learning solutions on the AWS platform.
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Question 1
A company provisions Amazon SageMaker notebook instances for its data science teamand creates Amazon VPC interface endpoints to ensure communication between the VPCand the notebook instances. All connections to the Amazon SageMaker API are containedentirely and securely using the AWS network. However, the data science team realizes thatindividuals outside the VPC can still connect to the notebook instances across the internet.Which set of actions should the data science team take to fix the issue?
A. Modify the notebook instances' security group to allow traffic only from the CIDR rangesof the VPC. Apply this security group to all of the notebook instances' VPC interfaces.
B. Create an IAM policy that allows the sagemaker:CreatePresignedNotebooklnstanceUrland sagemaker:DescribeNotebooklnstance actions from only the VPC endpoints. Applythis policy to all IAM users, groups, and roles used to access the notebook instances.
C. Add a NAT gateway to the VPC. Convert all of the subnets where the AmazonSageMaker notebook instances are hosted to private subnets. Stop and start all of thenotebook instances to reassign only private IP addresses.
D. Change the network ACL of the subnet the notebook is hosted in to restrict access toanyone outside the VPC.
Answer: B
Question 2
A data scientist is working on a public sector project for an urban traffic system. Whilestudying the traffic patterns, it is clear to the data scientist that the traffic behavior at eachlight is correlated, subject to a small stochastic error term. The data scientist must modelthe traffic behavior to analyze the traffic patterns and reduce congestionHow will the data scientist MOST effectively model the problem?
A. The data scientist should obtain a correlated equilibrium policy by formulating thisproblem as a multi-agent reinforcement learning problem.
B. The data scientist should obtain the optimal equilibrium policy by formulating thisproblem as a single-agent reinforcement learning problem.
C. Rather than finding an equilibrium policy, the data scientist should obtain accuratepredictors of traffic flow by using historical data through a supervised learning approach.
D. Rather than finding an equilibrium policy, the data scientist should obtain accuratepredictors of traffic flow by using unlabeled simulated data representing the new trafficpatterns in the city and applying an unsupervised learning approach.
Answer: D
Question 3
A company is converting a large number of unstructured paper receipts into images. Thecompany wants to create a model based on natural language processing (NLP) to findrelevant entities such as date, location, and notes, as well as some custom entities such asreceipt numbers.The company is using optical character recognition (OCR) to extract text for data labeling.However, documents are in different structures and formats, and the company is facingchallenges with setting up the manual workflows for each document type. Additionally, thecompany trained a named entity recognition (NER) model for custom entity detection usinga small sample size. This model has a very low confidence score and will require retrainingwith a large dataset.Which solution for text extraction and entity detection will require the LEAST amount ofeffort?
A. Extract text from receipt images by using Amazon Textract. Use the AmazonSageMaker BlazingText algorithm to train on the text for entities and custom entities.
B. Extract text from receipt images by using a deep learning OCR model from the AWSMarketplace. Use the NER deep learning model to extract entities.
C. Extract text from receipt images by using Amazon Textract. Use Amazon Comprehendfor entity detection, and use Amazon Comprehend custom entity recognition for customentity detection.
D. Extract text from receipt images by using a deep learning OCR model from the AWSMarketplace. Use Amazon Comprehend for entity detection, and use Amazon Comprehendcustom entity recognition for custom entity detection.
Answer: C
Question 4
A company has set up and deployed its machine learning (ML) model into production withan endpoint using Amazon SageMaker hosting services. The ML team has configuredautomatic scaling for its SageMaker instances to support workload changes. During testing,the team notices that additional instances are being launched before the new instances areready. This behavior needs to change as soon as possible.How can the ML team solve this issue?
A. Decrease the cooldown period for the scale-in activity. Increase the configuredmaximum capacity of instances.
B. Replace the current endpoint with a multi-model endpoint using SageMaker.
C. Set up Amazon API Gateway and AWS Lambda to trigger the SageMaker inferenceendpoint.
D. Increase the cooldown period for the scale-out activity.
Answer: A
Question 5
A power company wants to forecast future energy consumption for its customers inresidential properties and commercial business properties. Historical power consumptiondata for the last 10 years is available. A team of data scientists who performed the initialdata analysis and feature selection will include the historical power consumption data anddata such as weather, number of individuals on the property, and public holidays.The data scientists are using Amazon Forecast to generate the forecasts.Which algorithm in Forecast should the data scientists use to meet these requirements?
A. Autoregressive Integrated Moving Average (AIRMA)
B. Exponential Smoothing (ETS)
C. Convolutional Neural Network - Quantile Regression (CNN-QR)
D. Prophet
Answer: B
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