Amazon Amazon Certifications MLS-C01 Questions & Answers

• Question 311:

  A company is running an Amazon SageMaker training job that will access data stored in its Amazon S3 bucket A compliance policy requires that the data never be transmitted across the internet How should the company set up the job?

  A. Launch the notebook instances in a public subnet and access the data through the public S3 endpoint

  B. Launch the notebook instances in a private subnet and access the data through a NAT gateway

  C. Launch the notebook instances in a public subnet and access the data through a NAT gateway

  D. Launch the notebook instances in a private subnet and access the data through an S3 VPC endpoint.

  Correct Answer: D

• Question 312:

  A Machine Learning Specialist is building a logistic regression model that will predict whether or not a person will order a pizza. The Specialist is trying to build the optimal model with an ideal classification threshold.

  What model evaluation technique should the Specialist use to understand how different classification thresholds will impact the model's performance?

  A. Receiver operating characteristic (ROC) curve

  B. Misclassification rate

  C. Root Mean Square Error (RMand)

  D. L1 norm

  Correct Answer: A

  Reference: https://docs.aws.amazon.com/machine-learning/latest/dg/binary-model-insights.html

• Question 313:

  A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance's Amazon EBS volume, and needs to take a snapshot of that EBS volume. However the ML Specialist cannot find the Amazon SageMaker notebook instance's EBS volume or Amazon EC2 instance within the VPC.

  Why is the ML Specialist not seeing the instance visible in the VPC?

  A. Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.

  B. Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.

  C. Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.

  D. Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.

  Correct Answer: C

  Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/gs-setup-working-env.html

• Question 314:

  A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable

  What should be done to reduce the impact of having such a large number of features?

  A. Perform one-hot encoding on highly correlated features

  B. Use matrix multiplication on highly correlated features.

  C. Create a new feature space using principal component analysis (PCA)

  D. Apply the Pearson correlation coefficient

  Correct Answer: C

• Question 315:

  A Machine Learning Specialist is configuring Amazon SageMaker so multiple Data Scientists can access notebooks, train models, and deploy endpoints. To ensure the best operational performance, the Specialist needs to be able to track how often the Scientists are deploying models, GPU and CPU utilization on the deployed SageMaker endpoints, and all errors that are generated when an endpoint is invoked.

  Which services are integrated with Amazon SageMaker to track this information? (Choose two.)

  A. AWS CloudTrail

  B. AWS Health

  C. AWS Trusted Advisor

  D. Amazon CloudWatch

  E. AWS Config

  Correct Answer: AD

  Reference: https://aws.amazon.com/sagemaker/faqs/

• Question 316:

  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 EBS-backed Amazon EC2 instance with hourly directories

  B. An Amazon RDS database with hourly table partitions

  C. An Amazon S3 data lake with hourly object prefixes

  D. An Amazon EMR cluster with hourly hive partitions on Amazon EBS volumes

  Correct Answer: C

• Question 317:

  A large JSON dataset for a project has been uploaded to a private Amazon S3 bucket The Machine Learning Specialist wants to securely access and explore the data from an Amazon SageMaker notebook instance A new VPC was created and assigned to the Specialist

  How can the privacy and integrity of the data stored in Amazon S3 be maintained while granting access to the Specialist for analysis?

  A. Launch the SageMaker notebook instance within the VPC with SageMaker-provided internet access enabled Use an S3 ACL to open read privileges to the everyone group

  B. Launch the SageMaker notebook instance within the VPC and create an S3 VPC endpoint for the notebook to access the data Copy the JSON dataset from Amazon S3 into the ML storage volume on the SageMaker notebook instance and work against the local dataset

  C. Launch the SageMaker notebook instance within the VPC and create an S3 VPC endpoint for the notebook to access the data Define a custom S3 bucket policy to only allow requests from your VPC to access the S3 bucket

  D. Launch the SageMaker notebook instance within the VPC with SageMaker-provided internet access enabled. Generate an S3 pre-signed URL for access to data in the bucket

  Correct Answer: B

• Question 318:

  A Data Science team within a large company uses Amazon SageMaker notebooks to access data stored in Amazon S3 buckets. The IT Security team is concerned that internet-enabled notebook instances create a security vulnerability where malicious code running on the instances could compromise data privacy. The company mandates that all instances stay within a secured VPC with no internet access, and data communication traffic must stay within the AWS network.

  How should the Data Science team configure the notebook instance placement to meet these requirements?

  A. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Place the Amazon SageMaker endpoint and S3 buckets within the same VPC.

  B. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Use 1AM policies to grant access to Amazon S3 and Amazon SageMaker.

  C. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it.

  D. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has a NAT gateway and an associated security group allowing only outbound connections to Amazon S3 and Amazon SageMaker

  Correct Answer: C

  https://docs.aws.amazon.com/sagemaker/latest/dg/notebook-interface-endpoint.html

• Question 319:

  A Machine Learning Specialist wants to determine the appropriate SageMakerVariantInvocationsPerInstance setting for an endpoint automatic scaling configuration. The Specialist has performed a load test on a single instance and determined that peak requests per second (RPS) without service degradation is about 20 RPS. As this is the first deployment, the Specialist intends to set the invocation safety factor to 0.5.

  Based on the stated parameters and given that the invocations per instance setting is measured on a per-minute basis, what should the Specialist set as the SageMakerVariantInvocationsPerInstance setting?

  A. 10

  B. 30

  C. 600

  D. 2,400

  Correct Answer: C

  SageMakerVariantInvocationsPerInstance = (MAX_RPS * SAFETY_FACTOR) * 60 AWS recommended Saf_fac =0 .5

  https://docs.aws.amazon.com/sagemaker/latest/dg/endpoint-scaling-loadtest.html

• Question 320:

  A Machine Learning Specialist has built a model using Amazon SageMaker built-in algorithms and is not getting expected accurate results The Specialist wants to use hyperparameter optimization to increase the model's accuracy Which method is the MOST repeatable and requires the LEAST amount of effort to achieve this?

  A. Launch multiple training jobs in parallel with different hyperparameters

  B. Create an AWS Step Functions workflow that monitors the accuracy in Amazon CloudWatch Logs and relaunches the training job with a defined list of hyperparameters

  C. Create a hyperparameter tuning job and set the accuracy as an objective metric.

  D. Create a random walk in the parameter space to iterate through a range of values that should be used for each individual hyperparameter

  Correct Answer: C