A company employs Amazon SageMaker for its machine learning processes. During a compliance audit, it is discovered that an Amazon S3 bucket containing training data is encrypted using server-side encryption with S3 managed keys (SSE-S3). The company requires the use of customer managed keys instead.
In response, an ML engineer updates the S3 bucket to use server-side encryption with AWS KMS keys (SSE-KMS) without making any other configuration changes. However, following this update, SageMaker training jobs begin to fail with AccessDenied errors.
What steps should the ML engineer take to resolve this issue?
A. Update the IAM policy that is attached to the execution role for the training jobs. Include the s3:ListBucket and s3:GetObject permissions.A company needs to create a central catalog for all the company's ML models. The models are in AWS accounts where the company developed the models initially. The models are hosted in Amazon Elastic Container Registry (Amazon ECR)
repositories.
Which solution will meet these requirements?
A. Configure ECR cross-account replication for each existing ECR repository. Ensure that each model is visible in each AWS account.A medical company is leveraging AWS to develop a tool that recommends treatments for patients. It has collected health records and self-reported textual information in English. The goal is to extract insights from this data with minimal development effort.
Which solution would best meet these requirements?
A. Use Amazon SageMaker to build a recurrent neural network (RNN) to summarize the data.A company needs to give its ML engineers appropriate access to training data. The ML engineers must access training data from only their own business group. The ML engineers must not be allowed to access training data from other
business groups. The company uses a single AWS account and stores all the training data in Amazon S3 buckets. All ML model training occurs in Amazon SageMaker.
Which solution will provide the ML engineers with the appropriate access?
A. Enable S3 bucket versioning.An ML engineer needs to merge and transform data from two sources to retrain an existing ML model:
1.One source consists of .csv files stored in an Amazon S3 bucket, each containing millions of records.
2.The other source is an Amazon Aurora DB cluster.
The merged and transformed data must be written to a second S3 bucket, and this process needs to run weekly with minimal operational overhead.
Which solution would best fulfill these requirements?
A. Create a transient Amazon EMR cluster every week. Use the cluster to run an Apache Spark job to merge and transform the data.You have a dataset with highly imbalanced classes. Which technique would be most effective for improving model performance?
A. Increase the learning rateAn ML engineer is using Amazon SageMaker to train a deep learning model that requires distributed training. After some training attempts, the ML engineer observes that the instances are not performing as expected. The ML engineer
identifies communication overhead between the training instances.
What should the ML engineer do to MINIMIZE the communication overhead between the instances?
A. Place the instances in the same VPC subnet. Store the data in a different AWS Region from where the instances are deployed.A medical company needs to store clinical data that includes personally identifiable information (PII) and protected health information (PHI). An ML engineer must implement a solution to ensure that PII and PHI are excluded from training ML models.
Which solution would best satisfy these requirements?
A. Store the clinical data in Amazon S3 buckets. Use AWS Glue DataBrew to mask the PII and PHI before the data is used for model training.A company has deployed an ML model utilizing the XGBoost algorithm to predict product failures. The model is hosted on an Amazon SageMaker endpoint and was trained using normal operating data. Predictions are delivered to the
company's application via an AWS Lambda function.
An ML engineer must design a solution that continuously monitors incoming live data to detect any decline in the model's accuracy over time.
Which solution would effectively address these requirements?
A. Use Amazon CloudWatch to create a dashboard that monitors real-time inference data and model predictions. Use the dashboard to detect drift.An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region. The ML engineer needs to copy the model to Account B in the same Region. Which solution will meet this requirement with the LEAST development effort?
A. Use Amazon S3 to make a copy of the model. Transfer the copy to AccountNowadays, the certification exams become more and more important and required by more and more enterprises when applying for a job. But how to prepare for the exam effectively? How to prepare for the exam in a short time with less efforts? How to get a ideal result and how to find the most reliable resources? Here on Vcedump.com, you will find all the answers. Vcedump.com provide not only Amazon exam questions, answers and explanations but also complete assistance on your exam preparation and certification application. If you are confused on your MLA-C01 exam preparations and Amazon certification application, do not hesitate to visit our Vcedump.com to find your solutions here.