Download MLS-C01 Dumps

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QUESTION:1 A Machine Learning Specialist needs to be able to ingest streaming data and store it in Apache Parquet files for exploration and analysis. Which of the following services would both ingest and store this data in the correct format A. AWSDMS B. Amazon Kinesis Data Streams C. Amazon Kinesis Data Firehose D. Amazon Kinesis Data Analytics Answer: C

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QUESTION:2 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 instances Amazon EBS volume and needs to take a snapshot of that EBS volume. However the ML Specialist cannot find the Amazon SageMaker notebook instances 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. Answer: C

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QUESTION:3 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 Answer: B

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QUESTION:4 A retail company intends to use machine learning to categorize new products A labeled dataset of current products was provided to the Data Science team The dataset includes 1 200 products The labeled dataset has 15 features for each product such as title dimensions weight and price Each product is labeled as belonging to one of six categories such as books games electronics and movies. Which model should be used for categorizing new products using the provided dataset for training A. An XGBoost model where the objective parameter is set to multi: softmax B. A deep convolutional neural network CNN with a softmax activation function for the last layer C. A regression forest where the number of trees is set equal to the number of product categories D. A DeepAR forecasting model based on a recurrent neural network RNN Answer: B

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A manufacturing company asks its Machine Learning Specialist to develop a model that classifies defective parts into one of eight defect types. The company has provided roughly 100000 images per defect type for training During the injial training of the image classification model the Specialist notices that the validation accuracy is 80 while the training accuracy is 90 It is known that human-level performance for this type of image classification is around 90 What should the Specialist consider to fix this issue1 A. A longer training time B. Making the network larger C. Using a different optimizer D. Using some form of regularization QUESTION:5 Answer: D

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