With the help of Our MLS-C01 braindumps you can easily pass your Amazon MLS-C01 Exam. If you already done your preparation for exam then we have perfect tool to check your preparation before going for exam. You can attempt exam in exam mode. Exactly same scenario will be provided by us for the help of students.
Our aim to help students not only earn. Our Aws exam question answers are fully verified by IT professionals that have number of year's experience. If you are scared to done transaction then you can check Amazon MLS-C01 demo before your order submission.
Our support staff available here 24/7 you can ask anything about your exam or you can ask for demo of your desired exam.
We are fully assure that you will not lose anything you will pass your AWS exam with highest possible scores.
Gupta
Just like me, anyone can bring the best grades by preparing from MLS-C01 dumps. I cleared my Amazon MLS-C01 simply by downloading exam material in PDF form from amazondumps.us. I am confident for my future performances with MLS-C01 braindumps.
Upadhyay
It is worth mentioning that how I aced my exam within a short time with the help of MLS-C01 braindumps. It is the most compact dumps material among all I ever used for my IT exam preparation. I will strongly suggest the IT students to go for their exam under the guidance of experts working at (amazondumps.us.
Sample Questions
Question 1
Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?
A. Recall
B. Misclassification rate
C. Mean absolute percentage error (MAPE)
D. Area Under the ROC Curve (AUC)
ANSWER : D
Question 2
A Machine Learning Specialist is developing a custom video recommendation model for an application The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance.
Which approach allows the Specialist to use all the data to train the model?
A. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training
code is executing and the model parameters seem reasonable. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
B. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to the
instance. Train on a small amount of the data to verify the training code and hyperparameters. Go back to
Amazon SageMaker and train using the full dataset
C. Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the
S3 bucket using
Pipe input mode.
D. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Launch an Amazon EC2 instance with an
AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.
ANSWER : A
Question 3
A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting. Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls.
What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?
A. Implement an AWS Lambda function to long Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
B. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
C. Implement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrail. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
D. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Set up Amazon SNS to receive a notification when the model is overfitting.
ANSWER : C
Question 4
A Machine Learning Specialist was given a dataset consisting of unlabeled data The Specialist must create a model that can help the team classify the data into different buckets What model should be used to complete this work?
A. K-means clustering
B. Random Cut Forest (RCF)
C. XGBoost
D. BlazingText
ANSWER : A
Question 5
An Amazon SageMaker notebook instance is launched into Amazon VPC The SageMaker notebook references data contained in an Amazon S3 bucket in another account The bucket is encrypted using SSE-KMS The instance returns an access denied error when trying to access data in Amazon S3.
Which of the following are required to access the bucket and avoid the access denied error? (Select THREE )
A. An AWS KMS key policy that allows access to the customer master key (CMK)
B. A SageMaker notebook security group that allows access to Amazon S3
C. An 1AM role that allows access to the specific S3 bucket
D. A permissive S3 bucket policy
E. An S3 bucket owner that matches the notebook owner
F. A SegaMaker notebook subnet ACL that allow traffic to Amazon S3.
ANSWER : A,C,F
