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                     Amazon Web Services
MLS-C01
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Amazon Web Services - MLS-C01
Question #:1
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.
Answer: C
Question #:2
A Machine Learning Specialist deployed a model that provides product recommendations on a company's
website Initially, the model was performing very well and resulted in customers buying more products on
average However within the past few months the Specialist has noticed that the effect of product
recommendations has diminished and customers are starting to return to their original habits of spending less
The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a
year ago
Which method should the Specialist try to improve model performance?
A.  The model needs to be completely re-engineered because it is unable to handle product inventory
changes
B.  The model's hyperparameters should be periodically updated to prevent drift
C.  The model should be periodically retrained from scratch using the original data while adding a
regularization term to handle product inventory changes
D.  The model should be periodically retrained using the original training data plus new data as product
inventory changes
Answer: D
Amazon Web Services - MLS-C01
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
Question #:4
A company is setting up an Amazon SageMaker environment. The corporate data security policy does not
allow communication over the internet.
How can the company enable the Amazon SageMaker service without enabling direct internet access to
Amazon SageMaker notebook instances?
A.  Create a NAT gateway within the corporate VPC.
B.  Route Amazon SageMaker traffic through an on-premises network.
C.  Create Amazon SageMaker VPC interface endpoints within the corporate VPC.
D.  Create VPC peering with Amazon VPC hosting Amazon SageMaker.
Answer: A
Amazon Web Services - MLS-C01
Question #:5
A monitoring service generates 1 TB of scale metrics record data every minute A Research team performs
queries on this data using Amazon Athena The queries run slowly due to the large volume of data, and the
team requires better performance
How should the records be stored in Amazon S3 to improve query performance?
A.  CSV files
B.  Parquet files
C.  Compressed JSON
D.  RecordIO
Answer: B
Question #:6
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
Answer: D
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