Latest update Amazon MLS-C01 exam dumps tips and practice tests

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Table Of Content:

  1. Amazon MLS-C01 exam tips
  2. Amazon MLS-C01 exam details
  3. Amazon MLS-C01 Exam process
  4. Amazon MLS-C01 exam dumps (pdf + vce)
  5. Free sharing of Amazon MLS-C01 practice test questions
  6. Amazon MLS-C01 Coupon Code

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Amazon MLS-C01 exam details

Vendor: Amazon
Exam Code: MLS-C01
Exam Name: AWS Certified Machine Learning – Specialty (MLS-C01)
Certification: AWS Certified Specialty
Total Questions: 160 Q&A
Exam Language: English

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Amazon MLS-C01 Exam process

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Free sharing of Amazon MLS-C01 exam practice questions (1-5)

QUESTION 1
A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the
company\\’s Amazon S3-based data lake.
The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:
1.
Real-time analytics
2.
Interactive analytics of historical data
3.
Clickstream analytics
4.
Product recommendations
Which services should the Specialist use?
A. AWS Glue as the data dialog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data
insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate
personalized product recommendations
B. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near real-time data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized
product recommendations
C. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data
insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate
personalized product recommendations
D. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical
data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product
recommendations
Correct Answer: A

 

QUESTION 2
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters
MUST be specified? (Select THREE.)
A. The training channel identifying the location of training data on an Amazon S3 bucket.
B. The validation channel identifying the location of validation data on an Amazon S3 bucket.
C. The 1 AM role that Amazon SageMaker can assume to perform tasks on behalf of the users.
D. Hyperparameters in a JSON array as documented for the algorithm used.
E. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.
F. The output path specifying where on an Amazon S3 bucket the trained model will persist.
Correct Answer: AEF

 

QUESTION 3
A company that promotes healthy sleep patterns by providing cloud-connected devices currently hosts a sleep tracking
application on AWS. The application collects device usage information from device users. The company\\’s Data
The science team is building a machine learning model to predict if and when a user will stop utilizing the company\\’s
devices. Predictions from this model are used by a downstream application that determines the best approach for
contacting users.
The Data Science team is building multiple versions of the machine learning model to evaluate each version against the
company\\’s business goals. To measure long-term effectiveness, the team wants to run multiple versions of the model
in parallel for long periods of time, with the ability to control the portion of inferences served by the models.
Which solution satisfies these requirements with MINIMAL effort?
A. Build and host multiple models in Amazon SageMaker. Create multiple Amazon SageMaker endpoints, one for each
model. Programmatically control invoking different models for inference at the application layer.
B. Build and host multiple models in Amazon SageMaker. Create an Amazon SageMaker endpoint configuration with
multiple product variants. Programmatically control the portion of the inferences served by the multiple models by
updating the endpoint configuration.
C. Build and host multiple models in Amazon SageMaker Neo to take into account different types of medical devices.
Programmatically control which model is invoked for inference based on the medical device type.
D. Build and host multiple models in Amazon SageMaker. Create a single endpoint that accesses multiple models. Use
Amazon SageMaker batch transforms to control invoking the different models through the single endpoint.
Correct Answer: D

 

QUESTION 4
A Machine Learning Specialist is working for a credit card processing company and receives an unbalanced dataset
containing credit card transactions. It contains 99,000 valid transactions and 1,000 fraudulent transactions The
A specialist is asked to score a model that was run against the dataset The Specialist has been advised that identifying
valid transactions are equally as important as identifying fraudulent transactions What metric is BEST suited to score the
model?
A. Precision
B. Recall
C. Area Under the ROC Curve (AUC)
D. Root Mean Square Error (RMSE)
Correct Answer: A

 

QUESTION 5
A machine learning specialist works for a fruit processing company and needs to build a system that categorizes apples
into three types. The specialist has collected a dataset that contains 150 images for each type of apple and applied
transfer learning on a neural network that was trained on ImageNet with this dataset.
The company requires at least 85% accuracy to make use of the model.
After an exhaustive grid search, the optimal hyperparameters produced the following:
1.
68% accuracy on the training set
2.
67% accuracy on the validation set
What can the machine learning specialist do to improve the system\\’s accuracy?
A. Upload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPO feature to
optimize the model\\’s hyperparameters.
B. add more data to the training set and retrain the model using transfer learning to reduce the bias.
C. Use a neural network model with more layers that are pre-trained on ImageNet and apply transfer learning to increase
the variance.
D. Train a new model using the current neural network architecture.
Correct Answer: B

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