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Actual MLS-C01 Test Answers | Training MLS-C01 Material

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To be eligible for the Amazon MLS-C01 Certification Exam, candidates must have a minimum of one year of experience in designing and implementing machine learning solutions using AWS services. They should also have experience in data pre-processing, feature engineering, model selection, and model evaluation. Additionally, candidates should have knowledge of programming languages such as Python, R, and Java.

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Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) certification exam is designed to test the skills and knowledge of individuals who want to work with machine learning on the AWS platform. MLS-C01 exam is intended for individuals who already have experience with AWS services and are looking to specialize in the field of machine learning. AWS Certified Machine Learning - Specialty certification exam covers a wide range of topics, including data engineering, data analysis, machine learning algorithms, and model optimization.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q59-Q64):

NEW QUESTION # 59
A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor, and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset.
Which tool should be used to improve the validation accuracy?

A. Amazon SageMaker BlazingText cbow mode
B. Scikit-leam term frequency-inverse document frequency (TF-IDF) vectorizer
C. Natural Language Toolkit (NLTK) stemming and stop word removal
D. Amazon Comprehend syntax analysis and entity detection

Answer: B

Explanation:
https://monkeylearn.com/sentiment-analysis/

NEW QUESTION # 60
A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset Which tool should be used to improve the validation accuracy?

A. Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers
B. Natural Language Toolkit (NLTK) stemming and stop word removal
C. Amazon Comprehend syntax analysts and entity detection
D. Amazon SageMaker BlazingText allow mode

Answer: A

NEW QUESTION # 61
A Data Science team within a large company uses Amazon SageMaker notebooks to access data stored in Amazon S3 buckets. The IT Security team is concerned that internet-enabled notebook instances create a security vulnerability where malicious code running on the instances could compromise data privacy. The company mandates that all instances stay within a secured VPC with no internet access, and data communication traffic must stay within the AWS network.
How should the Data Science team configure the notebook instance placement to meet these requirements?

A. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Use 1AM policies to grant access to Amazon S3 and Amazon SageMaker.
B. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has a NAT gateway and an associated security group allowing only outbound connections to Amazon S3 and Amazon SageMaker
C. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it.
D. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Place the Amazon SageMaker endpoint and S3 buckets within the same VPC.

Answer: C

Explanation:
To configure the notebook instance placement to meet the requirements, the Data Science team should associate the Amazon SageMaker notebook with a private subnet in a VPC. A VPC is a virtual network that is logically isolated from other networks in AWS. A private subnet is a subnet that has no internet gateway attached to it, and therefore cannot communicate with the internet. By placing the notebook instance in a private subnet, the team can ensure that it stays within a secured VPC with no internet access.
However, to access data stored in Amazon S3 buckets and other AWS services, the team needs to ensure that the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it. A VPC endpoint is a gateway that enables private connections between the VPC and supported AWS services. A VPC endpoint does not require an internet gateway, a NAT device, or a VPN connection, and ensures that the traffic between the VPC and the AWS service does not leave the AWS network. By using VPC endpoints, the team can access Amazon S3 and Amazon SageMaker from the notebook instance without compromising data privacy or security.
References:
* : What Is Amazon VPC? - Amazon Virtual Private Cloud
* : Subnet Routing - Amazon Virtual Private Cloud
* : VPC Endpoints - Amazon Virtual Private Cloud

NEW QUESTION # 62
A Machine Learning Specialist is preparing data for training on Amazon SageMaker The Specialist is transformed into a numpy .array, which appears to be negatively affecting the speed of the training What should the Specialist do to optimize the data for training on SageMaker'?

A. Use AWS Glue to compress the data into the Apache Parquet format
B. Use the SageMaker batch transform feature to transform the training data into a DataFrame
C. Transform the dataset into the Recordio protobuf format
D. Use the SageMaker hyperparameter optimization feature to automatically optimize the data

Answer: C

Explanation:
The Recordio protobuf format is a binary data format that is optimized for training on SageMaker. It allows faster data loading and lower memory usage compared to other formats such as CSV or numpy arrays. The Recordio protobuf format also supports features such as sparse input, variable-length input, and label embedding. To use the Recordio protobuf format, the data needs to be serialized and deserialized using the appropriate libraries. Some of the built-in algorithms in SageMaker support the Recordio protobuf format as a content type for training and inference. References:
Common Data Formats for Training
Using RecordIO Format
Content Types Supported by Built-in Algorithms

NEW QUESTION # 63
A company has raw user and transaction data stored in AmazonS3 a MySQL database, and Amazon RedShift A Data Scientist needs to perform an analysis by joining the three datasets from Amazon S3, MySQL, and Amazon RedShift, and then calculating the average-of a few selected columns from the joined data Which AWS service should the Data Scientist use?

A. Amazon QuickSight
B. Amazon Redshift Spectrum
C. AWS Glue
D. Amazon Athena

Answer: D

Explanation:
Explanation
Amazon Athena is a serverless interactive query service that can analyze data in Amazon S3 using standard SQL. Amazon Athena can also query data from other sources, such as MySQL and Amazon Redshift, by using federated queries. Federated queries allow Amazon Athena to run SQL queries across data sources, such as relational and non-relational databases, data warehouses, and data lakes. By using Amazon Athena, the Data Scientist can perform an analysis by joining the three datasets from Amazon S3, MySQL, and Amazon Redshift, and then calculating the average of a few selected columns from the joined data. Amazon Athena can also integrate with other AWS services, such as AWS Glue and Amazon QuickSight, to provide additional features, such as data cataloging and visualization.
References:
What is Amazon Athena? - Amazon Athena
Federated Query Overview - Amazon Athena
Querying Data from Amazon S3 - Amazon Athena
Querying Data from MySQL - Amazon Athena
[Querying Data from Amazon Redshift - Amazon Athena]

NEW QUESTION # 64
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