Exam MLS-C01 Overviews & New MLS-C01 Exam Online

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Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) Exam is a certification exam designed for individuals who want to demonstrate their expertise in machine learning on the AWS platform. MLS-C01 exam is intended for professionals who have experience using AWS services for designing, building, and deploying machine learning solutions. AWS Certified Machine Learning - Specialty certification exam validates the candidate's ability to design, implement, and deploy machine learning models using AWS services.

Amazon AWS Certified Machine Learning - Specialty certification exam is designed for individuals who want to demonstrate their expertise in building, deploying, and managing machine learning solutions on the Amazon Web Services (AWS) platform. AWS Certified Machine Learning - Specialty certification validates the skills required to design, implement, deploy, and maintain machine learning solutions that are scalable, secure, and highly available. MLS-C01 Exam Tests candidates’ knowledge of various AWS services, including Amazon SageMaker, Amazon S3, Amazon EC2, and Amazon RDS.

Amazon MLS-C01 certification exam is designed for professionals who work with machine learning and want to demonstrate their expertise in this field. AWS Certified Machine Learning - Specialty certification is ideal for data scientists, machine learning engineers, software developers, and other IT professionals who want to validate their skills and knowledge in machine learning on the AWS cloud platform.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q289-Q294):

NEW QUESTION # 289
A Mobile Network Operator is building an analytics platform to analyze and optimize a company's operations using Amazon Athena and Amazon S3 The source systems send data in CSV format in real lime The Data Engineering team wants to transform the data to the Apache Parquet format before storing it on Amazon S3 Which solution takes the LEAST effort to implement?

A. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.
B. Ingest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use Apache Spark to convert data into Parquet.
C. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convert data into Parquet.
D. Ingest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 to serialize data as Parquet

Answer: A

Explanation:
https://medium.com/searce/convert-csv-json-files-to-apache-parquet-using-aws-glue-a760d177b45f
https://github.com/ecloudvalley/Building-a-Data-Lake-with-AWS-Glue-and-Amazon-S3

NEW QUESTION # 290
A Machine Learning Specialist is working with multiple data sources containing billions of records that need to be joined. What feature engineering and model development approach should the Specialist take with a dataset this large?

A. Use Amazon EMR for feature engineering and Amazon SageMaker SDK for model development
B. Use an Amazon SageMaker notebook for feature engineering and Amazon ML for model development
C. Use an Amazon SageMaker notebook for both feature engineering and model development
D. Use Amazon ML for both feature engineering and model development.

Answer: A

Explanation:
Amazon EMR is a service that can process large amounts of data efficiently and cost-effectively. It can run distributed frameworks such as Apache Spark, which can perform feature engineering on big data. Amazon SageMaker SDK is a Python library that can interact with Amazon SageMaker service to train and deploy machine learning models. It can also use Amazon EMR as a data source for training data. References:
Amazon EMR
Amazon SageMaker SDK

NEW QUESTION # 291
A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as either a potential risk or no risk. The model is not performing well, even though the Data Scientist has experimented with many different network structures and tuned the corresponding hyperparameters.
Which approach will provide the MAXIMUM performance boost?

A. Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector.
B. Reduce the learning rate and run the training process until the training loss stops decreasing.
C. Use gated recurrent units (GRUs) instead of LSTM and run the training process until the validation loss stops decreasing.
D. Initialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a large collection of news articles related to the energy sector.

Answer: A

Explanation:
Initializing the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector will provide the maximum performance boost for the LSTM model. Word2vec is a technique that learns distributed representations of words based on their co-occurrence in a large corpus of text. These representations capture semantic and syntactic similarities between words, which can help the LSTM model better understand the meaning and context of the sentences in the text documents. Using word2vec embeddings that are pretrained on a relevant domain (energy sector) can further improve the performance by reducing the vocabulary mismatch and increasing the coverage of the words in the text documents. References:
AWS Machine Learning Specialty Exam Guide
AWS Machine Learning Training - Text Classification with TF-IDF, LSTM, BERT: a comparison of performance AWS Machine Learning Training - Machine Learning - Exam Preparation Path

NEW QUESTION # 292
A company is running an Amazon SageMaker training job that will access data stored in its Amazon S3 bucket A compliance policy requires that the data never be transmitted across the internet How should the company set up the job?

A. Launch the notebook instances in a public subnet and access the data through a NAT gateway
B. Launch the notebook instances in a public subnet and access the data through the public S3 endpoint
C. Launch the notebook instances in a private subnet and access the data through a NAT gateway
D. Launch the notebook instances in a private subnet and access the data through an S3 VPC endpoint.

Answer: D

Explanation:
A private subnet is a subnet that does not have a route to the internet gateway, which means that the resources in the private subnet cannot access the internet or be accessed from the internet. An S3 VPC endpoint is a gateway endpoint that allows the resources in the VPC to access the S3 service without going through the internet. By launching the notebook instances in a private subnet and accessing the data through an S3 VPC endpoint, the company can set up the job in a secure and compliant way, as the data never leaves the AWS network and is not exposed to the internet. This can also improve the performance and reliability of the data transfer, as the traffic does not depend on the internet bandwidth or availability.
References:
Amazon VPC Endpoints - Amazon Virtual Private Cloud
Endpoints for Amazon S3 - Amazon Virtual Private Cloud
Connect to SageMaker Within your VPC - Amazon SageMaker
Working with VPCs and Subnets - Amazon Virtual Private Cloud

NEW QUESTION # 293
A Machine Learning Specialist is developing recommendation engine for a photography blog Given a picture, the recommendation engine should show a picture that captures similar objects The Specialist would like to create a numerical representation feature to perform nearest-neighbor searches What actions would allow the Specialist to get relevant numerical representations?

A. Average colors by channel to obtain three-dimensional representations of images.
B. Reduce image resolution and use reduced resolution pixel values as features
C. Use Amazon Mechanical Turk to label image content and create a one-hot representation indicating the presence of specific labels
D. Run images through a neural network pie-trained on ImageNet, and collect the feature vectors from the penultimate layer

Answer: D

Explanation:
Explanation
A neural network pre-trained on ImageNet is a deep learning model that has been trained on a large dataset of images containing 1000 classes of objects. The model can learn to extract high-level features from the images that capture the semantic and visual information of the objects. The penultimate layer of the model is the layer before the final output layer, and it contains a feature vector that represents the input image in a lower-dimensional space. By running images through a pre-trained neural network and collecting the feature vectors from the penultimate layer, the Specialist can obtain relevant numerical representations that can be used for nearest-neighbor searches. The feature vectors can capture the similarity between images based on the presence and appearance of similar objects, and they can be compared using distance metrics such as Euclidean distance or cosine similarity. This approach can enable the recommendation engine to show a picture that captures similar objects to a given picture.
References:
ImageNet - Wikipedia
How to use a pre-trained neural network to extract features from images | by Rishabh Anand | Analytics Vidhya | Medium Image Similarity using Deep Ranking | by Aditya Oke | Towards Data Science

NEW QUESTION # 294
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Exam MLS-C01 Overviews & New MLS-C01 Exam Online

100% Pass Quiz Amazon MLS-C01 - AWS Certified Machine Learning - Specialty Marvelous Exam Overviews

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q289-Q294):

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