Latest MLS-C01 Dumps Files, MLS-C01 Exam Dumps Demo

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The goal of a Amazon MLS-C01 mock exam is to test exam readiness. Actual4dump's online AWS Certified Machine Learning - Specialty MLS-C01 practice test can be accessed online through all major browsers such as Chrome, Firefox, Safari, and Edge. You can also download and install the offline version of AWS Certified Machine Learning - Specialty MLS-C01 Practice Exam software on Windows-based PCs only. You can prepare for the AWS Certified Machine Learning - Specialty exam without an internet connection using the offline version of the mock exam.

The AWS Certified Machine Learning - Specialty certification exam is a challenging and rigorous exam that requires a significant amount of preparation and study. To prepare for the exam, candidates can take advantage of a wide range of resources, including online courses, study guides, and practice exams. They can also attend training courses and workshops offered by AWS or other training providers.

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Achieving the AWS Certified Machine Learning – Specialty certification can open up a range of job opportunities in the field of machine learning, including roles such as Machine Learning Engineer, Data Scientist, and AI Developer. It also provides a solid foundation for pursuing advanced certifications in the field of machine learning on AWS.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q160-Q165):

NEW QUESTION # 160
A Data Engineer needs to build a model using a dataset containing customer credit card information.
How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

A. Use AWS KMS to encrypt the data on Amazon S3
B. Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC. Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.
C. Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.
D. Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.

Answer: B

NEW QUESTION # 161
A power company wants to forecast future energy consumption for its customers in residential properties and commercial business properties. Historical power consumption data for the last 10 years is available. A team of data scientists who performed the initial data analysis and feature selection will include the historical power consumption data and data such as weather, number of individuals on the property, and public holidays.
The data scientists are using Amazon Forecast to generate the forecasts.
Which algorithm in Forecast should the data scientists use to meet these requirements?

A. Exponential Smoothing (ETS)
B. Autoregressive Integrated Moving Average (AIRMA)
C. Prophet
D. Convolutional Neural Network - Quantile Regression (CNN-QR)

Answer: A

NEW QUESTION # 162
A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable What should be done to reduce the impact of having such a large number of features?

A. Create a new feature space using principal component analysis (PCA)
B. Apply the Pearson correlation coefficient
C. Use matrix multiplication on highly correlated features.
D. Perform one-hot encoding on highly correlated features

Answer: D

NEW QUESTION # 163
A retail chain has been ingesting purchasing records from its network of 20,000 stores to Amazon S3 using Amazon Kinesis Data Firehose. To support training an improved machine learning model, training records will require new but simple transformations, and some attributes will be combined. The model needs to be retrained daily.
Given the large number of stores and the legacy data ingestion, which change will require the LEAST amount of development effort?

A. Spin up a fleet of Amazon EC2 instances with the transformation logic, have them transform the data records accumulating on Amazon S3, and output the transformed records to Amazon S3.
B. Require that the stores to switch to capturing their data locally on AWS Storage Gateway for loading into Amazon S3, then use AWS Glue to do the transformation.
C. Deploy an Amazon EMR cluster running Apache Spark with the transformation logic, and have the cluster run each day on the accumulating records in Amazon S3, outputting new/transformed records to Amazon S3.
D. Insert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehose stream that transforms raw record attributes into simple transformed values using SQL.

Answer: D

NEW QUESTION # 164
A company is using Amazon Polly to translate plaintext documents to speech for automated company announcements However company acronyms are being mispronounced in the current documents How should a Machine Learning Specialist address this issue for future documents'?

A. Output speech marks to guide in pronunciation
B. Use Amazon Lex to preprocess the text files for pronunciation
C. Create an appropriate pronunciation lexicon.
D. Convert current documents to SSML with pronunciation tags

Answer: D

NEW QUESTION # 165
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Latest MLS-C01 Dumps Files, MLS-C01 Exam Dumps Demo

Top Latest MLS-C01 Dumps Files | Professional MLS-C01 Exam Dumps Demo: AWS Certified Machine Learning - Specialty 100% Pass

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q160-Q165):

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