Amazon MLS-C01 Exam Dumps

[Exploratory Data Analysis]

A company uses sensors on devices such as motor engines and factory machines to measure parameters, temperature and pressure. The company wants to use the sensor data to predict equipment malfunctions and reduce services outages.

The Machine learning (ML) specialist needs to gather the sensors data to train a model to predict device malfunctions The ML spoctafst must ensure that the data does not contain outliers before training the ..el.

What can the ML specialist meet these requirements with the LEAST operational overhead?

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[Machine Learning Implementation and Operations]

A company operates large cranes at a busy port. The company plans to use machine learning (ML) for predictive maintenance of the cranes to avoid unexpected breakdowns and to improve productivity.

The company already uses sensor data from each crane to monitor the health of the cranes in real time. The sensor data includes rotation speed, tension, energy consumption, vibration, pressure, and ...perature for each crane. The company contracts AWS ML experts to implement an ML solution.

Which potential findings would indicate that an ML-based solution is suitable for this scenario? (Select TWO.)

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[Data Engineering]

A company wants to create a data repository in the AWS Cloud for machine learning (ML) projects. The company wants to use AWS to perform complete ML lifecycles and wants to use Amazon S3 for the data storage. All of the company's data currently resides on premises and is 40 in size.

The company wants a solution that can transfer and automatically update data between the on-premises object storage and Amazon S3. The solution must support encryption, scheduling, monitoring, and data integrity validation.

Which solution meets these requirements?

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[Modeling]

A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.

How should a data scientist adjust the solution?

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[Exploratory Data Analysis]

A Machine Learning Specialist is given a structured dataset on the shopping habits of a company's customer

base. The dataset contains thousands of columns of data and hundreds of numerical columns for each

customer. The Specialist wants to identify whether there are natural groupings for these columns across all

customers and visualize the results as quickly as possible.

What approach should the Specialist take to accomplish these tasks?

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