AWS Certified Machine Learning - Specialty
Last Update Jun 14, 2025
Total Questions : 330
To help you prepare for the MLS-C01 Amazon Web Services exam, we are offering free MLS-C01 Amazon Web Services exam questions. All you need to do is sign up, provide your details, and prepare with the free MLS-C01 practice questions. Once you have done that, you will have access to the entire pool of AWS Certified Machine Learning - Specialty MLS-C01 test questions which will help you better prepare for the exam. Additionally, you can also find a range of AWS Certified Machine Learning - Specialty resources online to help you better understand the topics covered on the exam, such as AWS Certified Machine Learning - Specialty MLS-C01 video tutorials, blogs, study guides, and more. Additionally, you can also practice with realistic Amazon Web Services MLS-C01 exam simulations and get feedback on your progress. Finally, you can also share your progress with friends and family and get encouragement and support from them.
A data scientist is working on a forecast problem by using a dataset that consists of .csv files that are stored in Amazon S3. The files contain a timestamp variable in the following format:
March 1st, 2020, 08:14pm -
There is a hypothesis about seasonal differences in the dependent variable. This number could be higher or lower for weekdays because some days and hours present varying values, so the day of the week, month, or hour could be an important factor. As a result, the data scientist needs to transform the timestamp into weekdays, month, and day as three separate variables to conduct an analysis.
Which solution requires the LEAST operational overhead to create a new dataset with the added features?
A medical device company is building a machine learning (ML) model to predict the likelihood of device recall based on customer data that the company collects from a plain text survey. One of the survey questions asks which medications the customer is taking. The data for this field contains the names of medications that customers enter manually. Customers misspell some of the medication names. The column that contains the medication name data gives a categorical feature with high cardinality but redundancy.
What is the MOST effective way to encode this categorical feature into a numeric feature?
A health care company is planning to use neural networks to classify their X-ray images into normal and abnormal classes. The labeled data is divided into a training set of 1,000 images and a test set of 200 images. The initial training of a neural network model with 50 hidden layers yielded 99% accuracy on the training set, but only 55% accuracy on the test set.
What changes should the Specialist consider to solve this issue? (Choose three.)
Acybersecurity company is collecting on-premises server logs, mobile app logs, and loT sensor data. The company backs up the ingested data in an Amazon S3 bucket and sends the ingested data to Amazon OpenSearch Service for further analysis. Currently, the company has a custom ingestion pipeline that is running on Amazon EC2 instances. The company needs to implement a new serverless ingestion pipeline that can automatically scale to handle sudden changes in the data flow.
Which solution will meet these requirements MOST cost-effectively?