Exam style questions across every MLS-C01 domain
Last Update 5 months ago
Total Questions : 330
Start with our free MLS-C01 practice questions, carefully crafted to mirror the domains, phrasing, and difficulty of the real AWS Certified Specialty exam. Each MLS-C01 exam question comes with a detailed rationale that explains not just which answer is correct but why the others fall short. That's how concepts stick. Use the free set to benchmark yourself: identify your Amazon Web Services weak domains, see where you're losing marks, and build a focused study plan in minutes.
A company is using a machine learning (ML) model to recommend products to customers. An ML specialist wants to analyze the data for the most popular recommendations in four dimensions.
The ML specialist will visualize the first two dimensions as coordinates. The third dimension will be visualized as color. The ML specialist will use size to represent the fourth dimension in the visualization.
Which solution will meet these requirements?
A bank's Machine Learning team is developing an approach for credit card fraud detection The company has a large dataset of historical data labeled as fraudulent The goal is to build a model to take the information from new transactions and predict whether each transaction is fraudulent or not
Which built-in Amazon SageMaker machine learning algorithm should be used for modeling this problem?
An agricultural company is interested in using machine learning to detect specific types of weeds in a 100-acre grassland field. Currently, the company uses tractor-mounted cameras to capture multiple images of the field as 10 × 10 grids. The company also has a large training dataset that consists of annotated images of popular weed classes like broadleaf and non-broadleaf docks.
The company wants to build a weed detection model that will detect specific types of weeds and the location of each type within the field. Once the model is ready, it will be hosted on Amazon SageMaker endpoints. The model will perform real-time inferencing using the images captured by the cameras.
Which approach should a Machine Learning Specialist take to obtain accurate predictions?
A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena The dataset contains more than 800.000 records stored as plaintext CSV files Each record contains 200 columns and is approximately 1 5 MB in size Most queries will span 5 to 10 columns only
How should the Machine Learning Specialist transform the dataset to minimize query runtime?
A retail company wants to build a recommendation system for the company's website. The system needs to provide recommendations for existing users and needs to base those recommendations on each user's past browsing history. The system also must filter out any items that the user previously purchased.
Which solution will meet these requirements with the LEAST development effort?
A company wants to forecast the daily price of newly launched products based on 3 years of data for older product prices, sales, and rebates. The time-series data has irregular timestamps and is missing some values.
Data scientist must build a dataset to replace the missing values. The data scientist needs a solution that resamptes the data daily and exports the data for further modeling.
Which solution will meet these requirements with the LEAST implementation effort?
A Machine Learning Specialist is building a logistic regression model that will predict whether or not a person will order a pizza. The Specialist is trying to build the optimal model with an ideal classification threshold.
What model evaluation technique should the Specialist use to understand how different classification thresholds will impact the model's performance?
A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains Personally Identifiable Information (Pll). The dataset:
* Must be accessible from a VPC only.
* Must not traverse the public internet.
How can these requirements be satisfied?
A machine learning specialist is applying a linear least squares regression model to a dataset with 1,000 records and 50 features. Prior to training, the specialist notices that two features are perfectly linearly dependent.
Why could this be an issue for the linear least squares regression model?
A monitoring service generates 1 TB of scale metrics record data every minute A Research team performs queries on this data using Amazon Athena The queries run slowly due to the large volume of data, and the team requires better performance
How should the records be stored in Amazon S3 to improve query performance?
