A Machine Learning Specialist wants to bring a custom algorithm to Amazon SageMaker. The Specialist implements the algorithm in a Docker container supported by Amazon SageMaker.How should the Specialist package the Docker container so that Amazon SageMaker can launch the training correctly?
Answer(s): B
A Data Scientist needs to analyze employment data. The dataset contains approximately 10 million observations on people across 10 different features. During the preliminary analysis, the Data Scientist notices that income and age distributions are not normal. While income levels shows a right skew as expected, with fewer individuals having a higher income, the age distribution also show a right skew, with fewer older individuals participating in the workforce.Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.)
Answer(s): B,D
https://towardsdatascience.com/understanding-feature-engineering-part-1-continuous-numeric-data-da4e47099a7b
A web-based company wants to improve its conversion rate on its landing page. Using a large historical dataset of customer visits, the company has repeatedly trained a multi-class deep learning network algorithm on Amazon SageMaker. However, there is an overfitting problem: training data shows 90% accuracy in predictions, while test data shows 70% accuracy only.The company needs to boost the generalization of its model before deploying it into production to maximize conversions of visits to purchases.Which action is recommended to provide the HIGHEST accuracy model for the company’s test and validation data?
Answer(s): D
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?
Answer(s): A
Pay attention to what the question is asking:"Whether there are natural groupings for these columns across all customers and visualize the results as quickly as possible"The key point is to visualize the "groupings"(exactly what t-SNE scatter plot does, it visualize high-dimensional data points on 2D space).The question does not ask to visualize how many groups you would classify (K-Means Elbow Plot does not visualize the groupings, it is used to determine the optimal # of groups=K).
https://towardsdatascience.com/an-introduction-to-t-sne-with-python-example-5a3a293108d1
A Machine Learning Specialist is planning to create a long-running Amazon EMR cluster. The EMR cluster will have 1 master node, 10 core nodes, and 20 task nodes. To save on costs, the Specialist will use Spot Instances in the EMR cluster.Which nodes should the Specialist launch on Spot Instances?
Answer(s): C
"Long-Running Clusters and Data WarehousesIf you are running a persistent Amazon EMR cluster that has a predictable variation in computational capacity, such as a data warehouse, you can handle peak demand at lower cost with Spot Instances. You can launch your master and core instance groups as On-Demand Instances to handle the normal capacity and launch task instance groups as Spot Instances to handle your peak load requirements."
https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-plan-instances-guidelines.html
Post your Comments and Discuss Amazon MLS-C01 exam prep with other Community members:
Our website is free, but we have to fight against AI bots and content theft. We're sorry for the inconvenience caused by these security measures. You can access the rest of the MLS-C01 content, but please register or login to continue.