Amazon MLS-C01 Real Exam Questions
The questions for MLS-C01 were last updated at Sep 05,2024.
- Exam Code: MLS-C01
- Exam Name: AWS Certified Machine Learning - Specialty
- Certification Provider: Amazon
- Latest update: Sep 05,2024
A Machine Learning Specialist is building a convolutional neural network (CNN) that will classify 10 types of animals. The Specialist has built a series of layers in a neural network that will take an input image of an animal, pass it through a series of convolutional and pooling layers, and then finally pass it through a dense and fully connected layer with 10 nodes. The Specialist would like to get an output from the neural network that is a probability distribution of how likely it is that the input image belongs to each of the 10 classes
Which function will produce the desired output?
- A . Dropout
- B . Smooth L1 loss
- C . Softmax
- D . Rectified linear units (ReLU)
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?
- A . Increase the randomization of training data in the mini-batches used in training.
- B . Allocate a higher proportion of the overall data to the training dataset
- C . Apply L1 or L2 regularization and dropouts to the training.
- D . Reduce the number of layers and units (or neurons) from the deep learning network.
A retail company intends to use machine learning to categorize new products A labeled dataset of current products was provided to the Data Science team. The dataset includes 1 200 products. The labeled dataset has 15 features for each product such as title dimensions, weight, and price Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies.
Which model should be used for categorizing new products using the provided dataset for training?
- A . An XGBoost model where the objective parameter is set to multi: softmax
- B . A deep convolutional neural network (CNN) with a softmax activation function for the last layer
- C . A regression forest where the number of trees is set equal to the number of product categories
- D . A DeepAR forecasting model based on a recurrent neural network (RNN)
Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?
- A . Recall
- B . Misclassification rate
- C . Mean absolute percentage error (MAPE)
- D . Area Under the ROC Curve (AUC)
A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet
of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance’s Amazon EBS volume, and needs to take a snapshot of that EBS volume. However the ML Specialist cannot find the Amazon SageMaker notebook instance’s EBS volume or Amazon EC2 instance within the VPC.
Why is the ML Specialist not seeing the instance visible in the VPC?
- A . Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.
- B . Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.
- C . Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.
- D . Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.
A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet.
How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?
- A . Create a NAT gateway within the corporate VPC.
- B . Route Amazon SageMaker traffic through an on-premises network.
- C . Create Amazon SageMaker VPC interface endpoints within the corporate VPC.
- D . Create VPC peering with Amazon VPC hosting Amazon SageMaker.
A Machine Learning Specialist needs to be able to ingest streaming data and store it in Apache Parquet files for exploration and analysis.
Which of the following services would both ingest and store this data in the correct format?
- A . AWSDMS
- B . Amazon Kinesis Data Streams
- C . Amazon Kinesis Data Firehose
- D . Amazon Kinesis Data Analytics
A manufacturing company has a large set of labeled historical sales data. The manufacturer would like to predict how many units of a particular part should be produced each quarter.
Which machine learning approach should be used to solve this problem?
- A . Logistic regression
- B . Random Cut Forest (RCF)
- C . Principal component analysis (PCA)
- D . Linear regression
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 lo 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 . 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
- B . 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
- C . 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.
- D . Insert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehouse stream that transforms raw record attributes into simple transformed values using SQL.
A Machine Learning Specialist discover the following statistics while experimenting on a model.
What can the Specialist from the experiments?
- A . The model In Experiment 1 had a high variance error lhat was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal bias error in Experiment 1
- B . The model in Experiment 1 had a high bias error that was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal variance error in Experiment 1
- C . The model in Experiment 1 had a high bias error and a high variance error that were reduced in Experiment 3 by regularization Experiment 2 shows thai high bias cannot be reduced by increasing layers and neurons in the model
- D . The model in Experiment 1 had a high random noise error that was reduced in Experiment 3 by regularization Experiment 2 shows that random noise cannot be reduced by increasing layers and neurons in the model