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 works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may be fraudulent.
How should the Specialist frame this business problem?
- A . Streaming classification
- B . Binary classification
- C . Multi-category classification
- D . Regression classification
A Machine Learning Specialist is working with multiple data sources containing billions of records that need to be joined.
What feature engineering and model development approach should the Specialist take with a dataset this large?
- A . Use an Amazon SageMaker notebook for both feature engineering and model development
- B . Use an Amazon SageMaker notebook for feature engineering and Amazon ML for model development
- C . Use Amazon EMR for feature engineering and Amazon SageMaker SDK for model development
- D . Use Amazon ML for both feature engineering and model development.
A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.
Based on this information, which model would have the HIGHEST recall with respect to the fraudulent class?
- A . Decision tree
- B . Linear support vector machine (SVM)
- C . Naive Bayesian classifier
- D . Single Perceptron with sigmoidal activation function
A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users’ behavior and product preferences to predict which products users would like based on the users’ similarity to other users.
What should the Specialist do to meet this objective?
- A . Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
- B . Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.
- C . Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
- D . Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR.
An Amazon SageMaker notebook instance is launched into Amazon VPC. The SageMaker notebook references data contained in an Amazon S3 bucket in another account. The bucket is encrypted using SSE-KMS. The instance returns an access denied error when trying to access data in Amazon S3.
Which of the following are required to access the bucket and avoid the access denied error? (Select THREE)
- A . An AWS KMS key policy that allows access to the customer master key (CMK)
- B . A SageMaker notebook security group that allows access to Amazon S3
- C . An 1AM role that allows access to the specific S3 bucket
- D . A permissive S3 bucket policy
- E . An S3 bucket owner that matches the notebook owner
- F . A SegaMaker notebook subnet ACL that allow traffic to Amazon S3.
An insurance company is developing a new device for vehicles that uses a camera to observe drivers’ behavior and alert them when they appear distracted. The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models
During the model evaluation the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images.
Which of the following should be used to resolve this issue? (Select TWO)
- A . Add vanishing gradient to the model
- B . Perform data augmentation on the training data
- C . Make the neural network architecture complex.
- D . Use gradient checking in the model
- E . Add L2 regularization to the model
A company has raw user and transaction data stored in AmazonS3 a MySQL database, and Amazon RedShift A Data Scientist needs to perform an analysis by joining the three datasets from Amazon S3, MySQL, and Amazon RedShift, and then calculating the average-of a few selected columns from the joined data
Which AWS service should the Data Scientist use?
- A . Amazon Athena
- B . Amazon Redshift Spectrum
- C . AWS Glue
- D . Amazon QuickSight
A Machine Learning Specialist built an image classification deep learning model. However the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%r respectively.
How should the Specialist address this issue and what is the reason behind it?
- A . The learning rate should be increased because the optimization process was trapped at a local minimum.
- B . The dropout rate at the flatten layer should be increased because the model is not generalized enough.
- C . The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
- D . The epoch number should be increased because the optimization process was terminated before it reached the global minimum.
A Data Engineer needs to build a model using a dataset containing customer credit card information.
How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?
- A . Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker
instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers. - B . Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically
discard credit card numbers and insert fake credit card numbers. - C . Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC. Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.
- D . Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.
The Chief Editor for a product catalog wants the Research and Development team to build a machine learning system that can be used to detect whether or not individuals in a collection of images are wearing the company’s retail brand. The team has a set of training data
Which machine learning algorithm should the researchers use that BEST meets their requirements?
- A . Latent Dirichlet Allocation (LDA)
- B . Recurrent neural network (RNN)
- C . K-means
- D . Convolutional neural network (CNN)