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MLS-C01 Exam Prep: Study Materials & Mock Tests, AWS Certified Machine Learning | SPOTO

The AWS Certified Machine Learning—Specialty (MLS-C01) exam is tailored for professionals in Development or Data Science roles, assessing their expertise in constructing, refining, and launching ML models on AWS Cloud platforms. This certification is a testament to an individual's proficiency in leveraging AWS services like Amazon SageMaker, AWS Lambda, and Amazon S3 for ML tasks. SPOTO offers comprehensive MLS-C01 exam preparation resources, including study materials and mock tests. These study materials encompass exam questions, practice tests, sample questions, and exam dumps, facilitating a deep understanding of the exam structure, core concepts, and problem-solving strategies. Accessing these resources enhances exam readiness and confidence. Moreover, SPOTO's mock tests provide realistic exam simulations, enabling candidates to simulate exam conditions and evaluate their preparedness effectively. Leveraging SPOTO's study materials and mock tests streamlines MLS-C01 exam preparation, ensuring a structured and successful journey towards certification.

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Question #1
Q42: A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes. Which prior probability distribution should the ML Specialist use for this variable?
A. Poisson distribution
B. Uniform distribution
C. Normal distribution
D. Binomial distribution
View answer
Correct Answer: A

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Question #2
Q33: A gaming company has launched an online game where people can start playing for free, but they need to pay if they choose to use certain features. The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year. The company has gathered a labeled dataset from 1 million users. The training dataset consists of 1,000 positive samples (from users who ended up paying within 1 year) and 999,000 negative samples (from users who did not use any paid fea
A. Add more deep trees to the random forest to enable the model to learn more features
B. Include a copy of the samples in the test dataset in the training dataset
C. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data
D. Change the cost function so that false negatives have a higher impact on the cost value than false positives
E. Change the cost function so that false positives have a higher impact on the cost value than false negatives
View answer
Correct Answer: D
Question #3
Q41: A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression. During exploratory data analysis, the Specialist observes that many features are highly correlated with each other. This may make the model unstable. What should be done to reduce the impact of having such a large number of features?
A. Perform one-hot encoding on highly correlated features
B. Use matrix multiplication on highly correlated features
C. Create a new feature space using principal component analysis (PCA)
D. Apply the Pearson correlation coefficient
View answer
Correct Answer: C
Question #4
Q34: A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age. Initial models have performed poorly. While reviewing the underlying d
A. Drop all records from the dataset where age has been set to 0
B. Replace the age field value for records with a value of 0 with the mean or median value from the dataset
C. Drop the age feature from the dataset and train the model using the rest of the features
D. Use k-means clustering to handle missing features
View answer
Correct Answer: A
Question #5
Q23: 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
A. Dropout
B. Smooth L1 loss
C. Softmax
D. Rectified linear units (ReLU)
View answer
Correct Answer: C
Question #6
Q38: A company is observing low accuracy while training on the default built-in image classification algorithm in Amazon SageMaker. The Data Science team wants to use an Inception neural network architecture instead of a ResNet architecture. Which of the following will accomplish this? (Choose two.)
A. Customize the built-in image classification algorithm to use Inception and use this for model training
B. Create a support case with the SageMaker team to change the default image classification algorithm to Inception
C. Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training
D. Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network, and use this for model training
E. Download and apt-get install the inception network code into an Amazon EC2 instance and use this instance as a Jupyter notebook in Amazon SageMaker
View answer
Correct Answer: B
Question #7
Q35: A Data Science team is designing a dataset repository where it will store a large amount of training data commonly used in its machine learning models. As Data Scientists may create an arbitrary number of new datasets every day, the solution has to scale automatically and be cost-effective. Also, it must be possible to explore the data using SQL. Which storage scheme is MOST adapted to this scenario?
A. Store datasets as files in Amazon S3
B. Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance
C. Store datasets as tables in a multi-node Amazon Redshift cluster
D. Store datasets as global tables in Amazon DynamoDB
View answer
Correct Answer: D
Question #8
Given the dataset, the Specialist wants to convert the Day_Of_Week column to binary values. What technique should be used to convert this column to binary values?
A. Binarization
B. One-hot encoding
C. Tokenization
D. Normalization transformation
View answer
Correct Answer: CD
Question #9
Q24: A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target. What option can the Specialist use to determine whether it is overestimating or underestimating the target value?
A. Root Mean Square Error (RMSE)
B. Residual plots
C. Area under the curve
D. Confusion matrix
View answer
Correct Answer: B
Question #10
Q43: A Data Science team within a large company uses Amazon SageMaker notebooks to access data stored in Amazon S3 buckets. The IT Security team is concerned that internet-enabled notebook instances create a security vulnerability where malicious code running on the instances could compromise data privacy. The company mandates that all instances stay within a secured VPC with no internet access, and data communication traffic must stay within the AWS network. How should the Data Science team configure the notebook instance placement to meet these requirements?
A. Associate the Amazon SageMaker notebook with a private subnet in a VPC
B. Associate the Amazon SageMaker notebook with a private subnet in a VPC
C. Associate the Amazon SageMaker notebook with a private subnet in a VPC
D. Associate the Amazon SageMaker notebook with a private subnet in a VPC
View answer
Correct Answer: C
Question #11
Q39: 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%, 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
View answer
Correct Answer: B
Question #12
Q36: A Machine Learning Specialist deployed a model that provides product recommendations on a company’s website. Initially, the model was performing very well and resulted in customers buying more products on average. However, within the past few months, the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less. The Specialist is unsure of what happened, as the model has not changed from its initial de Which method should the Specialist try to improve model performance?
A. The model needs to be completely re-engineered because it is unable to handle product inventory changes
B. The model’s hyperparameters should be periodically updated to prevent drift
C. The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes
D. The model should be periodically retrained using the original training data plus new data as product inventory changes
View answer
Correct Answer: A

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