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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q72-Q77):

NEW QUESTION # 72
An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen Which combination of algorithms would provide the appropriate insights? (Select TWO )

• A. The factorization machines (FM) algorithm
• B. The k-means algorithm g The Random Cut Forest (RCF) algorithm
• C. The Latent Dirichlet Allocation (LDA) algorithm
• D. The principal component analysis (PCA) algorithm

Answer: D

NEW QUESTION # 73
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 data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population How should the Data Scientist correct this issue?

• A. Use k-means clustering to handle missing features
• B. Drop all records from the dataset where age has been set to 0.
• C. Drop the age feature from the dataset and train the model using the rest of the features.
• D. Replace the age field value for records with a value of 0 with the mean or median value from the dataset

Answer: B

Explanation:
Explanation

NEW QUESTION # 74
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 features) Each data sample consists of 200 features including user age, device, location, and play patterns Using this dataset for training, the Data Science team trained a random forest model that converged with over 99% accuracy on the training set However, the prediction results on a test dataset were not satisfactory.
Which of the following approaches should the Data Science team take to mitigate this issue? (Select TWO.)

• A. Change the cost function so that false negatives have a higher impact on the cost value than false positives
• B. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.
• C. Add more deep trees to the random forest to enable the model to learn more features.
• D. indicate a copy of the samples in the test database in the training dataset
• E. Change the cost function so that false positives have a higher impact on the cost value than false negatives

Answer: A,B

NEW QUESTION # 75
A Machine Learning team has several large CSV datasets in Amazon S3. Historically, models built with the Amazon SageMaker Linear Learner algorithm have taken hours to train on similar- sized datasets. The team's leaders need to accelerate the training process.
What can a Machine Learning Specialist do to address this concern?

• A. Use Amazon Machine Learning to train the models.
• B. Use Amazon SageMaker Pipe mode.
• C. Use Amazon Kinesis to stream the data to Amazon SageMaker.
• D. Use AWS Glue to transform the CSV dataset to the JSON format.

Answer: B

Explanation:
Amazon SageMaker Pipe mode streams the data directly to the container, which improves the performance of training jobs. In Pipe mode, your training job streams data directly from Amazon S3. Streaming can provide faster start times for training jobs and better throughput. With Pipe mode, you also reduce the size of the Amazon EBS volumes for your training instances. B would not apply in this scenario. C is a streaming ingestion solution, but is not applicable in this scenario. D transforms the data structure

NEW QUESTION # 76
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. Rectified linear units (ReLU)
• B. Dropout
• C. Softmax
• D. Smooth L1 loss

Answer: C

NEW QUESTION # 77
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