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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q75-Q80):

NEW QUESTION # 75
An ecommerce company has used Amazon SageMaker to deploy a factorization machines (FM) model to suggest products for customers. The company's data science team has developed two new models by using the TensorFlow and PyTorch deep learning frameworks. The company needs to use A/B testing to evaluate the new models against the deployed model.
...required A/B testing setup is as follows:
* Send 70% of traffic to the FM model, 15% of traffic to the TensorFlow model, and 15% of traffic to the Py Torch model.
* For customers who are from Europe, send all traffic to the TensorFlow model
..sh architecture can the company use to implement the required A/B testing setup?

A. Create two production variants for the TensorFlow and PyTorch models. Create an auto scaling policy and configure the desired A/B weights to direct traffic to each production variant Update the existing SageMaker endpoint with the auto scaling policy. To send traffic to the TensorFlow model for customers who are from Europe, set the TargetVariant header in the request to point to the variant name of the TensorFlow model.
B. Create two production variants for the TensorFlow and PyTorch models. Specify the weight for each production variant in the SageMaker endpoint configuration. Update the existing SageMaker endpoint with the new configuration. To send traffic to the TensorFlow model for customers who are from Europe, set the TargetVariant header in the request to point to the variant name of the TensorFlow model.
C. Create two new SageMaker endpoints for the TensorFlow and PyTorch models in addition to the existing SageMaker endpoint. Create an Application Load Balancer Create a target group for each endpoint. Configure listener rules and add weight to the target groups. To send traffic to the TensorFlow model for customers who are from Europe, create an additional listener rule to forward traffic to the TensorFlow target group.
D. Create two new SageMaker endpoints for the TensorFlow and PyTorch models in addition to the existing SageMaker endpoint. Create a Network Load Balancer. Create a target group for each endpoint. Configure listener rules and add weight to the target groups. To send traffic to the TensorFlow model for customers who are from Europe, create an additional listener rule to forward traffic to the TensorFlow target group.

Answer: B

Explanation:
The correct answer is D because it allows the company to use the existing SageMaker endpoint and leverage the built-in functionality of production variants for A/B testing. Production variants can be used to test ML models that have been trained using different training datasets, algorithms, and ML frameworks; test how they perform on different instance types; or a combination of all of the above1. By specifying the weight for each production variant in the endpoint configuration, the company can control how much traffic to send to each variant. By setting the TargetVariant header in the request, the company can invoke a specific variant directly for each request2. This enables the company to implement the required A/B testing setup without creating additional endpoints or load balancers.
1: Production variants - Amazon SageMaker
2: A/B Testing ML models in production using Amazon SageMaker | AWS Machine Learning Blog

NEW QUESTION # 76
A Machine Learning Specialist prepared the following graph displaying the results of k-means for k = [1:10]

Considering the graph, what is a reasonable selection for the optimal choice of k?

A. 0
B. 1
C. 2
D. 3

Answer: B

Explanation:
Explanation
The elbow method is a technique that we use to determine the number of centroids (k) to use in a k-means clustering algorithm. In this method, we plot the within-cluster sum of squares (WCSS) against the number of clusters (k) and look for the point where the curve bends sharply. This point is called the elbow point and it indicates that adding more clusters does not improve the model significantly. The graph in the question shows that the elbow point is at k = 4, which means that 4 is a reasonable choice for the optimal number of clusters.
References:
Elbow Method for optimal value of k in KMeans: A tutorial on how to use the elbow method with Amazon SageMaker.
K-Means Clustering: A video that explains the concept and benefits of k-means clustering.

NEW QUESTION # 77
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 epoch number should be increased because the optimization process was terminated before it reached the global minimum.
C. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
D. The dropout rate at the flatten layer should be increased because the model is not generalized enough.

Answer: A

NEW QUESTION # 78
A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data.
Which of the following methods should the Specialist consider using to correct this? (Choose three.)

A. Increase feature combinations.
B. Increase regularization.
C. Decrease feature combinations.
D. Increase dropout.
E. Decrease dropout.
F. Decrease regularization.

Answer: B,C,D

Explanation:
Feature selection: consider using fewer feature combinations, decrease n-grams size, and decrease the number of numeric attribute bins.
Increase the amount of regularization used
https://docs.aws.amazon.com/machine-learning/latest/dg/model-fit-underfitting-vs-overfitting.html

NEW QUESTION # 79
A medical device company is building a machine learning (ML) model to predict the likelihood of device recall based on customer data that the company collects from a plain text survey. One of the survey questions asks which medications the customer is taking. The data for this field contains the names of medications that customers enter manually. Customers misspell some of the medication names. The column that contains the medication name data gives a categorical feature with high cardinality but redundancy.
What is the MOST effective way to encode this categorical feature into a numeric feature?

A. Fix the spelling in the column by using char-RNN. Use Amazon SageMaker Data Wrangler one-hot encoding to transform a categorical feature to a numerical feature.
B. Use Amazon SageMaker Data Wrangler similarity encoding on the column to create embeddings Of vectors Of real numbers.
C. Use Amazon SageMaker Data Wrangler ordinal encoding on the column to encode categories into an integer between O and the total number Of categories in the column.
D. Spell check the column. Use Amazon SageMaker one-hot encoding on the column to transform a categorical feature to a numerical feature.

Answer: A

Explanation:
The most effective way to encode this categorical feature into a numeric feature is to use Amazon SageMaker Data Wrangler similarity encoding on the column to create embeddings of vectors of real numbers. Similarity encoding is a technique that transforms categorical features into numerical features by computing the similarity between the categories. Similarity encoding can handle high cardinality and redundancy in categorical features, as it can group similar categories together based on their string similarity. For example, if the column contains the values "aspirin", "asprin", and "ibuprofen", similarity encoding will assign a high similarity score to "aspirin" and "asprin", and a low similarity score to "ibuprofen". Similarity encoding can also create embeddings of vectors of real numbers, which can be used as input for machine learning models. Amazon SageMaker Data Wrangler is a feature of Amazon SageMaker that enables you to prepare data for machine learning quickly and easily. You can use SageMaker Data Wrangler to apply similarity encoding to a column of categorical data, and generate embeddings of vectors of real numbers that capture the similarity between the categories1. The other options are either less effective or more complex to implement. Spell checking the column and using one-hot encoding would require additional steps and resources, and may not capture all the misspellings or redundancies. One-hot encoding would also create a large number of features, which could increase the dimensionality and sparsity of the dat a. Ordinal encoding would assign an arbitrary order to the categories, which could introduce bias or noise in the data. References:
1: Amazon SageMaker Data Wrangler - Amazon Web Services

NEW QUESTION # 80
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AWS-Certified-Machine-Learning-Specialty New Braindumps Book - Test AWS-Certified-Machine-Learning-Specialty Simulator

Web-Based Amazon AWS-Certified-Machine-Learning-Specialty Practice Exam - Get Familiar With Real Exam Environment

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q75-Q80):

Newest Amazon - AWS-Certified-Machine-Learning-Specialty - AWS Certified Machine Learning - Specialty New Braindumps Book

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