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Home Amazon AWS Certified Machine Learning - Specialty

Amazon AWS Certified Machine Learning - Specialty Exam Practice Questions (P. 2)

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Question #11
A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The
Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences, and trends to enhance the website for better service and smart recommendations.
Which solution should the Specialist recommend?
  1. A
    Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.
  2. B
    A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database.
  3. C
    Collaborative filtering based on user interactions and correlations to identify patterns in the customer database.
  4. D
    Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database.

Correct Answer:
C
chatbot-img GPT-4o - Answer
Collaborative filtering, as correctly indicated by the consensus among user comments, is indeed the most appropriate approach for this scenario where the goal is to identify customer shopping patterns, trends, and preferences based on user interactions and correlations. Understanding that LDA and Random Cut Forest target entirely different problem types like topic modeling and anomaly detection respectively, and neural networks, although versatile, would need more contextual tuning which makes it not the default choice here. Therefore, collaborative filtering stands out as it directly leverages user interaction data to generate personalized recommendations.

Question #12
A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist.
Which machine learning model type should the Specialist use to accomplish this task?
  1. A
    Linear regression
  2. B
    Classification
  3. C
    Clustering
  4. D
    Reinforcement learning

Correct Answer:
B
The goal of classification is to determine to which class or category a data point (customer in our case) belongs to. For classification problems, data scientists would use historical data with predefined target variables AKA labels (churner/non-churner) ג€" answers that need to be predicted ג€" to train an algorithm. With classification, businesses can answer the following questions:
✑ Will this customer churn or not?
✑ Will a customer renew their subscription?
✑ Will a user downgrade a pricing plan?
✑ Are there any signs of unusual customer behavior?
Reference:
https://www.kdnuggets.com/2019/05/churn-prediction-machine-learning.html

Question #13
The displayed graph is from a forecasting model for testing a time series.

Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?
  1. A
    The model predicts both the trend and the seasonality well
  2. B
    The model predicts the trend well, but not the seasonality.
  3. C
    The model predicts the seasonality well, but not the trend.
  4. D
    The model does not predict the trend or the seasonality well.

Correct Answer:
A

Question #14
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 accuracy?
  1. A
    Long short-term memory (LSTM) model with scaled exponential linear unit (SELU)
  2. B
    Logistic regression
  3. C
    Support vector machine (SVM) with non-linear kernel
  4. D
    Single perceptron with tanh activation function

Correct Answer:
C

Question #15
A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains
Personally Identifiable Information (PII).
The dataset:
✑ Must be accessible from a VPC only.
✑ Must not traverse the public internet.
How can these requirements be satisfied?
  1. A
    Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.
  2. B
    Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.
  3. C
    Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.
  4. D
    Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance

Correct Answer:
A
chatbot-img GPT-4o - Answer
For the dataset containing PII stored in Amazon S3, to ensure that it is accessible only from a VPC and doesn’t utilize the public internet, setting up a VPC endpoint is essential. Adding a specific bucket access policy, which effectively limits the accessibility to this VPC-only via the set VPC endpoint, addresses both security concerns. By doing this, no EC2 specifics are necessary nor is the use of ALLOW policies from various security peripherals like NACLs or different groups, ensuring a streamlined and secure access route that adheres strictly to the given constraints.

Question #16
During mini-batch training of a neural network for a classification problem, a Data Scientist notices that training accuracy oscillates.
What is the MOST likely cause of this issue?
  1. A
    The class distribution in the dataset is imbalanced.
  2. B
    Dataset shuffling is disabled.
  3. C
    The batch size is too big.
  4. D
    The learning rate is very high.

Correct Answer:
D
Reference:
https://towardsdatascience.com/deep-learning-personal-notes-part-1-lesson-2-8946fe970b95

Question #17
An employee found a video clip with audio on a company's social media feed. The language used in the video is Spanish. English is the employee's first language, and they do not understand Spanish. The employee wants to do a sentiment analysis.
What combination of services is the MOST efficient to accomplish the task?
  1. A
    Amazon Transcribe, Amazon Translate, and Amazon Comprehend
  2. B
    Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker seq2seq
  3. C
    Amazon Transcribe, Amazon Translate, and Amazon SageMaker Neural Topic Model (NTM)
  4. D
    Amazon Transcribe, Amazon Translate and Amazon SageMaker BlazingText

Correct Answer:
A
chatbot-img GPT-4o - Answer
Amazon Transcribe, Amazon Translate, and Amazon Comprehend together provide the most seamless path to achieving sentiment analysis from a Spanish video for an English speaker. First, Transcribe converts spoken language in the video into written Spanish. Then, Translate converts the Spanish text into English. Finally, Comprehend analyzes the English text to determine sentiment. This sequence provides a completely managed service experience without the need for additional coding or infrastructure setup, ensuring an efficient solution.

Question #18
A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training. The
Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs.
What does the Specialist need to do?
  1. A
    Bundle the NVIDIA drivers with the Docker image.
  2. B
    Build the Docker container to be NVIDIA-Docker compatible.
  3. C
    Organize the Docker container's file structure to execute on GPU instances.
  4. D
    Set the GPU flag in the Amazon SageMaker CreateTrainingJob request body.

Correct Answer:
A
chatbot-img GPT-4o - Answer
Upon reviewing the latest AWS documentation as referenced by users in their comments, it becomes clear that the correct preparation for leveraging NVIDIA GPUs in a Docker container on Amazon EC2 P3 instances involves ensuring that the container is NVIDIA-Docker compatible. Importantly, specialists should not bundle NVIDIA drivers within the Docker image; instead, they should include only the CUDA toolkit. This adjustment aligns with best practices for configuring environments in AWS SageMaker for efficient GPU utilization during model training.

Question #19
A Machine Learning Specialist is building a logistic regression model that will predict whether or not a person will order a pizza. The Specialist is trying to build the optimal model with an ideal classification threshold.
What model evaluation technique should the Specialist use to understand how different classification thresholds will impact the model's performance?
  1. A
    Receiver operating characteristic (ROC) curve
  2. B
    Misclassification rate
  3. C
    Root Mean Square Error (RMSE)
  4. D
    L1 norm

Correct Answer:
A
Reference:
https://docs.aws.amazon.com/machine-learning/latest/dg/binary-model-insights.html

Question #20
An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget.
What should the Specialist do to meet these requirements?
  1. A
    Create one-hot word encoding vectors.
  2. B
    Produce a set of synonyms for every word using Amazon Mechanical Turk.
  3. C
    Create word embedding vectors that store edit distance with every other word.
  4. D
    Download word embeddings pre-trained on a large corpus.

Correct Answer:
A
Reference:
https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-object2vec-adds-new-features-that-support-automatic-negative-sampling-and- speed-up-training/

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