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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q71-Q76):

NEW QUESTION # 71
A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers Currently, the company has the following data in Amazon Aurora
* Profiles for all past and existing customers
* Profiles for all past and existing insured pets
* Policy-level information
* Premiums received
* Claims paid
What steps should be taken to implement a machine learning model to identify potential new customers on social media?

A. Use a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
B. Use clustering on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.
C. Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
D. Use regression on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.

Answer: B

Explanation:
Explanation
Clustering is a machine learning technique that can group data points into clusters based on their similarity or proximity. Clustering can help discover the underlying structure and patterns in the data, as well as identify outliers or anomalies. Clustering can also be used for customer segmentation, which is the process of dividing customers into groups based on their characteristics, behaviors, preferences, or needs. Customer segmentation can help understand the key features and needs of different customer segments, as well as design and implement targeted marketing campaigns for each segment. In this case, the Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers.
To do this, the Manager can use clustering on customer profile data to understand the key characteristics of consumer segments, such as their demographics, pet types, policy preferences, premiums paid, claims made, etc. The Manager can then find similar profiles on social media, such as Facebook, Twitter, Instagram, etc., by using the cluster features as filters or keywords. The Manager can then target these potential new customers with personalized and relevant ads or offers that match their segment's needs and interests. This way, the Manager can implement a machine learning model to identify potential new customers on social media.

NEW QUESTION # 72
A financial company is trying to detect credit card fraud. The company observed that, on average, 2% of credit card transactions were fraudulent. A data scientist trained a classifier on a year's worth of credit card transactions dat a. The model needs to identify the fraudulent transactions (positives) from the regular ones (negatives). The company's goal is to accurately capture as many positives as possible.
Which metrics should the data scientist use to optimize the model? (Choose two.)

A. True positive rate
B. Accuracy
C. Specificity
D. False positive rate
E. Area under the precision-recall curve

Answer: A,E

Explanation:
The data scientist should use the area under the precision-recall curve and the true positive rate to optimize the model. These metrics are suitable for imbalanced classification problems, such as credit card fraud detection, where the positive class (fraudulent transactions) is much rarer than the negative class (non-fraudulent transactions).
The area under the precision-recall curve (AUPRC) is a measure of how well the model can identify the positive class among all the predicted positives. Precision is the fraction of predicted positives that are actually positive, and recall is the fraction of actual positives that are correctly predicted. A higher AUPRC means that the model can achieve a higher precision with a higher recall, which is desirable for fraud detection.
The true positive rate (TPR) is another name for recall. It is also known as sensitivity or hit rate. It measures the proportion of actual positives that are correctly identified by the model. A higher TPR means that the model can capture more positives, which is the company's goal.
References:
Metrics for Imbalanced Classification in Python - Machine Learning Mastery Precision-Recall - scikit-learn

NEW QUESTION # 73
A machine learning (ML) specialist is administering a production Amazon SageMaker endpoint with model monitoring configured. Amazon SageMaker Model Monitor detects violations on the SageMaker endpoint, so the ML specialist retrains the model with the latest dataset. This dataset is statistically representative of the current production traffic. The ML specialist notices that even after deploying the new SageMaker model and running the first monitoring job, the SageMaker endpoint still has violations.
What should the ML specialist do to resolve the violations?

A. Manually trigger the monitoring job to re-evaluate the SageMaker endpoint traffic sample.
B. Delete the endpoint and recreate it with the original configuration.
C. Run the Model Monitor baseline job again on the new training set. Configure Model Monitor to use the new baseline.
D. Retrain the model again by using a combination of the original training set and the new training set.

Answer: C

Explanation:
The ML specialist should run the Model Monitor baseline job again on the new training set and configure Model Monitor to use the new baseline. This is because the baseline job computes the statistics and constraints for the data quality and model quality metrics, which are used to detect violations. If the training set changes, the baseline job should be updated accordingly to reflect the new distribution of the data and the model performance. Otherwise, the old baseline may not be representative of the current production traffic and may cause false alarms or miss violations. References:
Monitor data and model quality - Amazon SageMaker
Detecting and analyzing incorrect model predictions with Amazon SageMaker Model Monitor and Debugger | AWS Machine Learning Blog

NEW QUESTION # 74
A company is building a new supervised classification model in an AWS environment. The company's data science team notices that the dataset has a large quantity of variables Ail the variables are numeric. The model accuracy for training and validation is low. The model's processing time is affected by high latency The data science team needs to increase the accuracy of the model and decrease the processing.
How it should the data science team do to meet these requirements?

A. Use a multiple correspondence analysis (MCA) model
B. Use a principal component analysis (PCA) model.
C. Create new features and interaction variables.
D. Apply normalization on the feature set.

Answer: B

Explanation:
Explanation
The best way to meet the requirements is to use a principal component analysis (PCA) model, which is a technique that reduces the dimensionality of the dataset by transforming the original variables into a smaller set of new variables, called principal components, that capture most of the variance and information in the data1. This technique has the following advantages:
It can increase the accuracy of the model by removing noise, redundancy, and multicollinearity from the data, and by enhancing the interpretability and generalization of the model23.
It can decrease the processing time of the model by reducing the number of features and the computational complexity of the model, and by improving the convergence and stability of the model45.
It is suitable for numeric variables, as it relies on the covariance or correlation matrix of the data, and it can handle a large quantity of variables, as it can extract the most relevant ones16.
The other options are not effective or appropriate, because they have the following drawbacks:
A: Creating new features and interaction variables can increase the accuracy of the model by capturing more complex and nonlinear relationships in the data, but it can also increase the processing time of the model by adding more features and increasing the computational complexity of the model7. Moreover, it can introduce more noise, redundancy, and multicollinearity in the data, which can degrade the performance and interpretability of the model8.
C: Applying normalization on the feature set can increase the accuracy of the model by scaling the features to a common range and avoiding the dominance of some features over others, but it can also decrease the processing time of the model by reducing the numerical instability and improving the convergence of the model . However, normalization alone is not enough to address the high dimensionality and high latency issues of the dataset, as it does not reduce the number of features or the variance in the data.
D: Using a multiple correspondence analysis (MCA) model is not suitable for numeric variables, as it is a technique that reduces the dimensionality of the dataset by transforming the original categorical variables into a smaller set of new variables, called factors, that capture most of the inertia and information in the data. MCA is similar to PCA, but it is designed for nominal or ordinal variables, not for continuous or interval variables.
References:
1: Principal Component Analysis - Amazon SageMaker
2: How to Use PCA for Data Visualization and Improved Performance in Machine Learning | by Pratik Shukla | Towards Data Science
3: Principal Component Analysis (PCA) for Feature Selection and some of its Pitfalls | by Nagesh Singh Chauhan | Towards Data Science
4: How to Reduce Dimensionality with PCA and Train a Support Vector Machine in Python | by James Briggs | Towards Data Science
5: Dimensionality Reduction and Its Applications | by Aniruddha Bhandari | Towards Data Science
6: Principal Component Analysis (PCA) in Python | by Susan Li | Towards Data Science
7: Feature Engineering for Machine Learning | by Dipanjan (DJ) Sarkar | Towards Data Science
8: Feature Engineering - How to Engineer Features and How to Get Good at It | by Parul Pandey | Towards Data Science
9: [Feature Scaling for Machine Learning: Understanding the Difference Between Normalization vs.
Standardization | by Benjamin Obi Tayo Ph.D. | Towards Data Science]
1: [Why, How and When to Scale your Features | by George Seif | Towards Data Science]
2: [Normalization vs Dimensionality Reduction | by Saurabh Annadate | Towards Data Science]
3: [Multiple Correspondence Analysis - Amazon SageMaker]
4: [Multiple Correspondence Analysis (MCA) | by Raul Eulogio | Towards Data Science]

NEW QUESTION # 75
A company has an ecommerce website with a product recommendation engine built in TensorFlow. The recommendation engine endpoint is hosted by Amazon SageMaker. Three compute-optimized instances support the expected peak load of the website.
Response times on the product recommendation page are increasing at the beginning of each month. Some users are encountering errors. The website receives the majority of its traffic between 8 AM and 6 PM on weekdays in a single time zone.
Which of the following options are the MOST effective in solving the issue while keeping costs to a minimum? (Choose two.)

A. Deploy a second instance pool to support a blue/green deployment of models.
B. Configure the endpoint to use Amazon Elastic Inference (EI) accelerators.
C. Configure the endpoint to automatically scale with the Invocations Per Instance metric.
D. Reconfigure the endpoint to use burstable instances.
E. Create a new endpoint configuration with two production variants.

Answer: B,C

Explanation:
The solution A and C are the most effective in solving the issue while keeping costs to a minimum. The solution A and C involve the following steps:
* Configure the endpoint to use Amazon Elastic Inference (EI) accelerators. This will enable the company to reduce the cost and latency of running TensorFlow inference on SageMaker. Amazon EI provides GPU-powered acceleration for deep learning models without requiring the use of GPU instances. Amazon EI can attach to any SageMaker instance type and provide the right amount of acceleration based on the workload1.
* Configure the endpoint to automatically scale with the Invocations Per Instance metric. This will enable the company to adjust the number of instances based on the demand and traffic patterns of the website.
The Invocations Per Instance metric measures the average number of requests that each instance processes over a period of time. By using this metric, the company can scale out the endpoint when the load increases and scale in when the load decreases. This can improve the response time and availability of the product recommendation engine2.
The other options are not suitable because:
* Option B: Creating a new endpoint configuration with two production variants will not solve the issue of increasing response time and errors. Production variants are used to split the traffic between different models or versions of the same model. They can be useful for testing, updating, or A/B testing models. However, they do not provide any scaling or acceleration benefits for the inference workload3.
* Option D: Deploying a second instance pool to support a blue/green deployment of models will not solve the issue of increasing response time and errors. Blue/green deployment is a technique for updating models without downtime or disruption. It involves creating a new endpoint configuration with a different instance pool and model version, and then shifting the traffic from the old endpoint to the new endpoint gradually. However, this technique does not provide any scaling or acceleration benefits for the inference workload4.
* Option E: Reconfiguring the endpoint to use burstable instances will not solve the issue of increasing response time and errors. Burstable instances are instances that provide a baseline level of CPU performance with the ability to burst above the baseline when needed. They can be useful for workloads that have moderate CPU utilization and occasional spikes. However, they are not suitable for workloads that have high and consistent CPU utilization, such as the product recommendation engine. Moreover, burstable instances may incur additional charges when they exceed their CPU credits5.
References:
* 1: Amazon Elastic Inference
* 2: How to Scale Amazon SageMaker Endpoints
* 3: Deploying Models to Amazon SageMaker Hosting Services
* 4: Updating Models in Amazon SageMaker Hosting Services
* 5: Burstable Performance Instances

NEW QUESTION # 76
......

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q71-Q76):

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