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Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) Exam is an industry-recognized certification that validates your expertise in designing, deploying, and maintaining machine learning solutions on the Amazon Web Services platform. It is designed for professionals who want to demonstrate their ability to use AWS services to build and deploy machine learning models.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q161-Q166):

NEW QUESTION # 161
A financial services company wants to automate its loan approval process by building a machine learning (ML) model. Each loan data point contains credit history from a third-party data source and demographic information about the customer. Each loan approval prediction must come with a report that contains an explanation for why the customer was approved for a loan or was denied for a loan. The company will use Amazon SageMaker to build the model.
Which solution will meet these requirements with the LEAST development effort?

• A. Use AWS Lambda to provide feature importance and partial dependence plots. Use the plots to generate and attach the explanation report.
• B. Use custom Amazon Cloud Watch metrics to generate the explanation report. Attach the report to the predicted results.
• C. Use SageMaker Clarify to generate the explanation report. Attach the report to the predicted results.
• D. Use SageMaker Model Debugger to automatically debug the predictions, generate the explanation, and attach the explanation report.

Answer: C

Explanation:
The best solution for this scenario is to use SageMaker Clarify to generate the explanation report and attach it to the predicted results. SageMaker Clarify provides tools to help explain how machine learning (ML) models make predictions using a model-agnostic feature attribution approach based on SHAP values. It can also detect and measure potential bias in the data and the model. SageMaker Clarify can generate explanation reports during data preparation, model training, and model deployment. The reports include metrics, graphs, and examples that help understand the model behavior and predictions. The reports can be attached to the predicted results using the SageMaker SDK or the SageMaker API.
The other solutions are less optimal because they require more development effort and additional services. Using SageMaker Model Debugger would require modifying the training script to save the model output tensors and writing custom rules to debug and explain the predictions. Using AWS Lambda would require writing code to invoke the ML model, compute the feature importance and partial dependence plots, and generate and attach the explanation report. Using custom Amazon CloudWatch metrics would require writing code to publish the metrics, create dashboards, and generate and attach the explanation report.
References:
Bias Detection and Model Explainability - Amazon SageMaker Clarify - AWS Amazon SageMaker Clarify Model Explainability Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability GitHub - aws/amazon-sagemaker-clarify: Fairness Aware Machine Learning

NEW QUESTION # 162
A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among
200 categories, and the date of the final outcome. Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance.
What type of machine learning model should be used?

• A. Classification month-to-month using supervised learning of the 200 categories based on claim contents.
• B. Classification with supervised learning of the categories for which partial information on claim contents is provided, and forecasting using claim IDs and timestamps for all other categories.
• C. Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.
• D. Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.

Answer: C

Explanation:
Forecasting is a type of machine learning model that predicts future values of a target variable based on historical data and other features. Forecasting is suitable for problems that involve time-series data, such as the number of claims in each category from month to month. Forecasting can handle multiple categories of the target variable, as well as missing or partial information on some features. Therefore, option C is the best choice for the given problem.
Option A is incorrect because classification is a type of machine learning model that assigns a label to an input based on predefined categories. Classification is not suitable for predicting continuous or numerical values, such as the number of claims in each category from month to month. Moreover, classification requires sufficient and complete information on the features that are relevant to the target variable, which is not the case for the given problem. Option B is incorrect because reinforcement learning is a type of machine learning model that learns from its own actions and rewards in an interactive environment. Reinforcement learning is not suitable for problems that involve historical data and do not require an agent to take actions. Option D is incorrect because it combines two different types of machine learning models, which is unnecessary and inefficient. Moreover, classification is not suitable for predicting the number of claims in some categories, as explained in option A.
Forecasting | AWS Solutions for Machine Learning (AI/ML) | AWS Solutions Library Time Series Forecasting Service - Amazon Forecast - Amazon Web Services Amazon Forecast: Guide to Predicting Future Outcomes - Onica Amazon Launches What-If Analyses for Machine Learning Forecasting ...

NEW QUESTION # 163
A logistics company needs a forecast model to predict next month's inventory requirements for a single item in 10 warehouses. A machine learning specialist uses Amazon Forecast to develop a forecast model from 3 years of monthly data. There is no missing data. The specialist selects the DeepAR+ algorithm to train a predictor. The predictor means absolute percentage error (MAPE) is much larger than the MAPE produced by the current human forecasters.
Which changes to the CreatePredictor API call could improve the MAPE? (Choose two.)

• A. Set PerformAutoML to true.
• B. Set FeaturizationMethodName to filling.
• C. Set ForecastHorizon to 4.
• D. Set ForecastFrequency to W for weekly.
• E. Set PerformHPO to true.

Answer: A,E

Explanation:
The MAPE of the predictor could be improved by making the following changes to the CreatePredictor API call:
* Set PerformAutoML to true. This will allow Amazon Forecast to automatically evaluate different algorithms and choose the one that minimizes the objective function, which is the mean of the weighted losses over the forecast types. By default, these are the p10, p50, and p90 quantile losses1. This option can help find a better algorithm than DeepAR+ for the given data.
* Set PerformHPO to true. This will enable hyperparameter optimization (HPO), which is the process of finding the optimal values for the algorithm-specific parameters that affect the quality of the forecasts. HPO can improve the accuracy of the predictor by tuning the hyperparameters based on the training data2.
The other options are not likely to improve the MAPE of the predictor. Setting ForecastHorizon to 4 will reduce the number of time steps that the model predicts, which may not match the business requirement of predicting next month's inventory. Setting ForecastFrequency to W for weekly will change the granularity of the forecasts, which may not be appropriate for the monthly data. Setting FeaturizationMethodName to filling will not have any effect, since there is no missing data in the dataset.
CreatePredictor - Amazon Forecast
HPOConfig - Amazon Forecast

NEW QUESTION # 164
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 principal component analysis (PCA) model.
• B. Use a multiple correspondence analysis (MCA) model
• C. Create new features and interaction variables.
• D. Apply normalization on the feature set.

Answer: A

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
* : [Feature Scaling for Machine Learning: Understanding the Difference Between Normalization vs.
Standardization | by Benjamin Obi Tayo Ph.D. | Towards Data Science]
* : [Why, How and When to Scale your Features | by George Seif | Towards Data Science]
* : [Normalization vs Dimensionality Reduction | by Saurabh Annadate | Towards Data Science]
* : [Multiple Correspondence Analysis - Amazon SageMaker]
* : [Multiple Correspondence Analysis (MCA) | by Raul Eulogio | Towards Data Science]

NEW QUESTION # 165
A data scientist wants to use Amazon Forecast to build a forecasting model for inventory demand for a retail company. The company has provided a dataset of historic inventory demand for its products as a .csv file stored in an Amazon S3 bucket. The table below shows a sample of the dataset.

How should the data scientist transform the data?

• A. Use AWS Batch jobs to separate the dataset into a target time series dataset, a related time series dataset, and an item metadata dataset. Upload them directly to Forecast from a local machine.
• B. Use a Jupyter notebook in Amazon SageMaker to separate the dataset into a related time series dataset and an item metadata dataset. Upload both datasets as tables in Amazon Aurora.
• C. Use a Jupyter notebook in Amazon SageMaker to transform the data into the optimized protobuf recordIO format. Upload the dataset in this format to Amazon S3.
• D. Use ETL jobs in AWS Glue to separate the dataset into a target time series dataset and an item metadata dataset. Upload both datasets as .csv files to Amazon S3.

Answer: B

NEW QUESTION # 166
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