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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q197-Q202):

NEW QUESTION # 197
An ecommerce company has developed a XGBoost model in Amazon SageMaker to predict whether a customer will return a purchased item. The dataset is imbalanced. Only 5% of customers return items A data scientist must find the hyperparameters to capture as many instances of returned items as possible. The company has a small budget for compute.
How should the data scientist meet these requirements MOST cost-effectively?

A. Tune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using automatic model tuning (AMT). Optimize on {"HyperParameterTuningJobObjective": {"MetricName":
"validation:f1", "Type": "Minimize"}).
B. Tune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using automatic model tuning (AMT). Optimize on {"HyperParameterTuningJobObjective": {"MetricName":
"validation:f1", "Type": "Maximize"}}.
C. Tune all possible hyperparameters by using automatic model tuning (AMT). Optimize on
{"HyperParameterTuningJobObjective": {"MetricName": "validation:f1", "Type": "Maximize"}}.
D. Tune all possible hyperparameters by using automatic model tuning (AMT). Optimize on
{"HyperParameterTuningJobObjective": {"MetricName": "validation:accuracy", "Type": "Maximize"}}

Answer: B

Explanation:
The best solution to meet the requirements is to tune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using automatic model tuning (AMT). Optimize on
{"HyperParameterTuningJobObjective": {"MetricName": "validation:f1", "Type": "Maximize"}}.
The csv_weight hyperparameter is used to specify the instance weights for the training data in CSV format.
This can help handle imbalanced data by assigning higher weights to the minority class examples and lower weights to the majority class examples. The scale_pos_weight hyperparameter is used to control the balance of positive and negative weights. It is the ratio of the number of negative class examples to the number of positive class examples. Setting a higher value for this hyperparameter can increase the importance of the positive class and improve the recall. Both of these hyperparameters can help the XGBoost model capture as many instances of returned items as possible.
Automatic model tuning (AMT) is a feature of Amazon SageMaker that automates the process of finding the best hyperparameter values for a machine learning model. AMT uses Bayesian optimization to search the hyperparameter space and evaluate the model performance based on a predefined objective metric. The objective metric is the metric that AMT tries to optimize by adjusting the hyperparameter values. For imbalanced classification problems, accuracy is not a good objective metric, as it can be misleading and biased towards the majority class. A better objective metric is the F1 score, which is the harmonic mean of precision and recall. The F1 score can reflect the balance between precision and recall and is more suitable for imbalanced data. The F1 score ranges from 0 to 1, where 1 is the best possible value. Therefore, the type of the objective should be "Maximize" to achieve the highest F1 score.
By tuning the csv_weight and scale_pos_weight hyperparameters and optimizing on the F1 score, the data scientist can meet the requirements most cost-effectively. This solution requires tuning only two hyperparameters, which can reduce the computation time and cost compared to tuning all possible hyperparameters. This solution also uses the appropriate objective metric for imbalanced classification, which can improve the model performance and capture more instances of returned items.
References:
*XGBoost Hyperparameters
*Automatic Model Tuning
*How to Configure XGBoost for Imbalanced Classification
*Imbalanced Data

NEW QUESTION # 198
A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and 1,000 fraudulent observations.
The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is
99.1%, but the Data Scientist has been asked to reduce the number of false negatives.

Which combination of steps should the Data Scientist take to reduce the number of false positive predictions by the model? (Select TWO.)

A. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.
B. Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.
C. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.
D. Change the XGBoost evaljnetric parameter to optimize based on AUC instead of error.
E. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.

Answer: A,D

Explanation:
* The XGBoost algorithm is a popular machine learning technique for classification problems. It is based on the idea of boosting, which is to combine many weak learners (decision trees) into a strong learner (ensemble model).
* The XGBoost algorithm can handle imbalanced data by using the scale_pos_weight parameter, which controls the balance of positive and negative weights in the objective function. A typical value to consider is the ratio of negative cases to positive cases in the data. By increasing this parameter, the algorithm will pay more attention to the minority class (positive) and reduce the number of false negatives.
* The XGBoost algorithm can also use different evaluation metrics to optimize the model performance.
The default metric is error, which is the misclassification rate. However, this metric can be misleading for imbalanced data, as it does not account for the different costs of false positives and false negatives.
A better metric to use is AUC, which is the area under the receiver operating characteristic (ROC) curve. The ROC curve plots the true positive rate against the false positive rate for different threshold values. The AUC measures how well the model can distinguish between the two classes, regardless of the threshold. By changing the eval_metric parameter to AUC, the algorithm will try to maximize the AUC score and reduce the number of false negatives.
* Therefore, the combination of steps that should be taken to reduce the number of false negatives are to increase the scale_pos_weight parameter and change the eval_metric parameter to AUC.
References:
* XGBoost Parameters
* XGBoost for Imbalanced Classification

NEW QUESTION # 199
A machine learning (ML) specialist uploads 5 TB of data to an Amazon SageMaker Studio environment. The ML specialist performs initial data cleansing. Before the ML specialist begins to train a model, the ML specialist needs to create and view an analysis report that details potential bias in the uploaded data.
Which combination of actions will meet these requirements with the LEAST operational overhead? (Choose two.)

A. Configure SageMaker Data Wrangler to generate a bias report.
B. Use SageMaker Clarify to automatically detect data bias
C. Use SageMaker Experiments to perform a data check
D. Turn on the bias detection option in SageMaker Ground Truth to automatically analyze data features.
E. Use SageMaker Model Monitor to generate a bias drift report.

Answer: A,B

Explanation:
Explanation
The combination of actions that will meet the requirements with the least operational overhead is to use SageMaker Clarify to automatically detect data bias and to configure SageMaker Data Wrangler to generate a bias report. SageMaker Clarify is a feature of Amazon SageMaker that provides machine learning (ML) developers with tools to gain greater insights into their ML training data and models. SageMaker Clarify can detect potential bias during data preparation, after model training, and in your deployed model. For instance, you can check for bias related to age in your dataset or in your trained model and receive a detailed report that quantifies different types of potential bias1. SageMaker Data Wrangler is another feature of Amazon SageMaker that enables you to prepare data for machine learning (ML) quickly and easily. You can use SageMaker Data Wrangler to identify potential bias during data preparation without having to write your own code. You specify input features, such as gender or age, and SageMaker Data Wrangler runs an analysis job to detect potential bias in those features. SageMaker Data Wrangler then provides a visual report with a description of the metrics and measurements of potential bias so that you can identify steps to remediate the bias2. The other actions either require more customization (such as using SageMaker Model Monitor or SageMaker Experiments) or do not meet the requirement of detecting data bias (such as using SageMaker Ground Truth). References:
1: Bias Detection and Model Explainability - Amazon Web Services
2: Amazon SageMaker Data Wrangler - Amazon Web Services

NEW QUESTION # 200
A manufacturing company wants to use machine learning (ML) to automate quality control in its facilities.
The facilities are in remote locations and have limited internet connectivity. The company has 20 ## of training data that consists of labeled images of defective product parts. The training data is in the corporate on- premises data center.
The company will use this data to train a model for real-time defect detection in new parts as the parts move on a conveyor belt in the facilities. The company needs a solution that minimizes costs for compute infrastructure and that maximizes the scalability of resources for training. The solution also must facilitate the company's use of an ML model in the low-connectivity environments.
Which solution will meet these requirements?

A. Train the model on premises. Upload the model to an Amazon S3 bucket. Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. Deploy the model on the edge device.
B. Move the training data to an Amazon S3 bucket. Train and evaluate the model by using Amazon SageMaker. Optimize the model by using SageMaker Neo. Deploy the model on a SageMaker hosting services endpoint.
C. Train and evaluate the model on premises. Upload the model to an Amazon S3 bucket. Deploy the model on an Amazon SageMaker hosting services endpoint.
D. Move the training data to an Amazon S3 bucket. Train and evaluate the model by using Amazon SageMaker. Optimize the model by using SageMaker Neo. Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. Deploy the model on the edge device.

Answer: D

Explanation:
The solution C meets the requirements because it minimizes costs for compute infrastructure, maximizes the scalability of resources for training, and facilitates the use of an ML model in low-connectivity environments.
The solution C involves the following steps:
* Move the training data to an Amazon S3 bucket. This will enable the company to store the large amount of data in a durable, scalable, and cost-effective way. It will also allow the company to access the data from the cloud for training and evaluation purposes1.
* Train and evaluate the model by using Amazon SageMaker. This will enable the company to use a fully managed service that provides various features and tools for building, training, tuning, and deploying ML models. Amazon SageMaker can handle large-scale data processing and distributed training, and it can leverage the power of AWS compute resources such as Amazon EC2, Amazon EKS, and AWS Fargate2.
* Optimize the model by using SageMaker Neo. This will enable the company to reduce the size of the model and improve its performance and efficiency. SageMaker Neo can compile the model into an executable that can run on various hardware platforms, such as CPUs, GPUs, and edge devices3.
* Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. This will enable the company to deploy the model on a local device that can run inference in real time, even in low- connectivity environments. AWS IoT Greengrass can extend AWS cloud capabilities to the edge, and it can securely communicate with the cloud for updates and synchronization4.
* Deploy the model on the edge device. This will enable the company to automate quality control in its facilities by using the model to detect defects in new parts as they move on a conveyor belt. The model can run inference locally on the edge device without requiring internet connectivity, and it can send the results to the cloud when the connection is available4.
The other options are not suitable because:
* Option A: Deploying the model on a SageMaker hosting services endpoint will not facilitate the use of the model in low-connectivity environments, as it will require internet access to perform inference.
Moreover, it may incur higher costs for hosting and data transfer than deploying the model on an edge device.
* Option B: Training and evaluating the model on premises will not minimize costs for compute infrastructure, as it will require the company to maintain and upgrade its own hardware and software.
Moreover, it will not maximize the scalability of resources for training, as it will limit the company's ability to leverage the cloud's elasticity and flexibility.
* Option D: Training the model on premises will not minimize costs for compute infrastructure, nor maximize the scalability of resources for training, for the same reasons as option B.
References:
* 1: Amazon S3
* 2: Amazon SageMaker
* 3: SageMaker Neo
* 4: AWS IoT Greengrass

NEW QUESTION # 201
A data scientist is developing a pipeline to ingest streaming web traffic dat a. The data scientist needs to implement a process to identify unusual web traffic patterns as part of the pipeline. The patterns will be used downstream for alerting and incident response. The data scientist has access to unlabeled historic data to use, if needed.
The solution needs to do the following:
Calculate an anomaly score for each web traffic entry.
Adapt unusual event identification to changing web patterns over time.
Which approach should the data scientist implement to meet these requirements?

A. Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker built-in XGBoost model. Use an Amazon Kinesis Data Stream to process the incoming web traffic data. Attach a preprocessing AWS Lambda function to perform data enrichment by calling the XGBoost model to calculate the anomaly score for each record.
B. Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker Random Cut Forest (RCF) built-in model. Use an Amazon Kinesis Data Stream to process the incoming web traffic data. Attach a preprocessing AWS Lambda function to perform data enrichment by calling the RCF model to calculate the anomaly score for each record.
C. Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an input source for Amazon Kinesis Data Analytics. Write a SQL query to run in real time against the streaming data with the Amazon Random Cut Forest (RCF) SQL extension to calculate anomaly scores for each record using a sliding window.
D. Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an input source for Amazon Kinesis Data Analytics. Write a SQL query to run in real time against the streaming data with the k-Nearest Neighbors (kNN) SQL extension to calculate anomaly scores for each record using a tumbling window.

Answer: C

NEW QUESTION # 202
......

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q197-Q202):

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