Amazon Amazon Certifications MLS-C01 Questions & Answers

• Question 81:

  A data scientist receives a collection of insurance claim records. Each record includes a claim ID. the final outcome of the insurance claim, and the date of the final outcome.

  The final outcome of each claim is a selection from among 200 outcome categories. Some claim records include only partial information. However, incomplete claim records include only 3 or 4 outcome ...gones from among the 200 available outcome categories. The collection includes hundreds of records for each outcome category. The records are from the previous 3 years.

  The data scientist must create a solution to predict the number of claims that will be in each outcome category every month, several months in advance.

  Which solution will meet these requirements?

  A. Perform classification every month by using supervised learning of the 20X3 outcome categories based on claim contents.

  B. Perform reinforcement learning by using claim IDs and dates Instruct the insurance agents who submit the claim records to estimate the expected number of claims in each outcome category every month.

  C. Perform forecasting by using claim IDs and dates to identify the expected number ot claims in each outcome category every month.

  D. Perform classification by using supervised learning of the outcome categories for which partial information on claim contents is provided. Perform forecasting by using claim IDs and dates for all other outcome categories.

  Correct Answer: C

  The best solution for this scenario is to perform forecasting by using claim IDs and dates to identify the expected number of claims in each outcome category every month. This solution has the following advantages:

  It leverages the historical data of claim outcomes and dates to capture the temporal patterns and trends of the claims in each category1. It does not require the claim contents or any other features to make predictions, which simplifies the data

  preparation and reduces the impact of missing or incomplete data2. It can handle the high cardinality of the outcome categories, as forecasting models can output multiple values for each time point3.

  It can provide predictions for several months in advance, which is useful for planning and budgeting purposes4.

  The other solutions have the following drawbacks:

  A: Performing classification every month by using supervised learning of the 200 outcome categories based on claim contents is not suitable, because it assumes that the claim contents are available and complete for all the records, which is

  not the case in this scenario2. Moreover, classification models usually output a single label for each input, which is not adequate for predicting the number of claims in each category3. Additionally, classification models do not account for the

  temporal aspect of the data, which is important for forecasting1.

  B: Performing reinforcement learning by using claim IDs and dates and instructing the insurance agents who submit the claim records to estimate the expected number of claims in each outcome category every month is not feasible, because

  it requires a feedback loop between the model and the agents, which might not be available or reliable in this scenario5. Furthermore, reinforcement learning is more suitable for sequential decision making problems, where the model learns

  from its actions and rewards, rather than forecasting problems, where the model learns from historical data and outputs future values6.

  D: Performing classification by using supervised learning of the outcome categories for which partial information on claim contents is provided and performing forecasting by using claim IDs and dates for all other outcome categories is not

  optimal, because it combines two different methods that might not be consistent or compatible with each other7. Also, this solution suffers from the same limitations as solution A, such as the dependency on claim contents, the inability to

  handle multiple outputs, and the ignorance of temporal patterns123.

  References:

  1: Time Series Forecasting - Amazon SageMaker

  2: Handling Missing Data for Machine Learning | AWS Machine Learning Blog

  3: Forecasting vs Classification: What's the Difference? | DataRobot

  4: Amazon Forecast ?Time Series Forecasting Made Easy | AWS News Blog

  5: Reinforcement Learning - Amazon SageMaker

  6: What is Reinforcement Learning? The Complete Guide | Edureka

  7: Combining Machine Learning Models | by Will Koehrsen | Towards Data Science

• Question 82:

  A chemical company has developed several machine learning (ML) solutions to identify chemical process abnormalities. The time series values of independent variables and the labels are available for the past 2 years and are sufficient to accurately model the problem.

  The regular operation label is marked as 0. The abnormal operation label is marked as 1 . Process abnormalities have a significant negative effect on the companys profits. The company must avoid these abnormalities.

  Which metrics will indicate an ML solution that will provide the GREATEST probability of detecting an abnormality?

  A. Precision = 0.91 Recall = 0.6

  B. Precision = 0.61 Recall = 0.98

  C. Precision = 0.7 Recall = 0.9

  D. Precision = 0.98 Recall = 0.8

  Correct Answer: B

  The metrics that will indicate an ML solution that will provide the greatest probability of detecting an abnormality are precision and recall. Precision is the ratio of true positives (TP) to the total number of predicted positives (TP + FP), where FP is false positives. Recall is the ratio of true positives (TP) to the total number of actual positives (TP + FN), where FN is false negatives. A high precision means that the ML solution has a low rate of false alarms, while a high recall means that the ML solution has a high rate of true detections. For the chemical company, the goal is to avoid process abnormalities, which are marked as 1 in the labels. Therefore, the company needs an ML solution that has a high recall for the positive class, meaning that it can detect most of the abnormalities and minimize the false negatives. Among the four options, option B has the highest recall for the positive class, which is 0.98. This means that the ML solution can detect 98% of the abnormalities and miss only 2%. Option B also has a reasonable precision for the positive class, which is 0.61. This means that the ML solution has a false alarm rate of 39%, which may be acceptable for the company, depending on the cost and benefit analysis. The other options have lower recall for the positive class, which means that they have higher false negative rates, which can be more detrimental for the company than false positive rates. References:

  1: AWS Certified Machine Learning - Specialty uide

  2: AWS Training - Machine Learning on AWS

  3: AWS Whitepaper - An Overview of Machine Learning on AWS

  4: Precision and recall

• Question 83:

  A data science team is working with a tabular dataset that the team stores in Amazon S3. The team wants to experiment with different feature transformations such as categorical feature encoding. Then the team wants to visualize the resulting distribution of the dataset. After the team finds an appropriate set of feature transformations, the team wants to automate the workflow for feature transformations.

  Which solution will meet these requirements with the MOST operational efficiency?

  A. Use Amazon SageMaker Data Wrangler preconfigured transformations to explore feature transformations. Use SageMaker Data Wrangler templates for visualization. Export the feature processing workflow to a SageMaker pipeline for automation.

  B. Use an Amazon SageMaker notebook instance to experiment with different feature transformations. Save the transformations to Amazon S3. Use Amazon QuickSight for visualization. Package the feature processing steps into an AWS Lambda function for automation.

  C. Use AWS Glue Studio with custom code to experiment with different feature transformations. Save the transformations to Amazon S3. Use Amazon QuickSight for visualization. Package the feature processing steps into an AWS Lambda function for automation.

  D. Use Amazon SageMaker Data Wrangler preconfigured transformations to experiment with different feature transformations. Save the transformations to Amazon S3. Use Amazon QuickSight for visualzation. Package each feature transformation step into a separate AWS Lambda function. Use AWS Step Functions for workflow automation.

  Correct Answer: A

  The solution A will meet the requirements with the most operational efficiency because it uses Amazon SageMaker Data Wrangler, which is a service that simplifies the process of data preparation and feature engineering for machine learning. The solution A involves the following steps: Use Amazon SageMaker Data Wrangler preconfigured transformations to explore feature transformations. Amazon SageMaker Data Wrangler provides a visual interface that allows data scientists to apply various transformations to their tabular data, such as encoding categorical features, scaling numerical features, imputing missing values, and more. Amazon SageMaker Data Wrangler also supports custom transformations using Python code or SQL queries1. Use SageMaker Data Wrangler templates for visualization. Amazon SageMaker Data Wrangler also provides a set of templates that can generate visualizations of the data, such as histograms, scatter plots, box plots, and more. These visualizations can help data scientists to understand the distribution and characteristics of the data, and to compare the effects of different feature transformations1. Export the feature processing workflow to a SageMaker pipeline for automation. Amazon SageMaker Data Wrangler can export the feature processing workflow as a SageMaker pipeline, which is a service that orchestrates and automates machine learning workflows. A SageMaker pipeline can run the feature processing steps as a preprocessing step, and then feed the output to a training step or an inference step. This can reduce the operational overhead of managing the feature processing workflow and ensure its consistency and reproducibility2. The other options are not suitable because: Option B: Using an Amazon SageMaker notebook instance to experiment with different feature transformations, saving the transformations to Amazon S3, using Amazon QuickSight for visualization, and packaging the feature processing steps into an AWS Lambda function for automation will incur more operational overhead than using Amazon SageMaker Data Wrangler. The data scientist will have to write the code for the feature transformations, the data storage, the data visualization, and the Lambda function. Moreover, AWS Lambda has limitations on the execution time, memory size, and package size, which may not be sufficient for complex feature processing tasks3. Option C: Using AWS Glue Studio with custom code to experiment with different feature transformations, saving the transformations to Amazon S3, using Amazon QuickSight for visualization, and packaging the feature processing steps into an AWS Lambda function for automation will incur more operational overhead than using Amazon SageMaker Data Wrangler. AWS Glue Studio is a visual interface that allows data engineers to create and run extract, transform, and load (ETL) jobs on AWS Glue. However, AWS Glue Studio does not provide preconfigured transformations or templates for feature engineering or data visualization. The data scientist will have to write custom code for these tasks, as well as for the Lambda function. Moreover, AWS Glue Studio is not integrated with SageMaker pipelines, and it may not be optimized for machine learning workflows4. Option D: Using Amazon SageMaker Data Wrangler preconfigured transformations to experiment with different feature transformations, saving the transformations to Amazon S3, using Amazon QuickSight for visualization, packaging each feature transformation step into a separate AWS Lambda function, and using AWS Step Functions for workflow automation will incur more operational overhead than using Amazon SageMaker Data Wrangler. The data scientist will have to create and manage multiple AWS Lambda functions and AWS Step Functions, which can increase the complexity and cost of the solution. Moreover, AWS Lambda and AWS Step Functions may not be compatible with SageMaker pipelines, and they may not be optimized for machine learning workflows5. References:

  1: Amazon SageMaker Data Wrangler

  2: Amazon SageMaker Pipelines

  3: AWS Lambda

  4: AWS Glue Studio

  5: AWS Step Functions

• Question 84:

  A data scientist for a medical diagnostic testing company has developed a machine learning (ML) model to identify patients who have a specific disease. The dataset that the scientist used to train the model is imbalanced. The dataset contains a large number of healthy patients and only a small number of patients who have the disease. The model should consider that patients who are incorrectly identified as positive for the disease will increase costs for the company.

  Which metric will MOST accurately evaluate the performance of this model?

  A. Recall

  B. F1 score

  C. Accuracy

  D. Precision

  Correct Answer: D

• Question 85:

  A wildlife research company has a set of images of lions and cheetahs. The company created a dataset of the images. The company labeled each image with a binary label that indicates whether an image contains a lion or cheetah. The

  company wants to train a model to identify whether new images contain a lion or cheetah.

  Which Amazon SageMaker algorithm will meet this requirement?

  A. XGBoost

  B. Image Classification - TensorFlow

  C. Object Detection - TensorFlow

  D. Semantic segmentation - MXNet

  Correct Answer: B

• Question 86:

  A company is planning a marketing campaign to promote a new product to existing customers. The company has data for past promotions that are similar. The company decides to try an experiment to send a more expensive marketing

  package to a smaller number of customers. The company wants to target the marketing campaign to customers who are most likely to buy the new product. The experiment requires that at least 90% of the customers who are likely to

  purchase the new product receive the marketing materials.

  The company trains a model by using the linear learner algorithm in Amazon SageMaker. The model has a recall score of 80% and a precision of 75%.

  How should the company retrain the model to meet these requirements?

  A. Set the target_recall hyperparameter to 90%. Set the binary_classifier_model_selection_criteria hyperparameter to recall_at_target_precision.

  B. Set the target_precision hyperparameter to 90%. Set the binary_classifier_model_selection_criteria hyperparameter to precision_at_target_recall.

  C. Use 90% of the historical data for training. Set the number of epochs to 20.

  D. Set the normalize_label hyperparameter to true. Set the number of classes to 2.

  Correct Answer: B

• Question 87:

  A data scientist is building a linear regression model. The scientist inspects the dataset and notices that the mode of the distribution is lower than the median, and the median is lower than the mean.

  Which data transformation will give the data scientist the ability to apply a linear regression model?

  A. Exponential transformation

  B. Logarithmic transformation

  C. Polynomial transformation

  D. Sinusoidal transformation

  Correct Answer: B

  A logarithmic transformation is a suitable data transformation for a linear regression model when the data has a skewed distribution, such as when the mode is lower than the median and the median is lower than the mean. A logarithmic transformation can reduce the skewness and make the data more symmetric and normally distributed, which are desirable properties for linear regression. A logarithmic transformation can also reduce the effect of outliers and heteroscedasticity (unequal variance) in the data. An exponential transformation would have the opposite effect of increasing the skewness and making the data more asymmetric. A polynomial transformation may not be able to capture the nonlinearity in the data and may introduce multicollinearity among the transformed variables. A sinusoidal transformation is not appropriate for data that does not have a periodic pattern. References: Data Transformation - Scaler Topics Linear Regression - GeeksforGeeks Linear Regression - Scribbr

• Question 88:

  A network security vendor needs to ingest telemetry data from thousands of endpoints that run all over the world. The data is transmitted every 30 seconds in the form of records that contain 50 fields. Each record is up to 1 KB in size. The security vendor uses Amazon Kinesis Data Streams to ingest the data. The vendor requires hourly summaries of the records that Kinesis Data Streams ingests. The vendor will use Amazon Athena to query the records and to generate the summaries. The Athena queries will target 7 to 12 of the available data fields.

  Which solution will meet these requirements with the LEAST amount of customization to transform and store the ingested data?

  A. Use AWS Lambda to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using Amazon Kinesis Data Firehose.

  B. Use Amazon Kinesis Data Firehose to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using a short-lived Amazon EMR cluster.

  C. Use Amazon Kinesis Data Analytics to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using Amazon Kinesis Data Firehose.

  D. Use Amazon Kinesis Data Firehose to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using AWS Lambda.

  Correct Answer: C

• Question 89:

  A machine learning (ML) specialist is training a linear regression model. The specialist notices that the model is overfitting. The specialist applies an L1 regularization parameter and runs the model again. This change results in all features having zero weights.

  What should the ML specialist do to improve the model results?

  A. Increase the L1 regularization parameter. Do not change any other training parameters.

  B. Decrease the L1 regularization parameter. Do not change any other training parameters.

  C. Introduce a large L2 regularization parameter. Do not change the current L1 regularization value.

  D. Introduce a small L2 regularization parameter. Do not change the current L1 regularization value.

  Correct Answer: B

• Question 90:

  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. Use SageMaker Clarify to automatically detect data bias

  B. Turn on the bias detection option in SageMaker Ground Truth to automatically analyze data features.

  C. Use SageMaker Model Monitor to generate a bias drift report.

  D. Configure SageMaker Data Wrangler to generate a bias report.

  E. Use SageMaker Experiments to perform a data check

  Correct Answer: AD