MLS-C01 practice questions 171–180

1. Question 171 · 368 in bank

   A music streaming company is building a pipeline to extract features. The company wants to store the features for offline model training and online inference. The company wants to track feature history and to give the company’s data science teams access to the features. Which solution will meet these requirements with the MOST operational efficiency?

   • AUse Amazon SageMaker Feature Store to store features for model training and inference. Create an online store for online inference. Create an offline store for model training. Create an IAM role for data scientists to access and search through feature groups.
   • BUse Amazon SageMaker Feature Store to store features for model training and inference. Create an online store for both online inference and model training. Create an IAM role for data scientists to access and search through feature groups.
   • CCreate one Amazon S3 bucket to store online inference features. Create a second S3 bucket to store offline model training features. Turn on versioning for the S3 buckets and use tags to specify which tags are for online inference features and which are for offline model training features. Use Amazon Athena to query the S3 bucket for online inference. Connect the S3 bucket for offline model training to a SageMaker training job. Create an IAM policy that allows data scientists to access both buckets.
   • DCreate two separate Amazon DynamoDB tables to store online inference features and offline model training features. Use time-based versioning on both tables. Query the DynamoDB table for online inference. Move the data from DynamoDB to Amazon S3 when a new SageMaker training job is launched. Create an IAM policy that allows data scientists to access both tables.

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   Answer: A

   Explanation

   Answer is A - "SageMaker Feature Store consists of an online and an offline mode for managing features. The online store is used for low-latency real-time inference use cases. The offline store is primarily used for batch predictions and model training." https://aws.amazon.com/blogs/machine-learning/speed-ml-development-using-sagemaker-feature-store-and-apache-iceberg-offline-store-compaction/

   Source: Adapted from community discussion — asdfzxc

   References

   • AWS documentation

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2. Question 172 · 368 in bank

   A beauty supply store wants to understand some characteristics of visitors to the store. The store has security video recordings from the past several years. The store wants to generate a report of hourly visitors from the recordings. The report should group visitors by hair style and hair color. Which solution will meet these requirements with the LEAST amount of effort?

   • AUse an object detection algorithm to identify a visitor’s hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.
   • BUse an object detection algorithm to identify a visitor’s hair in video frames. Pass the identified hair to an XGBoost algorithm to determine hair style and hair color.
   • CUse a semantic segmentation algorithm to identify a visitor’s hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.
   • DUse a semantic segmentation algorithm to identify a visitor’s hair in video frames. Pass the identified hair to an XGBoost algorithm to determine hair style and hair.

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   Answer: A

   Explanation

   I was sure that it was option C but but when we want to select the option requiring the least amount of effort, it must be A. Hair color is detected by ResNet-50, not by semantic algorithm. So, object detection algorithms are generally easier to implement and fine-tune compared to semantic segmentation algorithms. They can accurately locate and extract specific objects, such as hair, from the video frames, simplifying the subsequent analysis. Additionally, ResNet-50 is a widely used pre-trained model for image classification tasks, making it relatively straightforward to apply for determining hair style and hair color

   Source: Adapted from community discussion — Denise123

   References

   • AWS documentation

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3. Question 173 · 368 in bank

   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?

   • AUse SageMaker Model Debugger to automatically debug the predictions, generate the explanation, and attach the explanation report.
   • BUse AWS Lambda to provide feature importance and partial dependence plots. Use the plots to generate and attach the explanation report.
   • CUse SageMaker Clarify to generate the explanation report. Attach the report to the predicted results.
   • DUse custom Amazon CloudWatch metrics to generate the explanation report. Attach the report to the predicted results.

   Show answer

   Answer: C

   Explanation

   The solution that will meet these requirements with the least development effort is C. Using SageMaker Clarify to generate the explanation report, and attaching the report to the predicted results . This solution allows the financial services company to use SageMaker Clarify, a feature that provides machine learning (ML) model transparency and explainability, to generate the explanation report for each loan approval prediction. SageMaker Clarify can provide feature importance scores, which indicate how much each feature contributes to the prediction, and SHAP values, which measure how each feature affects the prediction compared to the average prediction . The company can use these metrics to generate and attach the explanation report that contains the reason for why the customer was approved or denied for a loan.

   Source: Adapted from community discussion — Mickey321

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4. Question 174 · 368 in bank

   A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric. This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours. With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s). Which visualization will accomplish this?

   • AA histogram showing whether the most important input feature is Gaussian.
   • BA scatter plot with points colored by target variable that uses t-Distributed Stochastic Neighbor Embedding (t-SNE) to visualize the large number of input variables in an easier-to-read dimension.
   • CA scatter plot showing the performance of the objective metric over each training iteration.
   • DA scatter plot showing the correlation between maximum tree depth and the objective metric.

   Show answer

   Answer: D

   Explanation

   This is a very tricky question. The idea is to reconfigure the ranges of the hyperparameters. A refers to a feature, not a hyperparameter. A is out. C refers to training the model, not optimizing the range of hyperparameters. C is out. Now it gets tricky. D will let you find determine what the approximately best tree depth is. That's good. That's what you're trying to do but it's only one of many hyperparameters. It's the best choice so far. B is tricky. t-SNE does help you visualize multidimensional data but option B refers to input variables, not hyperparameters. For this very tricky question, I would do with D. It's the only one that accomplishes the task of limiting the range of a hyperparameter, even if it is only one of them.

   Source: Adapted from community discussion — cloud_trail

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5. Question 175 · 368 in bank

   A financial company sends special offers to customers through weekly email campaigns. A bulk email marketing system takes the list of email addresses as an input and sends the marketing campaign messages in batches. Few customers use the offers from the campaign messages. The company does not want to send irrelevant offers to customers. A machine learning (ML) team at the company is using Amazon SageMaker to build a model to recommend specific offers to each customer based on the customer's profile and the offers that the customer has accepted in the past. Which solution will meet these requirements with the MOST operational efficiency?

   • AUse the Factorization Machines algorithm to build a model that can generate personalized offer recommendations for customers. Deploy a SageMaker endpoint to generate offer recommendations. Feed the offer recommendations into the bulk email marketing system.
   • BUse the Neural Collaborative Filtering algorithm to build a model that can generate personalized offer recommendations for customers. Deploy a SageMaker endpoint to generate offer recommendations. Feed the offer recommendations into the bulk email marketing system.
   • CUse the Neural Collaborative Filtering algorithm to build a model that can generate personalized offer recommendations for customers. Deploy a SageMaker batch inference job to generate offer recommendations. Feed the offer recommendations into the bulk email marketing system.
   • DUse the Factorization Machines algorithm to build a model that can generate personalized offer recommendations for customers. Deploy a SageMaker batch inference job to generate offer recommendations. Feed the offer recommendations into the bulk email marketing system.

   Show answer

   Answer: D

   Explanation

   D makes more sence to me. Collaborative filtering takes into account other users preferences which is what we want to avoid because we do not want irrelevant promotions

   Source: Adapted from community discussion — endeesa

   References

   • AWS documentation

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6. Question 176 · 368 in bank

   A social media company wants to develop a machine learning (ML) model to detect inappropriate or offensive content in images. The company has collected a large dataset of labeled images and plans to use the built-in Amazon SageMaker image classification algorithm to train the model. The company also intends to use SageMaker pipe mode to speed up the training. The company splits the dataset into training, validation, and testing datasets. The company stores the training and validation images in folders that are named Training and Validation, respectively. The folders contain subfolders that correspond to the names of the dataset classes. The company resizes the images to the same size and generates two input manifest files named training.lst and validation.lst, for the training dataset and the validation dataset, respectively. Finally, the company creates two separate Amazon S3 buckets for uploads of the training dataset and the validation dataset. Which additional data preparation steps should the company take before uploading the files to Amazon S3?

   • AGenerate two Apache Parquet files, training.parquet and validation.parquet, by reading the images into a Pandas data frame and storing the data frame as a Parquet file. Upload the Parquet files to the training S3 bucket.
   • BCompress the training and validation directories by using the Snappy compression library. Upload the manifest and compressed files to the training S3 bucket.
   • CCompress the training and validation directories by using the gzip compression library. Upload the manifest and compressed files to the training S3 bucket.
   • DGenerate two RecordIO files, training.rec and validation.rec, from the manifest files by using the im2rec Apache MXNet utility tool. Upload the RecordIO files to the training S3 bucket.

   Show answer

   Answer: D

   Explanation

   Amazon SageMaker's built-in image classification algorithm supports input data in RecordIO format for training. RecordIO is a binary file format that efficiently stores images and labels in a compact format, making it suitable for training deep learning models with large datasets. The im2rec utility tool provided by Apache MXNet can be used to generate RecordIO files from the manifest files (training.lst and validation.lst) containing image paths and labels. Using RecordIO files allows for efficient streaming of data during training, especially when combined with SageMaker's pipe mode, which can speed up the training process by reducing disk I/O.

   Source: Adapted from community discussion — AIWave

   References

   • AWS documentation

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7. Question 177 · 368 in bank

   A media company wants to create a solution that identifies celebrities in pictures that users upload. The company also wants to identify the IP address and the timestamp details from the users so the company can prevent users from uploading pictures from unauthorized locations. Which solution will meet these requirements with LEAST development effort?

   • AUse AWS Panorama to identify celebrities in the pictures. Use AWS CloudTrail to capture IP address and timestamp details.
   • BUse AWS Panorama to identify celebrities in the pictures. Make calls to the AWS Panorama Device SDK to capture IP address and timestamp details.
   • CUse Amazon Rekognition to identify celebrities in the pictures. Use AWS CloudTrail to capture IP address and timestamp details.
   • DUse Amazon Rekognition to identify celebrities in the pictures. Use the text detection feature to capture IP address and timestamp details.

   Show answer

   Answer: C

   Explanation

   A. NO - AWS Panorama is for edge computing B. NO - AWS Panorama is for edge computing C. YES - best practice D. NO - IP address cannot be captured from the image, but from internet traffic

   Source: Adapted from community discussion — loict

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8. Question 178 · 368 in bank

   Select 2

   A pharmaceutical company performs periodic audits of clinical trial sites to quickly resolve critical findings. The company stores audit documents in text format. Auditors have requested help from a data science team to quickly analyze the documents. The auditors need to discover the 10 main topics within the documents to prioritize and distribute the review work among the auditing team members. Documents that describe adverse events must receive the highest priority. A data scientist will use statistical modeling to discover abstract topics and to provide a list of the top words for each category to help the auditors assess the relevance of the topic. Which algorithms are best suited to this scenario? (Choose two.)

   • ALatent Dirichlet allocation (LDA)
   • BRandom forest classifier
   • CNeural topic modeling (NTM)
   • DLinear support vector machine
   • ELinear regression

   Show answer

   Answer: AC

   Explanation

   A. LDA is designed to discover abstract topics in a collection of documents. It is commonly used for topic modeling and is one of the most popular techniques for extracting topics from text data. C. NTM is also used in topic modeling. It uses deep learning to discover topics in a collection of documents, and it can produce similar results to LDA but potentially with better accuracy due to its neural network foundation. Incorrect choices: B. Random forest classifier is a classification algorithm. It is better suited for classification tasks based on labeled data. D. SVM is also a classification algorithm. It works well for binary classification problems. E. Linear regression is a regression algorithm used to predict continuous values. It's not suitable for topic modeling.

   Source: Adapted from community discussion — seifskl

   References

   • AWS documentation
   • AWS documentation

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9. Question 179 · 368 in bank

   A company needs to deploy a chatbot to answer common questions from customers. The chatbot must base its answers on company documentation. Which solution will meet these requirements with the LEAST development effort?

   • AIndex company documents by using Amazon Kendra. Integrate the chatbot with Amazon Kendra by using the Amazon Kendra Query API operation to answer customer questions.
   • BTrain a Bidirectional Attention Flow (BiDAF) network based on past customer questions and company documents. Deploy the model as a real-time Amazon SageMaker endpoint. Integrate the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation to answer customer questions.
   • CTrain an Amazon SageMaker Blazing Text model based on past customer questions and company documents. Deploy the model as a real-time SageMaker endpoint. Integrate the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation to answer customer questions.
   • DIndex company documents by using Amazon OpenSearch Service. Integrate the chatbot with OpenSearch Service by using the OpenSearch Service k-nearest neighbors (k-NN) Query API operation to answer customer questions.

   Show answer

   Answer: A

   Explanation

   A. Amazon Kendra is designed to search through various types of documents and provide relevant answers. B. Training a BiDAF network requires expertise in deep learning and natural language processing. It would require substantial effort in data preparation, model training, and integration. C. Amazon SageMaker Blazing Text is primarily used for text classification and word embeddings, not for extracting answers from company documents based on user queries. D. Amazon OpenSearch Service is a search and analytics engine, but it's not tailored for extracting precise answers from documents. The k-NN Query API is used for similarity searches and isn't inherently designed to answer questions based on document content.

   Source: Adapted from community discussion — seifskl

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10. Question 180 · 368 in bank

    A company wants to conduct targeted marketing to sell solar panels to homeowners. The company wants to use machine learning (ML) technologies to identify which houses already have solar panels. The company has collected 8,000 satellite images as training data and will use Amazon SageMaker Ground Truth to label the data. The company has a small internal team that is working on the project. The internal team has no ML expertise and no ML experience. Which solution will meet these requirements with the LEAST amount of effort from the internal team?

    • ASet up a private workforce that consists of the internal team. Use the private workforce and the SageMaker Ground Truth active learning feature to label the data. Use Amazon Rekognition Custom Labels for model training and hosting.
    • BSet up a private workforce that consists of the internal team. Use the private workforce to label the data. Use Amazon Rekognition Custom Labels for model training and hosting.
    • CSet up a private workforce that consists of the internal team. Use the private workforce and the SageMaker Ground Truth active learning feature to label the data. Use the SageMaker Object Detection algorithm to train a model. Use SageMaker batch transform for inference.
    • DSet up a public workforce. Use the public workforce to label the data. Use the SageMaker Object Detection algorithm to train a model. Use SageMaker batch transform for inference.

    Show answer

    Answer: A

    Explanation

    B. Set up a private workforce that consists of the internal team. Use the private workforce to label the data. Use Amazon Rekognition Custom Labels for model training and hosting. By setting up a private workforce consisting of the internal team and using Amazon Rekognition Custom Labels, the company can leverage the labeling capabilities of the internal team to label the data. Amazon Rekognition Custom Labels can then be used for model training and hosting. This option eliminates the need for additional complex steps such as active learning or object detection algorithm training, which may require more ML expertise and effort from the internal team. Instead, it relies on the simplicity and convenience of using Amazon Rekognition Custom Labels for model training and hosting, making it the least effort-intensive option for the team with no ML expertise or experience.

    Source: Adapted from community discussion — RRST

    References

    • AWS documentation
    • AWS documentation

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MLS-C01 practice, question 171. 368 questions available.