MLS-C01 practice questions 51–60

1. Question 51 · 368 in bank

   A company sells thousands of products on a public website and wants to automatically identify products with potential durability problems. The company has 1.000 reviews with date, star rating, review text, review summary, and customer email fields, but many reviews are incomplete and have empty fields. Each review has already been labeled with the correct durability result. A machine learning specialist must train a model to identify reviews expressing concerns over product durability. The first model needs to be trained and ready to review in 2 days. What is the MOST direct approach to solve this problem within 2 days?

   • ATrain a custom classifier by using Amazon Comprehend.
   • BBuild a recurrent neural network (RNN) in Amazon SageMaker by using Gluon and Apache MXNet.
   • CTrain a built-in BlazingText model using Word2Vec mode in Amazon SageMaker.
   • DUse a built-in seq2seq model in Amazon SageMaker.

   Show answer

   Answer: A

   Explanation

   The most direct approach to solve this problem within 2 days is option A, train a custom classifier by using Amazon Comprehend. By doing so, you can use Amazon Comprehend, a natural language processing (NLP) service that uses machine learning to find insights and relationships in text, to create a custom classifier that can identify reviews expressing concerns over product durability. You can use the labeled reviews as your training data and specify the durability result as the class label. Amazon Comprehend will automatically preprocess the text, extract features, and train the classifier for you. You can also use Amazon Comprehend to evaluate the performance of your classifier and deploy it as an endpoint. This way, you can train a model to solve this problem within 2 days without requiring much coding or infrastructure management.

   Source: Adapted from community discussion — Mickey321

   References

   • AWS documentation

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2. Question 52 · 368 in bank

   A company that runs an online library is implementing a chatbot using Amazon Lex to provide book recommendations based on category. This intent is fulfilled by an AWS Lambda function that queries an Amazon DynamoDB table for a list of book titles, given a particular category. For testing, there are only three categories implemented as the custom slot types: "comedy," "adventure,` and "documentary.` A machine learning (ML) specialist notices that sometimes the request cannot be fulfilled because Amazon Lex cannot understand the category spoken by users with utterances such as "funny," "fun," and "humor." The ML specialist needs to fix the problem without changing the Lambda code or data in DynamoDB. How should the ML specialist fix the problem?

   • AAdd the unrecognized words in the enumeration values list as new values in the slot type.
   • BCreate a new custom slot type, add the unrecognized words to this slot type as enumeration values, and use this slot type for the slot.
   • CUse the AMAZON.SearchQuery built-in slot types for custom searches in the database.
   • DAdd the unrecognized words as synonyms in the custom slot type.

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   Answer: D

   Explanation

   D is the answer. The unrecognized words are synonyms for "comedy", so they should be added as synonyms under the comedy slot type see the excerpt: "For each intent, you can specify parameters that indicate the information that the intent needs to fulfill the user's request. These parameters, or slots, have a type. A slot type is a list of values that Amazon Lex uses to train the machine learning model to recognize values for a slot. For example, you can define a slot type called "Genres." Each value in the slot type is the name of a genre, "comedy," "adventure," "documentary," etc. You can define a synonym for a slot type value. For example, you can define the synonyms "funny" and "humorous" for the value "comedy."" https://docs.aws.amazon.com/lex/latest/dg/howitworks-custom-slots.html

   Source: Adapted from community discussion — ovokpus

   References

   • AWS documentation
   • AWS documentation

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3. Question 53 · 368 in bank

   A manufacturing company uses machine learning (ML) models to detect quality issues. The models use images that are taken of the company's product at the end of each production step. The company has thousands of machines at the production site that generate one image per second on average. The company ran a successful pilot with a single manufacturing machine. For the pilot, ML specialists used an industrial PC that ran AWS IoT Greengrass with a long-running AWS Lambda function that uploaded the images to Amazon S3. The uploaded images invoked a Lambda function that was written in Python to perform inference by using an Amazon SageMaker endpoint that ran a custom model. The inference results were forwarded back to a web service that was hosted at the production site to prevent faulty products from being shipped. The company scaled the solution out to all manufacturing machines by installing similarly configured industrial PCs on each production machine. However, latency for predictions increased beyond acceptable limits. Analysis shows that the internet connection is at its capacity limit. How can the company resolve this issue MOST cost-effectively?

   • ASet up a 10 Gbps AWS Direct Connect connection between the production site and the nearest AWS Region. Use the Direct Connect connection to upload the images. Increase the size of the instances and the number of instances that are used by the SageMaker endpoint.
   • BExtend the long-running Lambda function that runs on AWS IoT Greengrass to compress the images and upload the compressed files to Amazon S3. Decompress the files by using a separate Lambda function that invokes the existing Lambda function to run the inference pipeline.
   • CUse auto scaling for SageMaker. Set up an AWS Direct Connect connection between the production site and the nearest AWS Region. Use the Direct Connect connection to upload the images.
   • DDeploy the Lambda function and the ML models onto the AWS IoT Greengrass core that is running on the industrial PCs that are installed on each machine. Extend the long-running Lambda function that runs on AWS IoT Greengrass to invoke the Lambda function with the captured images and run the inference on the edge component that forwards the results directly to the web service.

   Show answer

   Answer: D

   Explanation

   Option D eliminates the need for internet connection since the inference is done on the edge component, and the results are directly forwarded to the web service. This approach also reduces the need for larger instances and direct connect connections, thus being the most cost-effective solution.

   Source: Adapted from community discussion — AjoseO

   References

   • AWS documentation

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4. Question 54 · 368 in bank

   A data scientist is using an Amazon SageMaker notebook instance and needs to securely access data stored in a specific Amazon S3 bucket. How should the data scientist accomplish this?

   • AAdd an S3 bucket policy allowing GetObject, PutObject, and ListBucket permissions to the Amazon SageMaker notebook ARN as principal.
   • BEncrypt the objects in the S3 bucket with a custom AWS Key Management Service (AWS KMS) key that only the notebook owner has access to.
   • CAttach the policy to the IAM role associated with the notebook that allows GetObject, PutObject, and ListBucket operations to the specific S3 bucket.
   • DUse a script in a lifecycle configuration to configure the AWS CLI on the instance with an access key ID and secret.

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   Answer: C

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5. Question 55 · 368 in bank

   A company is launching a new product and needs to build a mechanism to monitor comments about the company and its new product on social media. The company needs to be able to evaluate the sentiment expressed in social media posts, and visualize trends and configure alarms based on various thresholds. The company needs to implement this solution quickly, and wants to minimize the infrastructure and data science resources needed to evaluate the messages. The company already has a solution in place to collect posts and store them within an Amazon S3 bucket. What services should the data science team use to deliver this solution?

   • ATrain a model in Amazon SageMaker by using the BlazingText algorithm to detect sentiment in the corpus of social media posts. Expose an endpoint that can be called by AWS Lambda. Trigger a Lambda function when posts are added to the S3 bucket to invoke the endpoint and record the sentiment in an Amazon DynamoDB table and in a custom Amazon CloudWatch metric. Use CloudWatch alarms to notify analysts of trends.
   • BTrain a model in Amazon SageMaker by using the semantic segmentation algorithm to model the semantic content in the corpus of social media posts. Expose an endpoint that can be called by AWS Lambda. Trigger a Lambda function when objects are added to the S3 bucket to invoke the endpoint and record the sentiment in an Amazon DynamoDB table. Schedule a second Lambda function to query recently added records and send an Amazon Simple Notification Service (Amazon SNS) notification to notify analysts of trends.
   • CTrigger an AWS Lambda function when social media posts are added to the S3 bucket. Call Amazon Comprehend for each post to capture the sentiment in the message and record the sentiment in an Amazon DynamoDB table. Schedule a second Lambda function to query recently added records and send an Amazon Simple Notification Service (Amazon SNS) notification to notify analysts of trends.
   • DTrigger an AWS Lambda function when social media posts are added to the S3 bucket. Call Amazon Comprehend for each post to capture the sentiment in the message and record the sentiment in a custom Amazon CloudWatch metric and in S3. Use CloudWatch alarms to notify analysts of trends.

   Show answer

   Answer: D

   Explanation

   D is the correct answer. Following from the previous comment. The company wants to minimize the infrastructure and data science resources needed to evaluate the messages. Therefore any custom services would be eliminated (A and B). Similarly DynamoDB would add complexity to the infrastructure there C is eliminated. leaving D

   Source: Adapted from community discussion — cron0001

   References

   • AWS documentation

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6. Question 56 · 368 in bank

   A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains Personally Identifiable Information (PII). The dataset: ✑ Must be accessible from a VPC only. ✑ Must not traverse the public internet. How can these requirements be satisfied?

   • ACreate a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.
   • BCreate a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.
   • CCreate a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.
   • DCreate a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance

   Show answer

   Answer: A

   Explanation

   Important things to note here is that 1. "The Data in S3 Needs to be Accessible from VPC" 2. "Traffic should not Traverse internet" To fulfill Requirement #2 we need a VPC endpoint To RESTRICT the access to S3/Bucket - Access allowed only from VPC via VPC Endpoint Even though Sagemaker uses EC2 - we are NOT asked to secure the EC2 :) So the answer is A

   Source: Adapted from community discussion — rajs

   References

   • AWS documentation
   • AWS documentation
   • AWS documentation

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7. Question 57 · 368 in bank

   A bank wants to launch a low-rate credit promotion. The bank is located in a town that recently experienced economic hardship. Only some of the bank's customers were affected by the crisis, so the bank's credit team must identify which customers to target with the promotion. However, the credit team wants to make sure that loyal customers' full credit history is considered when the decision is made. The bank's data science team developed a model that classifies account transactions and understands credit eligibility. The data science team used the XGBoost algorithm to train the model. The team used 7 years of bank transaction historical data for training and hyperparameter tuning over the course of several days. The accuracy of the model is sufficient, but the credit team is struggling to explain accurately why the model denies credit to some customers. The credit team has almost no skill in data science. What should the data science team do to address this issue in the MOST operationally efficient manner?

   • AUse Amazon SageMaker Studio to rebuild the model. Create a notebook that uses the XGBoost training container to perform model training. Deploy the model at an endpoint. Enable Amazon SageMaker Model Monitor to store inferences. Use the inferences to create Shapley values that help explain model behavior. Create a chart that shows features and SHapley Additive exPlanations (SHAP) values to explain to the credit team how the features affect the model outcomes.
   • BUse Amazon SageMaker Studio to rebuild the model. Create a notebook that uses the XGBoost training container to perform model training. Activate Amazon SageMaker Debugger, and configure it to calculate and collect Shapley values. Create a chart that shows features and SHapley Additive exPlanations (SHAP) values to explain to the credit team how the features affect the model outcomes.
   • CCreate an Amazon SageMaker notebook instance. Use the notebook instance and the XGBoost library to locally retrain the model. Use the plot_importance() method in the Python XGBoost interface to create a feature importance chart. Use that chart to explain to the credit team how the features affect the model outcomes.
   • DUse Amazon SageMaker Studio to rebuild the model. Create a notebook that uses the XGBoost training container to perform model training. Deploy the model at an endpoint. Use Amazon SageMaker Processing to post-analyze the model and create a feature importance explainability chart automatically for the credit team.

   Show answer

   Answer: B

   Explanation

   There are so many explanations, but most of them are just superfacial, focusing on what service is related to SHAP. This is the only one really answer the difference between A and C. 1. Both SagaMaker Model Monitor and Debugger can explain model, can generate SHAP. so it should be either A or C 2. Monitor is about inference. After deploy the model, we may find some attributes start to contribute more to the model, contradict to the training dataset. This case we use SageMaker Model Monitor. But our problem is not about deploying, is still in training stage. We only want to figure out why some customer with specfic characteristics are more likely to get loan, in other words, certain feature contribute more to the prediction. It is C !!!! If you don't fully understant the question, stop explaining !!!

   Source: Adapted from community discussion — ZSun

   References

   • AWS documentation

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8. Question 58 · 368 in bank

   A data science team is planning to build a natural language processing (NLP) application. The application's text preprocessing stage will include part-of-speech tagging and key phase extraction. The preprocessed text will be input to a custom classification algorithm that the data science team has already written and trained using Apache MXNet. Which solution can the team build MOST quickly to meet these requirements?

   • AUse Amazon Comprehend for the part-of-speech tagging, key phase extraction, and classification tasks.
   • BUse an NLP library in Amazon SageMaker for the part-of-speech tagging. Use Amazon Comprehend for the key phase extraction. Use AWS Deep Learning Containers with Amazon SageMaker to build the custom classifier.
   • CUse Amazon Comprehend for the part-of-speech tagging and key phase extraction tasks. Use Amazon SageMaker built-in Latent Dirichlet Allocation (LDA) algorithm to build the custom classifier.
   • DUse Amazon Comprehend for the part-of-speech tagging and key phase extraction tasks. Use AWS Deep Learning Containers with Amazon SageMaker to build the custom classifier.

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   Answer: D

   Explanation

   Agree. A is my answer. 1. part of speech tagging : https://docs.aws.amazon.com/comprehend/latest/dg/API_PartOfSpeechTag.html 2. Key phas extraction https://docs.aws.amazon.com/comprehend/latest/dg/how-key-phrases.html 3. custum classification algorithm https://docs.aws.amazon.com/comprehend/latest/dg/how-document-classification.html

   Source: Adapted from community discussion — tgaos

   References

   • AWS documentation
   • AWS documentation
   • AWS documentation
   • AWS documentation
   • AWS documentation

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9. Question 59 · 368 in bank

   A machine learning (ML) specialist must develop a classification model for a financial services company. A domain expert provides the dataset, which is tabular with 10,000 rows and 1,020 features. During exploratory data analysis, the specialist finds no missing values and a small percentage of duplicate rows. There are correlation scores of > 0.9 for 200 feature pairs. The mean value of each feature is similar to its 50th percentile. Which feature engineering strategy should the ML specialist use with Amazon SageMaker?

   • AApply dimensionality reduction by using the principal component analysis (PCA) algorithm.
   • BDrop the features with low correlation scores by using a Jupyter notebook.
   • CApply anomaly detection by using the Random Cut Forest (RCF) algorithm.
   • DConcatenate the features with high correlation scores by using a Jupyter notebook.

   Show answer

   Answer: A

   Explanation

   the best feature engineering strategy for the ML specialist to use with Amazon SageMaker is to apply dimensionality reduction by using the PCA algorithm.

   Source: Adapted from community discussion — Mickey321

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10. Question 60 · 368 in bank

    A manufacturing company asks its machine learning specialist to develop a model that classifies defective parts into one of eight defect types. The company has provided roughly 100,000 images per defect type for training. During the initial training of the image classification model, the specialist notices that the validation accuracy is 80%, while the training accuracy is 90%. It is known that human-level performance for this type of image classification is around 90%. What should the specialist consider to fix this issue?

    • AA longer training time
    • BMaking the network larger
    • CUsing a different optimizer
    • DUsing some form of regularization

    Show answer

    Answer: D

    Explanation

    The specialist should consider using some form of regularization to fix this issue. Regularization techniques such as dropout or L2 regularization can help prevent overfitting, which can occur when the model performs well on the training data but poorly on the validation data. Option A, a longer training time, might not necessarily fix the issue and could lead to overfitting if the model is already performing well on the training data. Option B, making the network larger, could also lead to overfitting and may not be necessary if the current network architecture is sufficient to perform the classification task. Option C, using a different optimizer, might not necessarily fix the issue and could lead to slower convergence or worse performance. Therefore, option D, using some form of regularization, is the most appropriate solution to consider in this situation.

    Source: Adapted from community discussion — AjoseO

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MLS-C01 practice, question 51. 368 questions available.