MLS-C01 practice questions 311–320

1. Question 311 · 368 in bank

   A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet. How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?

   • ACreate a NAT gateway within the corporate VPC.
   • BRoute Amazon SageMaker traffic through an on-premises network.
   • CCreate Amazon SageMaker VPC interface endpoints within the corporate VPC.
   • DCreate VPC peering with Amazon VPC hosting Amazon SageMaker.

   Show answer

   Answer: C

   Explanation

   NAT CLOUD GO OUT TO THE INTERNET, IT STILL CANNOT PREVENT DOWNLOAD MALICIOUS BY YOURSELF. THE RIGHT ANSWER IS C. C.INTERFACE VPC ENDPOINT https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-dg.pdf (516) https://docs.aws.amazon.com/zh_tw/vpc/latest/userguide/vpc-endpoints.html

   Source: Adapted from community discussion — DonaldCMLIN

   References

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

   Next question

2. Question 312 · 368 in bank

   A Machine Learning Specialist is training a model to identify the make and model of vehicles in images. The Specialist wants to use transfer learning and an existing model trained on images of general objects. The Specialist collated a large custom dataset of pictures containing different vehicle makes and models. What should the Specialist do to initialize the model to re-train it with the custom data?

   • AInitialize the model with random weights in all layers including the last fully connected layer.
   • BInitialize the model with pre-trained weights in all layers and replace the last fully connected layer.
   • CInitialize the model with random weights in all layers and replace the last fully connected layer.
   • DInitialize the model with pre-trained weights in all layers including the last fully connected layer.

   Show answer

   Answer: B

   Explanation

   In transfer learning, a pre-trained model is used as a starting point to train a new model on a different task, typically using a smaller dataset. The pre-trained model contains weights that have been learned from a large amount of data on a related task, and these weights can be leveraged to train the new model more efficiently. To re-train the model with the custom data, the Specialist should initialize the model with pre-trained weights in all layers, as these weights can provide a good starting point for the new task. The Specialist should then replace the last fully connected layer, which is responsible for making the final predictions, as this layer will likely need to be modified to reflect the new task. By keeping the pre-trained weights in the other layers, the Specialist can take advantage of the knowledge learned from the previous task, and potentially speed up the training process.

   Source: Adapted from community discussion — AjoseO

   Next question

3. Question 313 · 368 in bank

   An office security agency conducted a successful pilot using 100 cameras installed at key locations within the main office. Images from the cameras were uploaded to Amazon S3 and tagged using Amazon Rekognition, and the results were stored in Amazon ES. The agency is now looking to expand the pilot into a full production system using thousands of video cameras in its office locations globally. The goal is to identify activities performed by non-employees in real time Which solution should the agency consider?

   • AUse a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection of known employees, and alert when non-employees are detected.
   • BUse a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Image to detect faces from a collection of known employees and alert when non-employees are detected.
   • CInstall AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis Video Streams for each camera. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection on each stream, and alert when non-employees are detected.
   • DInstall AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis Video Streams for each camera. On each stream, run an AWS Lambda function to capture image fragments and then call Amazon Rekognition Image to detect faces from a collection of known employees, and alert when non-employees are detected.

   Show answer

   Answer: A

   Explanation

   Answer is "A". C and D are out as DeepLens is not offered as a commercial product. It is purely for developers to experiment with. From https://aws.amazon.com/deeplens/device-terms-of-use/ " (i) you may use the AWS DeepLens Device for personal, educational, evaluation, development, and testing purposes, and not to process your production workloads;" A is correct as it's will analyse live video streams instead of images. From https://aws.amazon.com/rekognition/video-features/ "Amazon Rekognition Video can identify known people in a video by searching against a private repository of face images. "

   Source: Adapted from community discussion — scuzzy2010

   References

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

   Next question

4. Question 314 · 368 in bank

   A Data Engineer needs to build a model using a dataset containing customer credit card information How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

   • AUse a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.
   • BUse an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.
   • CUse an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC. Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.
   • DUse AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.

   Show answer

   Answer: D

   Explanation

   The reason for this choice is that AWS KMS is a service that allows you to easily create and manage encryption keys and control the use of encryption across a wide range of AWS services and in your applications1. By using AWS KMS, you can encrypt the data on Amazon S3, which is a durable, scalable, and secure object storage service2, and on Amazon SageMaker, which is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly3. This way, you can protect the data at rest and in transit.

   Source: Adapted from community discussion — Mickey321

   References

   • AWS documentation

   Next question

5. Question 315 · 368 in bank

   A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers. Currently, the company has the following data in Amazon Aurora: ✑ Profiles for all past and existing customers ✑ Profiles for all past and existing insured pets ✑ Policy-level information ✑ Premiums received ✑ Claims paid What steps should be taken to implement a machine learning model to identify potential new customers on social media?

   • AUse regression on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
   • BUse clustering on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
   • CUse a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.
   • DUse a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.

   Show answer

   Answer: B

   Explanation

   All of the questions in the preceding examples rely on having example data that includes answers. There are times that you don't need, or can't get, example data with answers. This is true for problems whose answers identify groups. For example: "I want to group current and prospective customers into 10 groups based on their attributes. How should I group them? " You might choose to send the mailing to customers in the group that has the highest percentage of current customers. That is, prospective customers that most resemble current customers based on the same set of attributes. For this type of question, Amazon SageMaker provides the K-Means Algorithm. https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html Clustering algorithms are unsupervised. In unsupervised learning, labels that might be associated with the objects in the training dataset aren't used. https://docs.aws.amazon.com/sagemaker/latest/dg/algo-kmeans-tech-notes.html THE ANSWER COULD BE B.clustering on customer profile data to understand key characteristic

   Source: Adapted from community discussion — DonaldCMLIN

   References

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

   Next question

6. Question 316 · 368 in bank

   A manufacturing company has a large set of labeled historical sales data. The manufacturer would like to predict how many units of a particular part should be produced each quarter. Which machine learning approach should be used to solve this problem?

   • ALogistic regression
   • BRandom Cut Forest (RCF)
   • CPrincipal component analysis (PCA)
   • DLinear regression

   Show answer

   Answer: D

   Explanation

   Amazon SageMaker Random Cut Forest (RCF) is an unsupervised algorithm for detecting anomalous data points within a data set Answer is D 100%

   Source: Adapted from community discussion — syu31svc

   References

   • AWS documentation
   • AWS documentation

   Next question

7. Question 317 · 368 in bank

   A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements: ✑ Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum. ✑ Support event-driven ETL pipelines ✑ Provide a quick and easy way to understand metadata Which approach meets these requirements?

   • AUse an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.
   • BUse an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.
   • CUse an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.
   • DUse an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.

   Show answer

   Answer: A

   Explanation

   BOTH A AND B ARE ANSWERS. BUT external Apache Hive MIGHT BE NOT SERVERLESS SOLUTION. The AWS Glue Data Catalog is your persistent metadata store. It is a managed service that lets you store, annotate, and share metadata in the AWS Cloud in the same way you would in an Apache Hive metastore. The Data Catalog is a drop-in replacement for the Apache Hive Metastore https://docs.aws.amazon.com/zh_tw/glue/latest/dg/components-overview.html BEAUTIFUL ANSWER IS A.

   Source: Adapted from community discussion — DonaldCMLIN

   References

   • AWS documentation
   • AWS documentation
   • AWS documentation

   Next question

8. Question 318 · 368 in bank

   A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily. The model accuracy is acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes. What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?

   • ADo not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training.
   • BChange the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals.
   • CSwitch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals.
   • DMove the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.

   Show answer

   Answer: B

   Explanation

   Answer is B. "minimize coding effort and infrastructure changes" If we use DeepAR then the code and infra has to be changed to work with DeepAR.

   Source: Adapted from community discussion — cybe001

   References

   • AWS documentation
   • AWS documentation
   • AWS documentation

   Next question

9. Question 319 · 368 in bank

   Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?

   • ARecall
   • BMisclassification rate
   • CMean absolute percentage error (MAPE)
   • DArea Under the ROC Curve (AUC)

   Show answer

   Answer: D

   Explanation

   Area Under the ROC Curve (AUC) is a commonly used metric to compare and evaluate machine learning classification models against each other. The AUC measures the model's ability to distinguish between positive and negative classes, and its performance across different classification thresholds. The AUC ranges from 0 to 1, with a score of 1 representing a perfect classifier and a score of 0.5 representing a classifier that is no better than random. While recall is an important evaluation metric for classification models, it alone is not sufficient to compare and evaluate different models against each other. Recall measures the proportion of actual positive cases that are correctly identified as positive, but does not take into account the false positive rate.

   Source: Adapted from community discussion — AjoseO

   References

   • AWS documentation

   Next question

10. Question 320 · 368 in bank

    A company is running a machine learning prediction service that generates 100 TB of predictions every day. A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from the predictions, and forward a read-only version to the Business team. Which solution requires the LEAST coding effort?

    • ARun a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3. Give the Business team read-only access to S3.
    • BGenerate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team.
    • CRun a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3. Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team.
    • DGenerate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.

    Show answer

    Answer: C

    Explanation

    using quick sight for creation of the precision recall with 100 TB every day cann't be done since the max size for quick sight to deal with is : For Standard edition, 25 million (25,000,000) rows or 25 GB for each dataset For Enterprise edition, 1 billion (1,000,000,000) rows or 1 TB for each dataset acc to AWS documentation : https://docs.aws.amazon.com/quicksight/latest/user/data-source-limits.html but we can do it with EMR and latterly use quick sight to visualize the results

    Source: Adapted from community discussion — teka112233

    References

    • AWS documentation
    • AWS documentation
    • AWS documentation

    Next question

Keyboard:J Kmove between questionsA–Dselect an answerRshow or hide the answer← →change page

MLS-C01 practice, question 311. 368 questions available.