MLS-C01 practice questions 71–80

1. Question 71 · 368 in bank

   Select 2

   A machine learning specialist is developing a regression model to predict rental rates from rental listings. A variable named Wall_Color represents the most prominent exterior wall color of the property. The following is the sample data, excluding all other variables:

   

   The specialist chose a model that needs numerical input data. Which feature engineering approaches should the specialist use to allow the regression model to learn from the Wall_Color data? (Choose two.)

   • AApply integer transformation and set Red = 1, White = 5, and Green = 10.
   • BAdd new columns that store one-hot representation of colors.
   • CReplace the color name string by its length.
   • DCreate three columns to encode the color in RGB format.
   • EReplace each color name by its training set frequency.

   Show answer

   Answer: BE

   Explanation

   B. Add new columns that store one-hot representation of colors. One-hot encoding is a common approach to represent categorical variables as numerical values. This approach creates new binary variables for each category and assigns a value of 1 to the corresponding category and 0 to the others. In this case, the specialist can create three new binary variables, one for each color (Red, White, and Green) and use them as input to the regression model. E. Replace each color name by its training set frequency. Another approach to convert categorical variables into numerical ones is to replace each category with its frequency of occurrence in the training set. In this case, the specialist can replace the color names with their respective frequencies (1/3 for Red, 1/3 for White, and 1/3 for Green) to represent them numerically.

   Source: Adapted from community discussion — AjoseO

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2. Question 72 · 368 in bank

   A data scientist is working on a public sector project for an urban traffic system. While studying the traffic patterns, it is clear to the data scientist that the traffic behavior at each light is correlated, subject to a small stochastic error term. The data scientist must model the traffic behavior to analyze the traffic patterns and reduce congestion. How will the data scientist MOST effectively model the problem?

   • AThe data scientist should obtain a correlated equilibrium policy by formulating this problem as a multi-agent reinforcement learning problem.
   • BThe data scientist should obtain the optimal equilibrium policy by formulating this problem as a single-agent reinforcement learning problem.
   • CRather than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using historical data through a supervised learning approach.
   • DRather than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using unlabeled simulated data representing the new traffic patterns in the city and applying an unsupervised learning approach.

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   Answer: A

   Explanation

   A. The data scientist should obtain a correlated equilibrium policy by formulating this problem as a multi-agent reinforcement learning problem. In this scenario, where the traffic behavior at each light is correlated, a multi-agent reinforcement learning (MARL) approach is well-suited to model the problem. In MARL, multiple agents interact with each other and the environment, and their behavior is influenced by the behavior of other agents. This approach is particularly useful in modeling traffic systems, where the behavior of each vehicle is affected by the behavior of other vehicles and traffic lights. Formulating the problem as a MARL problem can help the data scientist obtain a correlated equilibrium policy, which can optimize traffic flow across multiple traffic lights by taking into account the correlations between them. By optimizing traffic flow across all traffic lights in a correlated way, it may be possible to reduce congestion and improve overall traffic efficiency.

   Source: Adapted from community discussion — AjoseO

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3. Question 73 · 368 in bank

   A data scientist is using the Amazon SageMaker Neural Topic Model (NTM) algorithm to build a model that recommends tags from blog posts. The raw blog post data is stored in an Amazon S3 bucket in JSON format. During model evaluation, the data scientist discovered that the model recommends certain stopwords such as "a," "an," and "the" as tags to certain blog posts, along with a few rare words that are present only in certain blog entries. After a few iterations of tag review with the content team, the data scientist notices that the rare words are unusual but feasible. The data scientist also must ensure that the tag recommendations of the generated model do not include the stopwords. What should the data scientist do to meet these requirements?

   • AUse the Amazon Comprehend entity recognition API operations. Remove the detected words from the blog post data. Replace the blog post data source in the S3 bucket.
   • BRun the SageMaker built-in principal component analysis (PCA) algorithm with the blog post data from the S3 bucket as the data source. Replace the blog post data in the S3 bucket with the results of the training job.
   • CUse the SageMaker built-in Object Detection algorithm instead of the NTM algorithm for the training job to process the blog post data.
   • DRemove the stopwords from the blog post data by using the CountVectorizer function in the scikit-learn library. Replace the blog post data in the S3 bucket with the results of the vectorizer.

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   Answer: D

   Explanation

   The stop words need to be removed. The rare words don't need to be removed because it has been found that they are feasible tags.

   Source: Adapted from community discussion — Peeking

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4. Question 74 · 368 in bank

   A company wants to create a data repository in the AWS Cloud for machine learning (ML) projects. The company wants to use AWS to perform complete ML lifecycles and wants to use Amazon S3 for the data storage. All of the company's data currently resides on premises and is 40 ׀¢׀’ in size. The company wants a solution that can transfer and automatically update data between the on-premises object storage and Amazon S3. The solution must support encryption, scheduling, monitoring, and data integrity validation. Which solution meets these requirements?

   • AUse the S3 sync command to compare the source S3 bucket and the destination S3 bucket. Determine which source files do not exist in the destination S3 bucket and which source files were modified.
   • BUse AWS Transfer for FTPS to transfer the files from the on-premises storage to Amazon S3.
   • CUse AWS DataSync to make an initial copy of the entire dataset. Schedule subsequent incremental transfers of changing data until the final cutover from on premises to AWS.
   • DUse S3 Batch Operations to pull data periodically from the on-premises storage. Enable S3 Versioning on the S3 bucket to protect against accidental overwrites.

   Show answer

   Answer: C

   Explanation

   AWS DataSync is a service that can be used to transfer large amounts of data between on-premises storage and Amazon S3, EFS, or FSx for Windows File Server. DataSync is optimized for fast, automated, and secure transfers of large amounts of data, and it supports scheduling, monitoring, and data integrity validation. In this scenario, the company wants a solution that can transfer and automatically update data between the on-premises object storage and Amazon S3, with support for encryption, scheduling, monitoring, and data integrity validation. DataSync meets all of these requirements, as it can transfer data using secure network connections, schedule data transfers, verify data integrity, and encrypt data in transit and at rest.

   Source: Adapted from community discussion — AjoseO

   References

   • AWS documentation

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5. Question 75 · 368 in bank

   A company has video feeds and images of a subway train station. The company wants to create a deep learning model that will alert the station manager if any passenger crosses the yellow safety line when there is no train in the station. The alert will be based on the video feeds. The company wants the model to detect the yellow line, the passengers who cross the yellow line, and the trains in the video feeds. This task requires labeling. The video data must remain confidential. A data scientist creates a bounding box to label the sample data and uses an object detection model. However, the object detection model cannot clearly demarcate the yellow line, the passengers who cross the yellow line, and the trains. Which labeling approach will help the company improve this model?

   • AUse Amazon Rekognition Custom Labels to label the dataset and create a custom Amazon Rekognition object detection model. Create a private workforce. Use Amazon Augmented AI (Amazon A2I) to review the low-confidence predictions and retrain the custom Amazon Rekognition model.
   • BUse an Amazon SageMaker Ground Truth object detection labeling task. Use Amazon Mechanical Turk as the labeling workforce.
   • CUse Amazon Rekognition Custom Labels to label the dataset and create a custom Amazon Rekognition object detection model. Create a workforce with a third-party AWS Marketplace vendor. Use Amazon Augmented AI (Amazon A2I) to review the low-confidence predictions and retrain the custom Amazon Rekognition model.
   • DUse an Amazon SageMaker Ground Truth semantic segmentation labeling task. Use a private workforce as the labeling workforce.

   Show answer

   Answer: D

   Explanation

   It's A. See https://aws.amazon.com/rekognition/custom-labels-features/. It says "The Rekognition Custom Labels console provides a visual interface to make labeling your images fast and simple. The interface allows you to apply a label to the entire image or to identify and label specific objects in images using bounding boxes with a simple click-and-drag interface." We are not using semantic segmentation, as it applies a label to every pixel. We don't want that, we want labels to bounding boxes.

   Source: Adapted from community discussion — wolfsong

   References

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

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6. Question 76 · 368 in bank

   Select 2

   A data engineer at a bank is evaluating a new tabular dataset that includes customer data. The data engineer will use the customer data to create a new model to predict customer behavior. After creating a correlation matrix for the variables, the data engineer notices that many of the 100 features are highly correlated with each other. Which steps should the data engineer take to address this issue? (Choose two.)

   • AUse a linear-based algorithm to train the model.
   • BApply principal component analysis (PCA).
   • CRemove a portion of highly correlated features from the dataset.
   • DApply min-max feature scaling to the dataset.
   • EApply one-hot encoding category-based variables.

   Show answer

   Answer: BC

   Explanation

   PCA is a widely used technique for reducing the dimensionality of high-dimensional datasets while retaining as much of the original variability as possible. It is particularly useful when dealing with highly correlated features. Removing a portion of highly correlated features can be another effective way to address the issue of high correlation. By removing some of the correlated features, the model can become less complex and less prone to overfitting.

   Source: Adapted from community discussion — AjoseO

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7. Question 77 · 368 in bank

   A company is building a new version of a recommendation engine. Machine learning (ML) specialists need to keep adding new data from users to improve personalized recommendations. The ML specialists gather data from the users' interactions on the platform and from sources such as external websites and social media. The pipeline cleans, transforms, enriches, and compresses terabytes of data daily, and this data is stored in Amazon S3. A set of Python scripts was coded to do the job and is stored in a large Amazon EC2 instance. The whole process takes more than 20 hours to finish, with each script taking at least an hour. The company wants to move the scripts out of Amazon EC2 into a more managed solution that will eliminate the need to maintain servers. Which approach will address all of these requirements with the LEAST development effort?

   • ALoad the data into an Amazon Redshift cluster. Execute the pipeline by using SQL. Store the results in Amazon S3.
   • BLoad the data into Amazon DynamoDB. Convert the scripts to an AWS Lambda function. Execute the pipeline by triggering Lambda executions. Store the results in Amazon S3.
   • CCreate an AWS Glue job. Convert the scripts to PySpark. Execute the pipeline. Store the results in Amazon S3.
   • DCreate a set of individual AWS Lambda functions to execute each of the scripts. Build a step function by using the AWS Step Functions Data Science SDK. Store the results in Amazon S3.

   Show answer

   Answer: C

   Explanation

   The data pipeline involves cleaning, transforming, enriching, and compressing terabytes of data and storing the data in Amazon S3. AWS Glue is an ETL service that makes it easy to move data between data stores. The Glue job allows you to use PySpark scripts to perform ETL tasks. With AWS Glue, you do not need to provision and manage servers, which eliminates the need to maintain servers, as required by the company. Therefore, AWS Glue would address all of the company's requirements with the least development effort.

   Source: Adapted from community discussion — AjoseO

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8. Question 78 · 368 in bank

   An employee found a video clip with audio on a company's social media feed. The language used in the video is Spanish. English is the employee's first language, and they do not understand Spanish. The employee wants to do a sentiment analysis. What combination of services is the MOST efficient to accomplish the task?

   • AAmazon Transcribe, Amazon Translate, and Amazon Comprehend
   • BAmazon Transcribe, Amazon Comprehend, and Amazon SageMaker seq2seq
   • CAmazon Transcribe, Amazon Translate, and Amazon SageMaker Neural Topic Model (NTM)
   • DAmazon Transcribe, Amazon Translate and Amazon SageMaker BlazingText

   Show answer

   Answer: A

   Explanation

   the MOST efficient means to you don't need to coding, building infra All of sevices are manage by AWS is good, Transcribe, Amazon Translate, and Amazon Comprehend Answer is A

   Source: Adapted from community discussion — DonaldCMLIN

   References

   • AWS documentation
   • AWS documentation

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9. Question 79 · 368 in bank

   Select 3

   A retail company is selling products through a global online marketplace. The company wants to use machine learning (ML) to analyze customer feedback and identify specific areas for improvement. A developer has built a tool that collects customer reviews from the online marketplace and stores them in an Amazon S3 bucket. This process yields a dataset of 40 reviews. A data scientist building the ML models must identify additional sources of data to increase the size of the dataset. Which data sources should the data scientist use to augment the dataset of reviews? (Choose three.)

   • AEmails exchanged by customers and the company's customer service agents
   • BSocial media posts containing the name of the company or its products
   • CA publicly available collection of news articles
   • DA publicly available collection of customer reviews
   • EProduct sales revenue figures for the company
   • FInstruction manuals for the company's products

   Show answer

   Answer: ABD

   Explanation

   A: Emails exchanged by customers and the company's customer service agents can provide additional customer feedback and opinions about the products or services. This data can be used to improve the ML model. B: Social media posts containing the name of the company or its products can provide additional customer feedback and opinions about the products or services, which can be used to improve the ML model. D: A publicly available collection of customer reviews can be used to augment the existing dataset of reviews and increase the size of the dataset. This can help to improve the accuracy of the ML model.

   Source: Adapted from community discussion — AjoseO

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10. Question 80 · 368 in bank

    A machine learning (ML) specialist wants to create a data preparation job that uses a PySpark script with complex window aggregation operations to create data for training and testing. The ML specialist needs to evaluate the impact of the number of features and the sample count on model performance. Which approach should the ML specialist use to determine the ideal data transformations for the model?

    • AAdd an Amazon SageMaker Debugger hook to the script to capture key metrics. Run the script as an AWS Glue job.
    • BAdd an Amazon SageMaker Experiments tracker to the script to capture key metrics. Run the script as an AWS Glue job.
    • CAdd an Amazon SageMaker Debugger hook to the script to capture key parameters. Run the script as a SageMaker processing job.
    • DAdd an Amazon SageMaker Experiments tracker to the script to capture key parameters. Run the script as a SageMaker processing job.

    Show answer

    Answer: D

    Explanation

    while I agree that Sagemaker Experiments is the way to go, it only supports Training, Processing, and Transform jobs, so the right answer is to run the job as a processing job, hence D not B https://docs.aws.amazon.com/sagemaker/latest/dg/experiments-create.html#:~:text=CreateTrainingJob-,Processing,-Processor.run

    Source: Adapted from community discussion — dolorez

    References

    • AWS documentation
    • AWS documentation

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MLS-C01 practice, question 71. 368 questions available.