Amazon MLS-C01 Online Practice Questions and Exam Preparation

Amazon MLS-C01 Online Questions & Answers

• Question 91:

  A machine learning (ML) specialist needs to extract embedding vectors from a text series. The goal is to provide a ready-to-ingest feature space for a data scientist to develop downstream ML predictive models. The text consists of curated sentences in English. Many sentences use similar words but in different contexts. There are questions and answers among the sentences, and the embedding space must differentiate between them.

  Which options can produce the required embedding vectors that capture word context and sequential QA information? (Choose two.)

  A. Amazon SageMaker seq2seq algorithm
  B. Amazon SageMaker BlazingText algorithm in Skip-gram mode
  C. Amazon SageMaker Object2Vec algorithm
  D. Amazon SageMaker BlazingText algorithm in continuous bag-of-words (CBOW) mode
  E. Combination of the Amazon SageMaker BlazingText algorithm in Batch Skip-gram mode with a custom recurrent neural network (RNN)
  A. Amazon SageMaker seq2seq algorithm
  C. Amazon SageMaker Object2Vec algorithm

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  Explanation

  https://aws.amazon.com/blogs/machine-learning/create-a-word-pronunciation-sequence-to-sequence-model-using-amazon-sagemaker/ https://docs.aws.amazon.com/sagemaker/latest/dg/object2vec.html

• Question 92:

  A Machine Learning Specialist is configuring automatic model tuning in Amazon SageMaker When using the hyperparameter optimization feature, which of the following guidelines should be followed to improve optimization? Choose the maximum number of hyperparameters supported by

  A. Amazon SageMaker to search the largest number of combinations possible
  B. Specify a very large hyperparameter range to allow Amazon SageMaker to cover every possible value.
  C. Use log-scaled hyperparameters to allow the hyperparameter space to be searched as quickly as possible
  D. Execute only one hyperparameter tuning job at a time and improve tuning through successive rounds of experiments
  C. Use log-scaled hyperparameters to allow the hyperparameter space to be searched as quickly as possible

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  Explanation

• Question 93:

  A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among 200 categories, and the date of the final outcome. Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance.

  What type of machine learning model should be used?

  A. Classification month-to-month using supervised learning of the 200 categories based on claim contents.
  B. Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.
  C. Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.
  D. Classification with supervised learning of the categories for which partial information on claim contents is provided, and forecasting using claim IDs and timestamps for all other categories.
  C. Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.

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  Explanation

• Question 94:

  A manufacturing company needs to identify returned smartphones that have been damaged by moisture. The company has an automated process that produces 2,000 diagnostic values for each phone. The database contains more than five million phone evaluations. The evaluation process is consistent, and there are no missing values in the data. A machine learning (ML) specialist has trained an Amazon SageMaker linear learner ML model to classify phones as moisture damaged or not moisture damaged by using all available features. The model's F1 score is 0.6. Which changes in model training would MOST likely improve the model's F1 score? (Choose two.)

  A. Continue to use the SageMaker linear learner algorithm. Reduce the number of features with the SageMaker principal component analysis (PCA) algorithm.
  B. Continue to use the SageMaker linear learner algorithm. Reduce the number of features with the scikit-learn multi-dimensional scaling (MDS) algorithm.
  C. Continue to use the SageMaker linear learner algorithm. Set the predictor type to regressor.
  D. Use the SageMaker k-means algorithm with k of less than 1,000 to train the model.
  E. Use the SageMaker k-nearest neighbors (k-NN) algorithm. Set a dimension reduction target of less than 1,000 to train the model.
  A. Continue to use the SageMaker linear learner algorithm. Reduce the number of features with the SageMaker principal component analysis (PCA) algorithm.
  E. Use the SageMaker k-nearest neighbors (k-NN) algorithm. Set a dimension reduction target of less than 1,000 to train the model.

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  Explanation

  https://scikit-learn.org/stable/auto_examples/neighbors/plot_nca_dim_reduction.html

• Question 95:

  A law firm handles thousands of contracts every day. Every contract must be signed. Currently, a lawyer manually checks all contracts for signatures.

  The law firm is developing a machine learning (ML) solution to automate signature detection for each contract. The ML solution must also provide a confidence score for each contract page.

  Which Amazon Textract API action can the law firm use to generate a confidence score for each page of each contract?

  A. Use the AnalyzeDocument API action. Set the FeatureTypes parameter to SIGNATURES. Return the confidence scores for each page.
  B. Use the Prediction API call on the documents. Return the signatures and confidence scores for each page.
  C. Use the StartDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page.
  D. Use the GetDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page.
  A. Use the AnalyzeDocument API action. Set the FeatureTypes parameter to SIGNATURES. Return the confidence scores for each page.

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  Explanation

• Question 96:

  A company wants to forecast the daily price of newly launched products based on 3 years of data for older product prices, sales, and rebates. The time-series data has irregular timestamps and is missing some values.

  Data scientist must build a dataset to replace the missing values. The data scientist needs a solution that resamptes the data daily and exports the data for further modeling.

  Which solution will meet these requirements with the LEAST implementation effort?

  A. Use Amazon EMR Serveriess with PySpark.
  B. Use AWS Glue DataBrew.
  C. Use Amazon SageMaker Studio Data Wrangler.
  D. Use Amazon SageMaker Studio Notebook with Pandas.
  C. Use Amazon SageMaker Studio Data Wrangler.

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  Explanation

  Amazon SageMaker Studio Data Wrangler is a visual data preparation tool that enables users to clean and normalize data without writing any code. Using Data Wrangler, the data scientist can easily import the time-series data from various sources, such as Amazon S3, Amazon Athena, or Amazon Redshift. Data Wrangler can automatically generate data insights and quality reports, which can help identify and fix missing values, outliers, and anomalies in the data. Data Wrangler also provides over 250 built-in transformations, such as resampling, interpolation, aggregation, and filtering, which can be applied to the data with a point-and-click interface. Data Wrangler can also export the prepared data to different destinations, such as Amazon S3, Amazon SageMaker Feature Store, or Amazon SageMaker Pipelines, for further modeling and analysis. Data Wrangler is integrated with Amazon SageMaker Studio, a web- based IDE for machine learning, which makes it easy to access and use the tool. Data Wrangler is a serverless and fully managed service, which means the data scientist does not need to provision, configure, or manage any infrastructure or clusters. Option A is incorrect because Amazon EMR Serverless is a serverless option for running big data analytics applications using open-source frameworks, such as Apache Spark. However, using Amazon EMR Serverless would require the data scientist to write PySpark code to perform the data preparation tasks, such as resampling, imputation, and aggregation. This would require more implementation effort than using Data Wrangler, which provides a visual and code-free interface for data preparation. Option B is incorrect because AWS Glue DataBrew is another visual data preparation tool that can be used to clean and normalize data without writing code. However, DataBrew does not support time-series data as a data type, and does not provide built-in transformations for resampling, interpolation, or aggregation of time-series data. Therefore, using DataBrew would not meet the requirements of the use case. Option D is incorrect because using Amazon SageMaker Studio Notebook with Pandas would also require the data scientist to write Python code to perform the data preparation tasks. Pandas is a popular Python library for data analysis and manipulation, which supports time-series data and provides various methods for resampling, interpolation, and aggregation. However, using Pandas would require more implementation effort than using Data Wrangler, which provides a visual and code-free interface for data preparation.

  References:

  1: Amazon SageMaker Data Wrangler documentation

  2: Amazon EMR Serverless documentation

  3: AWS Glue DataBrew documentation

  4: Pandas documentation

• Question 97:

  A bank's Machine Learning team is developing an approach for credit card fraud detection The company has a large dataset of historical data labeled as fraudulent The goal is to build a model to take the information from new transactions and predict whether each transaction is fraudulent or not.

  Which built-in Amazon SageMaker machine learning algorithm should be used for modeling this problem?

  A. Seq2seq
  B. XGBoost
  C. K-means
  D. Random Cut Forest (RCF)
  D. Random Cut Forest (RCF)

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  Explanation

• Question 98:

  An automotive company uses computer vision in its autonomous cars. The company trained its object detection models successfully by using transfer learning from a convolutional neural network (CNN). The company trained the models by using PyTorch through the Amazon SageMaker SDK.

  The vehicles have limited hardware and compute power. The company wants to optimize the model to reduce memory, battery, and hardware consumption without a significant sacrifice in accuracy.

  Which solution will improve the computational efficiency of the models?

  A. Use Amazon CloudWatch metrics to gain visibility into the SageMaker training weights, gradients, biases, and activation outputs. Compute the filter ranks based on the training information. Apply pruning to remove the low-ranking filters. Set new weights based on the pruned set of filters. Run a new training job with the pruned model.
  B. Use Amazon SageMaker Ground Truth to build and run data labeling workflows. Collect a larger labeled dataset with the labelling workflows. Run a new training job that uses the new labeled data with previous training data.
  C. Use Amazon SageMaker Debugger to gain visibility into the training weights, gradients, biases, and activation outputs. Compute the filter ranks based on the training information. Apply pruning to remove the low-ranking filters. Set the new weights based on the pruned set of filters. Run a new training job with the pruned model.
  D. Use Amazon SageMaker Model Monitor to gain visibility into the ModelLatency metric and OverheadLatency metric of the model after the company deploys the model. Increase the model learning rate. Run a new training job.
  C. Use Amazon SageMaker Debugger to gain visibility into the training weights, gradients, biases, and activation outputs. Compute the filter ranks based on the training information. Apply pruning to remove the low-ranking filters. Set the new weights based on the pruned set of filters. Run a new training job with the pruned model.

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  Explanation

• Question 99:

  An ecommerce company wants to use machine learning (ML) to monitor fraudulent transactions on its website. The company is using Amazon SageMaker to research, train, deploy, and monitor the ML models.

  The historical transactions data is in a .csv file that is stored in Amazon S3. The data contains features such as the user's IP address, navigation time, average time on each page, and the number of clicks for each session. There is no label in

  the data to indicate if a transaction is anomalous.

  Which models should the company use in combination to detect anomalous transactions? (Choose two.)

  A. IP Insights
  B. K-nearest neighbors (k-NN)
  C. Linear learner with a logistic function
  D. Random Cut Forest (RCF)
  E. XGBoost
  A. IP Insights
  D. Random Cut Forest (RCF)

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  Explanation

• Question 100:

  A company wants to segment a large group of customers into subgroups based on shared characteristics. The company's data scientist is planning to use the Amazon SageMaker built-in k-means clustering algorithm for this task. The data scientist needs to determine the optimal number of subgroups (k) to use.

  Which data visualization approach will MOST accurately determine the optimal value of k?

  A. Calculate the principal component analysis (PCA) components. Run the k-means clustering algorithm for a range of k by using only the first two PCA components. For each value of k, create a scatter plot with a different color for each cluster. The optimal value of k is the value where the clusters start to look reasonably separated.
  B. Calculate the principal component analysis (PCA) components. Create a line plot of the number of components against the explained variance. The optimal value of k is the number of PCA components after which the curve starts decreasing in a linear fashion.
  C. Create a t-distributed stochastic neighbor embedding (t-SNE) plot for a range of perplexity values. The optimal value of k is the value of perplexity, where the clusters start to look reasonably separated.
  D. Run the k-means clustering algorithm for a range of k. For each value of k, calculate the sum of squared errors (SSE). Plot a line chart of the SSE for each value of k. The optimal value of k is the point after which the curve starts decreasing in a linear fashion.
  D. Run the k-means clustering algorithm for a range of k. For each value of k, calculate the sum of squared errors (SSE). Plot a line chart of the SSE for each value of k. The optimal value of k is the point after which the curve starts decreasing in a linear fashion.

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  Explanation