Amazon MLS-C01 Online Practice Questions and Exam Preparation

Amazon MLS-C01 Online Questions & Answers

• Question 101:

  A data scientist obtains a tabular dataset that contains 150 correlated features with different ranges to build a regression model. The data scientist needs to achieve more efficient model training by implementing a solution that minimizes impact on the model's performance. The data scientist decides to perform a principal component analysis (PCA) preprocessing step to reduce the number of features to a smaller set of independent features before the data scientist uses the new features in the regression model.

  Which preprocessing step will meet these requirements?

  A. Use the Amazon SageMaker built-in algorithm for PCA on the dataset to transform the data
  B. Load the data into Amazon SageMaker Data Wrangler. Scale the data with a Min Max Scaler transformation step Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data.
  C. Reduce the dimensionality of the dataset by removing the features that have the highest correlation Load the data into Amazon SageMaker Data Wrangler Perform a Standard Scaler transformation step to scale the data Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data
  D. Reduce the dimensionality of the dataset by removing the features that have the lowest correlation. Load the data into Amazon SageMaker Data Wrangler. Perform a Min Max Scaler transformation step to scale the data. Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data.
  B. Load the data into Amazon SageMaker Data Wrangler. Scale the data with a Min Max Scaler transformation step Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data.

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  Explanation

  Principal component analysis (PCA) is a technique for reducing the dimensionality of datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance. PCA is useful when dealing with datasets that have a large number of correlated features. However, PCA is sensitive to the scale of the features, so it is important to standardize or normalize the data before applying PCA. Amazon SageMaker provides a built-in algorithm for PCA that can be used to transform the data into a lower- dimensional representation. Amazon SageMaker Data Wrangler is a tool that allows data scientists to visually explore, clean, and prepare data for machine learning. Data Wrangler provides various transformation steps that can be applied to the data, such as scaling, encoding, imputing, etc. Data Wrangler also integrates with SageMaker built-in algorithms, such as PCA, to enable feature engineering and dimensionality reduction. Therefore, option B is the correct answer, as it involves scaling the data with a Min Max Scaler transformation step, which rescales the data to a range of [0, 1], and then using the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data. Option A is incorrect, as it does not involve scaling the data before applying PCA, which can affect the results of the dimensionality reduction. Option C is incorrect, as it involves removing the features that have the highest correlation, which can lead to information loss and reduce the performance of the regression model. Option D is incorrect, as it involves removing the features that have the lowest correlation, which can also lead to information loss and reduce the performance of the regression model.

  References: Principal Component Analysis (PCA) - Amazon SageMaker Scale data with a Min Max Scaler - Amazon SageMaker Data Wrangler Use Amazon SageMaker built-in algorithms - Amazon SageMaker Data Wrangler

• Question 102:

  A company hosts a public web application on AWS. The application provides a user feedback feature that consists of free-text fields where users can submit text to provide feedback. The company receives a large amount of free-text user feedback from the online web application. The product managers at the company classify the feedback into a set of fixed categories including user interface issues, performance issues, new feature request, and chat issues for further actions by the company's engineering teams.

  A machine learning (ML) engineer at the company must automate the classification of new user feedback into these fixed categories by using Amazon SageMaker. A large set of accurate data is available from the historical user feedback that the product managers previously classified.

  Which solution should the ML engineer apply to perform multi-class text classification of the user feedback?

  A. Use the SageMaker Latent Dirichlet Allocation (LDA) algorithm.
  B. Use the SageMaker BlazingText algorithm.
  C. Use the SageMaker Neural Topic Model (NTM) algorithm.
  D. Use the SageMaker CatBoost algorithm.
  B. Use the SageMaker BlazingText algorithm.

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  Explanation

• Question 103:

  A data scientist is building a forecasting model for a retail company by using the most recent 5 years of sales records that are stored in a data warehouse. The dataset contains sales records for each of the company's stores across five commercial regions The data scientist creates a working dataset with StorelD. Region. Date, and Sales Amount as columns. The data scientist wants to analyze yearly average sales for each region. The scientist also wants to compare how each region performed compared to average sales across all commercial regions.

  Which visualization will help the data scientist better understand the data trend?

  A. Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each store. Create a bar plot, faceted by year, of average sales for each store. Add an extra bar in each facet to represent average sales.
  B. Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each store. Create a bar plot, colored by region and faceted by year, of average sales for each store. Add a horizontal line in each facet to represent average sales.
  C. Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region Create a bar plot of average sales for each region. Add an extra bar in each facet to represent average sales.
  D. Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region Create a bar plot, faceted by year, of average sales for each region Add a horizontal line in each facet to represent average sales.
  D. Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region Create a bar plot, faceted by year, of average sales for each region Add a horizontal line in each facet to represent average sales.

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  Explanation

  The best visualization for this task is to create a bar plot, faceted by year, of average sales for each region and add a horizontal line in each facet to represent average sales. This way, the data scientist can easily compare the yearly average sales for each region with the overall average sales and see the trends over time. The bar plot also allows the data scientist to see the relative performance of each region within each year and across years. The other options are less effective because they either do not show the yearly trends, do not show the overall average sales, or do not group the data by region.

  References: pandas.DataFrame.groupby -- pandas 2.1.4 documentation pandas.DataFrame.plot.bar -- pandas 2.1.4 documentation Matplotlib - Bar Plot - Online Tutorials Library

• Question 104:

  A machine learning specialist works for a fruit processing company and needs to build a system that categorizes apples into three types. The specialist has collected a dataset that contains 150 images for each type of apple and applied

  transfer learning on a neural network that was pretrained on ImageNet with this dataset.

  The company requires at least 85% accuracy to make use of the model.

  After an exhaustive grid search, the optimal hyperparameters produced the following:

  1.68% accuracy on the training set

  2.67% accuracy on the validation set

  What can the machine learning specialist do to improve the system's accuracy?

  A. Upload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPO feature to optimize the model's hyperparameters.
  B. Add more data to the training set and retrain the model using transfer learning to reduce the bias.
  C. Use a neural network model with more layers that are pretrained on ImageNet and apply transfer learning to increase the variance.
  D. Train a new model using the current neural network architecture.
  B. Add more data to the training set and retrain the model using transfer learning to reduce the bias.

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  Explanation

• Question 105:

  A digital media company wants to build a customer churn prediction model by using tabular data. The model should clearly indicate whether a customer will stop using the company's services. The company wants to clean the data because the data contains some empty fields, duplicate values, and rare values.

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

  A. Use SageMaker Canvas to automatically clean the data and to prepare a categorical model.
  B. Use SageMaker Data Wrangler to clean the data. Use the built-in SageMaker XGBoost algorithm to train a classification model.
  C. Use SageMaker Canvas automatic data cleaning and preparation tools. Use the built-in SageMaker XGBoost algorithm to train a regression model.
  D. Use SageMaker Data Wrangler to clean the data. Use the SageMaker Autopilot to train a regression model
  A. Use SageMaker Canvas to automatically clean the data and to prepare a categorical model.

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  Explanation

• Question 106:

  A Mobile Network Operator is building an analytics platform to analyze and optimize a company's operations using Amazon Athena and Amazon S3

  The source systems send data in CSV format in real lime The Data Engineering team wants to transform the data to the Apache Parquet format before storing it on Amazon S3. Which solution takes the LEAST effort to implement?

  A. Ingest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 to serialize data as Parquet
  B. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.
  C. Ingest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use Apache Spark to convert data into Parquet.
  D. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convert data into Parquet.
  B. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.

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  Explanation

  https://github.com/ecloudvalley/Building-a-Data-Lake-with-AWS-Glue-and-Amazon-S3

• Question 107:

  A data scientist receives a collection of insurance claim records. Each record includes a claim ID. the final outcome of the insurance claim, and the date of the final outcome.

  The final outcome of each claim is a selection from among 200 outcome categories. Some claim records include only partial information. However, incomplete claim records include only 3 or 4 outcome ...gones from among the 200 available outcome categories. The collection includes hundreds of records for each outcome category. The records are from the previous 3 years.

  The data scientist must create a solution to predict the number of claims that will be in each outcome category every month, several months in advance.

  Which solution will meet these requirements?

  A. Perform classification every month by using supervised learning of the 20X3 outcome categories based on claim contents.
  B. Perform reinforcement learning by using claim IDs and dates Instruct the insurance agents who submit the claim records to estimate the expected number of claims in each outcome category every month.
  C. Perform forecasting by using claim IDs and dates to identify the expected number ot claims in each outcome category every month.
  D. Perform classification by using supervised learning of the outcome categories for which partial information on claim contents is provided. Perform forecasting by using claim IDs and dates for all other outcome categories.
  C. Perform forecasting by using claim IDs and dates to identify the expected number ot claims in each outcome category every month.

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  Explanation

  The best solution for this scenario is to perform forecasting by using claim IDs and dates to identify the expected number of claims in each outcome category every month. This solution has the following advantages:

  It leverages the historical data of claim outcomes and dates to capture the temporal patterns and trends of the claims in each category1. It does not require the claim contents or any other features to make predictions, which simplifies the data

  preparation and reduces the impact of missing or incomplete data2. It can handle the high cardinality of the outcome categories, as forecasting models can output multiple values for each time point3. It can provide predictions for several months in advance, which is useful for planning and budgeting purposes4. The other solutions have the following drawbacks:

  A: Performing classification every month by using supervised learning of the 200 outcome categories based on claim contents is not suitable, because it assumes that the claim contents are available and complete for all the records, which is

  not the case in this scenario2. Moreover, classification models usually output a single label for each input, which is not adequate for predicting the number of claims in each category3. Additionally, classification models do not account for the

  temporal aspect of the data, which is important for forecasting1.

  B: Performing reinforcement learning by using claim IDs and dates and instructing the insurance agents who submit the claim records to estimate the expected number of claims in each outcome category every month is not feasible, because

  it requires a feedback loop between the model and the agents, which might not be available or reliable in this scenario5. Furthermore, reinforcement learning is more suitable for sequential decision making problems, where the model learns

  from its actions and rewards, rather than forecasting problems, where the model learns from historical data and outputs future values6.

  D: Performing classification by using supervised learning of the outcome categories for which partial information on claim contents is provided and performing forecasting by using claim IDs and dates for all other outcome categories is not

  optimal, because it combines two different methods that might not be consistent or compatible with each other7. Also, this solution suffers from the same limitations as solution A, such as the dependency on claim contents, the inability to

  handle multiple outputs, and the ignorance of temporal patterns123.

  References:

  1: Time Series Forecasting - Amazon SageMaker

  2: Handling Missing Data for Machine Learning | AWS Machine Learning Blog

  3: Forecasting vs Classification: What's the Difference? | DataRobot

  4: Amazon Forecast ?Time Series Forecasting Made Easy | AWS News Blog

  5: Reinforcement Learning - Amazon SageMaker

  6: What is Reinforcement Learning? The Complete Guide | Edureka

  7: Combining Machine Learning Models | by Will Koehrsen | Towards Data Science

• Question 108:

  A company distributes an online multiple-choice survey to several thousand people. Respondents to the survey can select multiple options for each question.

  A machine learning (ML) engineer needs to comprehensively represent every response from all respondents in a dataset. The ML engineer will use the dataset to train a logistic regression model.

  Which solution will meet these requirements?

  A. Perform one-hot encoding on every possible option for each question of the survey.
  B. Perform binning on all the answers each respondent selected for each question.
  C. Use Amazon Mechanical Turk to create categorical labels for each set of possible responses.
  D. Use Amazon Textract to create numeric features for each set of possible responses.
  A. Perform one-hot encoding on every possible option for each question of the survey.

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  Explanation

• Question 109:

  A data scientist is developing a pipeline to ingest streaming web traffic data. The data scientist needs to implement a process to identify unusual web traffic patterns as part of the pipeline. The patterns will be used downstream for alerting and

  incident response. The data scientist has access to unlabeled historic data to use, if needed.

  The solution needs to do the following:

  Calculate an anomaly score for each web traffic entry.

  Adapt unusual event identification to changing web patterns over time.

  Which approach should the data scientist implement to meet these requirements?

  A. Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker Random Cut Forest (RCF) built-in model. Use an Amazon Kinesis Data Stream to process the incoming web traffic data. Attach a preprocessing AWS Lambda function to perform data enrichment by calling the RCF model to calculate the anomaly score for each record.
  B. Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker built-in XGBoost model. Use an Amazon Kinesis Data Stream to process the incoming web traffic data. Attach a preprocessing AWS Lambda function to perform data enrichment by calling the XGBoost model to calculate the anomaly score for each record.
  C. Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an input source for Amazon Kinesis Data Analytics. Write a SQL query to run in real time against the streaming data with the k-Nearest Neighbors (kNN) SQL extension to calculate anomaly scores for each record using a tumbling window.
  D. Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an input source for Amazon Kinesis Data Analytics. Write a SQL query to run in real time against the streaming data with the Amazon Random Cut Forest (RCF) SQL extension to calculate anomaly scores for each record using a sliding window.
  D. Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an input source for Amazon Kinesis Data Analytics. Write a SQL query to run in real time against the streaming data with the Amazon Random Cut Forest (RCF) SQL extension to calculate anomaly scores for each record using a sliding window.

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  Explanation

  The algorithm starts developing the machine learning model using current records in the stream when you start the application. The algorithm does not use older records in the stream for machine learning, nor does it use statistics from previous executions of the application.

• Question 110:

  An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget. What should the Specialist do to meet these requirements?

  A. Create one-hot word encoding vectors.
  B. Produce a set of synonyms for every word using Amazon Mechanical Turk.
  C. Create word embedding factors that store edit distance with every other word.
  D. Download word embedding's pre-trained on a large corpus.
  D. Download word embedding's pre-trained on a large corpus.

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  Explanation

  Word embeddings are a type of dense representation of words, which encode semantic meaning in a vector form. These embeddings are typically pre-trained on a large corpus of text data, such as a large set of books, news articles, or web pages, and capture the context in which words are used. Word embeddings can be used as features for a nearest neighbor model, which can be used to find words used in similar contexts.

  Downloading pre-trained word embeddings is a good way to get started quickly and leverage the strengths of these representations, which have been optimized on a large amount of data. This is likely to result in more accurate and reliable features than other options like one-hot encoding, edit distance, or using Amazon Mechanical Turk to produce synonyms.