MLS-C01 practice questions 91–100

1. Question 91 · 368 in bank

   A real-estate company is launching a new product that predicts the prices of new houses. The historical data for the properties and prices is stored in .csv format in an Amazon S3 bucket. The data has a header, some categorical fields, and some missing values. The company's data scientists have used Python with a common open-source library to fill the missing values with zeros. The data scientists have dropped all of the categorical fields and have trained a model by using the open-source linear regression algorithm with the default parameters. The accuracy of the predictions with the current model is below 50%. The company wants to improve the model performance and launch the new product as soon as possible. Which solution will meet these requirements with the LEAST operational overhead?

   • ACreate a service-linked role for Amazon Elastic Container Service (Amazon ECS) with access to the S3 bucket. Create an ECS cluster that is based on an AWS Deep Learning Containers image. Write the code to perform the feature engineering. Train a logistic regression model for predicting the price, pointing to the bucket with the dataset. Wait for the training job to complete. Perform the inferences.
   • BCreate an Amazon SageMaker notebook with a new IAM role that is associated with the notebook. Pull the dataset from the S3 bucket. Explore different combinations of feature engineering transformations, regression algorithms, and hyperparameters. Compare all the results in the notebook, and deploy the most accurate configuration in an endpoint for predictions.
   • CCreate an IAM role with access to Amazon S3, Amazon SageMaker, and AWS Lambda. Create a training job with the SageMaker built-in XGBoost model pointing to the bucket with the dataset. Specify the price as the target feature. Wait for the job to complete. Load the model artifact to a Lambda function for inference on prices of new houses.
   • DCreate an IAM role for Amazon SageMaker with access to the S3 bucket. Create a SageMaker AutoML job with SageMaker Autopilot pointing to the bucket with the dataset. Specify the price as the target attribute. Wait for the job to complete. Deploy the best model for predictions.

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   Answer: D

   Explanation

   The problem is not a classification problem so A is incorrect as logistic regression is used for binary problems. D is the correct solution

   Source: Adapted from community discussion — Morsa

   References

   • AWS documentation

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2. Question 92 · 368 in bank

   Select 2

   A data scientist is reviewing customer comments about a company's products. The data scientist needs to present an initial exploratory analysis by using charts and a word cloud. The data scientist must use feature engineering techniques to prepare this analysis before starting a natural language processing (NLP) model. Which combination of feature engineering techniques should the data scientist use to meet these requirements? (Choose two.)

   • ANamed entity recognition
   • BCoreference
   • CStemming
   • DTerm frequency-inverse document frequency (TF-IDF)
   • ESentiment analysis

   Show answer

   Answer: CD

   Explanation

   A. NO - it is categorization of words and thus inferencing, not pre-processing B. NO - Coreferencing (eg. linking "He" to "Mark" seen in a previous sentence) is a complex task, not pre-processing C. YES - it consists of reducing words to their base form to reduce dimensionality D. YES - fast pre-processing task E. NO - it is not feature engineering, it is training

   Source: Adapted from community discussion — loict

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3. Question 93 · 368 in bank

   A data scientist is evaluating a GluonTS on Amazon SageMaker DeepAR model. The evaluation metrics on the test set indicate that the coverage score is 0.489 and 0.889 at the 0.5 and 0.9 quantiles, respectively. What can the data scientist reasonably conclude about the distributional forecast related to the test set?

   • AThe coverage scores indicate that the distributional forecast is poorly calibrated. These scores should be approximately equal to each other at all quantiles.
   • BThe coverage scores indicate that the distributional forecast is poorly calibrated. These scores should peak at the median and be lower at the tails.
   • CThe coverage scores indicate that the distributional forecast is correctly calibrated. These scores should always fall below the quantile itself.
   • DThe coverage scores indicate that the distributional forecast is correctly calibrated. These scores should be approximately equal to the quantile itself.

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   Answer: D

   References

   • AWS documentation

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4. Question 94 · 368 in bank

   An energy company has wind turbines, weather stations, and solar panels that generate telemetry data. The company wants to perform predictive maintenance on these devices. The devices are in various locations and have unstable internet connectivity. A team of data scientists is using the telemetry data to perform machine learning (ML) to conduct anomaly detection and predict maintenance before the devices start to deteriorate. The team needs a scalable, secure, high-velocity data ingestion mechanism. The team has decided to use Amazon S3 as the data storage location. Which approach meets these requirements?

   • AIngest the data by using an HTTP API call to a web server that is hosted on Amazon EC2. Set up EC2 instances in an Auto Scaling configuration behind an Elastic Load Balancer to load the data into Amazon S3.
   • BIngest the data over Message Queuing Telemetry Transport (MQTT) to AWS IoT Core. Set up a rule in AWS IoT Core to use Amazon Kinesis Data Firehose to send data to an Amazon Kinesis data stream that is configured to write to an S3 bucket.
   • CIngest the data over Message Queuing Telemetry Transport (MQTT) to AWS IoT Core. Set up a rule in AWS IoT Core to direct all MQTT data to an Amazon Kinesis Data Firehose delivery stream that is configured to write to an S3 bucket.
   • DIngest the data over Message Queuing Telemetry Transport (MQTT) to Amazon Kinesis data stream that is configured to write to an S3 bucket.

   Show answer

   Answer: C

   References

   • AWS documentation

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5. Question 95 · 368 in bank

   A retail company collects customer comments about its products from social media, the company website, and customer call logs. A team of data scientists and engineers wants to find common topics and determine which products the customers are referring to in their comments. The team is using natural language processing (NLP) to build a model to help with this classification. Each product can be classified into multiple categories that the company defines. These categories are related but are not mutually exclusive. For example, if there is mention of "Sample Yogurt" in the document of customer comments, then "Sample Yogurt" should be classified as "yogurt," "snack," and "dairy product." The team is using Amazon Comprehend to train the model and must complete the project as soon as possible. Which functionality of Amazon Comprehend should the team use to meet these requirements?

   • ACustom classification with multi-class mode
   • BCustom classification with multi-label mode
   • CCustom entity recognition
   • DBuilt-in models

   Show answer

   Answer: B

   Explanation

   The answer is B. In multi-label mode, individual classes represent different categories, but these categories are not mutually exclusive while individual classes are mutually exclusive in multi-class mode

   Source: Adapted from community discussion — spaceexplorer

   References

   • AWS documentation

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6. Question 96 · 368 in bank

   Select 3

   A data engineer is using AWS Glue to create optimized, secure datasets in Amazon S3. The data science team wants the ability to access the ETL scripts directly from Amazon SageMaker notebooks within a VPC. After this setup is complete, the data science team wants the ability to run the AWS Glue job and invoke the SageMaker training job. Which combination of steps should the data engineer take to meet these requirements? (Choose three.)

   • ACreate a SageMaker development endpoint in the data science team's VPC.
   • BCreate an AWS Glue development endpoint in the data science team's VPC.
   • CCreate SageMaker notebooks by using the AWS Glue development endpoint.
   • DCreate SageMaker notebooks by using the SageMaker console.
   • EAttach a decryption policy to the SageMaker notebooks.
   • FCreate an IAM policy and an IAM role for the SageMaker notebooks.

   Show answer

   Answer: BCF

   Explanation

   In the AWS Glue console, choose Dev endpoints to navigate to the development endpoints list. Select the check box next to the name of a development endpoint that you want to use, and on the Action menu, choose Create SageMaker notebook. Fill out the Create and configure a notebook page as follows: Enter a notebook name. Under Attach to development endpoint, verify the development endpoint. Create or choose an AWS Identity and Access Management (IAM) role.

   Source: Adapted from community discussion — Chelseajcole

   References

   • AWS documentation

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7. Question 97 · 368 in bank

   A data engineer needs to provide a team of data scientists with the appropriate dataset to run machine learning training jobs. The data will be stored in Amazon S3. The data engineer is obtaining the data from an Amazon Redshift database and is using join queries to extract a single tabular dataset. A portion of the schema is as follows: TransactionTimestamp (Timestamp) CardName (Varchar) CardNo (Varchar) The data engineer must provide the data so that any row with a CardNo value of NULL is removed. Also, the TransactionTimestamp column must be separated into a TransactionDate column and a TransactionTime column. Finally, the CardName column must be renamed to NameOnCard. The data will be extracted on a monthly basis and will be loaded into an S3 bucket. The solution must minimize the effort that is needed to set up infrastructure for the ingestion and transformation. The solution also must be automated and must minimize the load on the Amazon Redshift cluster. Which solution meets these requirements?

   • ASet up an Amazon EMR cluster. Create an Apache Spark job to read the data from the Amazon Redshift cluster and transform the data. Load the data into the S3 bucket. Schedule the job to run monthly.
   • BSet up an Amazon EC2 instance with a SQL client tool, such as SQL Workbench/J, to query the data from the Amazon Redshift cluster directly Export the resulting dataset into a file. Upload the file into the S3 bucket. Perform these tasks monthly.
   • CSet up an AWS Glue job that has the Amazon Redshift cluster as the source and the S3 bucket as the destination. Use the built-in transforms Filter, Map, and RenameField to perform the required transformations. Schedule the job to run monthly.
   • DUse Amazon Redshift Spectrum to run a query that writes the data directly to the S3 bucket. Create an AWS Lambda function to run the query monthly.

   Show answer

   Answer: C

   Explanation

   The answer was between C and D, but we are suppose to minimize use of Redshift cluster, answer is C. And B are too much effort, so not to be done as per constraints of question.

   Source: Adapted from community discussion — akgarg00

   References

   • AWS documentation

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8. Question 98 · 368 in bank

   A machine learning (ML) specialist wants to bring a custom training algorithm to Amazon SageMaker. The ML specialist implements the algorithm in a Docker container that is supported by SageMaker. How should the ML specialist package the Docker container so that SageMaker can launch the training correctly?

   • ASpecify the server argument in the ENTRYPOINT instruction in the Dockerfile.
   • BSpecify the training program in the ENTRYPOINT instruction in the Dockerfile.
   • CInclude the path to the training data in the docker build command when packaging the container.
   • DUse a COPY instruction in the Dockerfile to copy the training program to the /opt/ml/train directory.

   Show answer

   Answer: B

   Explanation

   https://docs.aws.amazon.com/sagemaker/latest/dg/adapt-training-container.html In Step 2, it is mentioned to use this instruction on dockerfile: # Defines train.py as script entrypoint ENV SAGEMAKER_PROGRAM train.py

   Source: Adapted from community discussion — wjohnny

   References

   • AWS documentation
   • AWS documentation

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9. Question 99 · 368 in bank

   Select 2

   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.)

   • AIP Insights
   • BK-nearest neighbors (k-NN)
   • CLinear learner with a logistic function
   • DRandom Cut Forest (RCF)
   • EXGBoost

   Show answer

   Answer: AD

   Explanation

   IP Insights is an unsupervised learning algorithm that learns the usage patterns of IP addresses. It can capture associations between IP addresses and various entities, such as user IDs or account numbers. It can also identify anomalous events, such as a user attempting to log in from an unusual IP address, or an account that is creating resources from a suspicious IP address1. Random Cut Forest (RCF) is another unsupervised algorithm for detecting anomalous data points within a dataset. It can handle arbitrary-dimensional input and scale well with respect to number of features, data set size, and number of instances. It can detect anomalies such as unexpected spikes in time series data, breaks in periodicity, or unclassifiable data points2.

   Source: Adapted from community discussion — Mickey321

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10. Question 100 · 368 in bank

    A Machine Learning Specialist is building a logistic regression model that will predict whether or not a person will order a pizza. The Specialist is trying to build the optimal model with an ideal classification threshold. What model evaluation technique should the Specialist use to understand how different classification thresholds will impact the model's performance?

    • AReceiver operating characteristic (ROC) curve
    • BMisclassification rate
    • CRoot Mean Square Error (RMSE)
    • DL1 norm

    Show answer

    Answer: A

    Explanation

    Answer is A. An ROC curve (receiver operating characteristic curve) is a graph showing the performance of a classification model at all classification thresholds

    Source: Adapted from community discussion — AKT

    References

    • Google developer documentation

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MLS-C01 practice, question 91. 368 questions available.