MLS-C01 practice questions 331–340

1. Question 331 · 368 in bank

   Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors. While exploring the data, the Specialist notices that the magnitude of the input features vary greatly. The Specialist does not want variables with a larger magnitude to dominate the model. What should the Specialist do to prepare the data for model training?

   • AApply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution.
   • BApply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude.
   • CApply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude.
   • DApply the orthogonal sparse bigram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.

   Show answer

   Answer: C

   Explanation

   Guys, I passed the exam today. It is a tough one but there are many questions here. Good luck everyone! Thank examtopics

   Source: Adapted from community discussion — Phong

   References

   • AWS documentation
   • AWS documentation

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2. Question 332 · 368 in bank

   A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena. The dataset contains more than 800,000 records stored as plaintext CSV files. Each record contains 200 columns and is approximately 1.5 MB in size. Most queries will span 5 to 10 columns only. How should the Machine Learning Specialist transform the dataset to minimize query runtime?

   • AConvert the records to Apache Parquet format.
   • BConvert the records to JSON format.
   • CConvert the records to GZIP CSV format.
   • DConvert the records to XML format.

   Show answer

   Answer: A

   Explanation

   clue is: most queries will span 5 to 10 column while there are 200 columns. Indicating Data Warehouse means columner storage. Option A is correct.

   Source: Adapted from community discussion — exam_prep

   References

   • AWS documentation
   • AWS documentation

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3. Question 333 · 368 in bank

   A Machine Learning Specialist is developing a daily ETL workflow containing multiple ETL jobs. The workflow consists of the following processes: * Start the workflow as soon as data is uploaded to Amazon S3. * When all the datasets are available in Amazon S3, start an ETL job to join the uploaded datasets with multiple terabyte-sized datasets already stored in Amazon S3. * Store the results of joining datasets in Amazon S3. * If one of the jobs fails, send a notification to the Administrator. Which configuration will meet these requirements?

   • AUse AWS Lambda to trigger an AWS Step Functions workflow to wait for dataset uploads to complete in Amazon S3. Use AWS Glue to join the datasets. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
   • BDevelop the ETL workflow using AWS Lambda to start an Amazon SageMaker notebook instance. Use a lifecycle configuration script to join the datasets and persist the results in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
   • CDevelop the ETL workflow using AWS Batch to trigger the start of ETL jobs when data is uploaded to Amazon S3. Use AWS Glue to join the datasets in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
   • DUse AWS Lambda to chain other Lambda functions to read and join the datasets in Amazon S3 as soon as the data is uploaded to Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.

   Show answer

   Answer: A

   Explanation

   Reference: https://aws.amazon.com/step-functions/use-cases/

   Source: Adapted from community discussion — HaiHN

   References

   • AWS documentation
   • AWS documentation

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4. Question 334 · 368 in bank

   Select 2

   An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen. Which combination of algorithms would provide the appropriate insights? (Choose two.)

   • AThe factorization machines (FM) algorithm
   • BThe Latent Dirichlet Allocation (LDA) algorithm
   • CThe principal component analysis (PCA) algorithm
   • DThe k-means algorithm
   • EThe Random Cut Forest (RCF) algorithm

   Show answer

   Answer: CD

   Explanation

   C: (OK) Use PCA for reducing number of variables. Each citizen's response should have answer for 500 questions, so it should have 500 variables D: (OK) Use K-means clustering A: (Not OK) Factorization Machines Algorithm is usually used for tasks dealing with high dimensional sparse datasets B: (Not OK) The Latent Dirichlet Allocation (LDA) algorithm should be used for task dealing topic modeling in NLP E: (Not OK) Random Cut Forest should be used for detecting anormal in data

   Source: Adapted from community discussion — HaiHN

   References

   • AWS documentation

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5. Question 335 · 368 in bank

   A large consumer goods manufacturer has the following products on sale: * 34 different toothpaste variants * 48 different toothbrush variants * 43 different mouthwash variants The entire sales history of all these products is available in Amazon S3. Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products. The company wants to predict the demand for a new product that will soon be launched. Which solution should a Machine Learning Specialist apply?

   • ATrain a custom ARIMA model to forecast demand for the new product.
   • BTrain an Amazon SageMaker DeepAR algorithm to forecast demand for the new product.
   • CTrain an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
   • DTrain a custom XGBoost model to forecast demand for the new product.

   Show answer

   Answer: B

   Explanation

   B https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html "...When your dataset contains hundreds of related time series, DeepAR outperforms the standard ARIMA and ETS methods. You can also use the trained model to generate forecasts for new time series that are similar to the ones it has been trained on."

   Source: Adapted from community discussion — HaiHN

   References

   • AWS documentation

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6. Question 336 · 368 in bank

   A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker. The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant. Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test?

   • AReview SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon QuickSight to visualize logs as they are being produced.
   • BGenerate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker.
   • CBuild custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the log data as it is generated by Amazon SageMaker.
   • DSend Amazon CloudWatch Logs that were generated by Amazon SageMaker to Amazon ES and use Kibana to query and visualize the log data.

   Show answer

   Answer: B

   Explanation

   the question is clear that the specialist is seeking for latency, memory utilization, and CPU utilization during the load test and the ideal answer for all of these is amazon cloud watch which give you all these metrics https://docs.aws.amazon.com/sagemaker/latest/dg/monitoring-cloudwatch.html

   Source: Adapted from community discussion — teka112233

   References

   • AWS documentation

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7. Question 337 · 368 in bank

   A Machine Learning Specialist uploads a dataset to an Amazon S3 bucket protected with server-side encryption using AWS KMS. How should the ML Specialist define the Amazon SageMaker notebook instance so it can read the same dataset from Amazon S3?

   • ADefine security group(s) to allow all HTTP inbound/outbound traffic and assign those security group(s) to the Amazon SageMaker notebook instance.
   • B׀¡onfigure the Amazon SageMaker notebook instance to have access to the VPC. Grant permission in the KMS key policy to the notebook's KMS role.
   • CAssign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset. Grant permission in the KMS key policy to that role.
   • DAssign the same KMS key used to encrypt data in Amazon S3 to the Amazon SageMaker notebook instance.

   Show answer

   Answer: C

   Explanation

   Should be C. "You don’t need to specify the AWS KMS key ID when you download an SSE-KMS-encrypted object from an S3 bucket. Instead, you need the permission to decrypt the AWS KMS key. When a user sends a GET request, Amazon S3 checks if the AWS Identity and Access Management (IAM) user or role that sent the request is authorized to decrypt the key associated with the object. If the IAM user or role belongs to the same AWS account as the key, then the permission to decrypt must be granted on the AWS KMS key’s policy." https://aws.amazon.com/premiumsupport/knowledge-center/decrypt-kms-encrypted-objects-s3/?nc1=h_ls

   Source: Adapted from community discussion — seanLu

   References

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

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8. Question 338 · 368 in bank

   A Data Scientist needs to migrate an existing on-premises ETL process to the cloud. The current process runs at regular time intervals and uses PySpark to combine and format multiple large data sources into a single consolidated output for downstream processing. The Data Scientist has been given the following requirements to the cloud solution: ✑ Combine multiple data sources. ✑ Reuse existing PySpark logic. ✑ Run the solution on the existing schedule. ✑ Minimize the number of servers that will need to be managed. Which architecture should the Data Scientist use to build this solution?

   • AWrite the raw data to Amazon S3. Schedule an AWS Lambda function to submit a Spark step to a persistent Amazon EMR cluster based on the existing schedule. Use the existing PySpark logic to run the ETL job on the EMR cluster. Output the results to a ג€processedג€ location in Amazon S3 that is accessible for downstream use.
   • BWrite the raw data to Amazon S3. Create an AWS Glue ETL job to perform the ETL processing against the input data. Write the ETL job in PySpark to leverage the existing logic. Create a new AWS Glue trigger to trigger the ETL job based on the existing schedule. Configure the output target of the ETL job to write to a ג€processedג€ location in Amazon S3 that is accessible for downstream use.
   • CWrite the raw data to Amazon S3. Schedule an AWS Lambda function to run on the existing schedule and process the input data from Amazon S3. Write the Lambda logic in Python and implement the existing PySpark logic to perform the ETL process. Have the Lambda function output the results to a ג€processedג€ location in Amazon S3 that is accessible for downstream use.
   • DUse Amazon Kinesis Data Analytics to stream the input data and perform real-time SQL queries against the stream to carry out the required transformations within the stream. Deliver the output results to a ג€processedג€ location in Amazon S3 that is accessible for downstream use.

   Show answer

   Answer: B

   Explanation

   A is not correct because Minimize the number of servers that will need to be managed. EMR is not server-less. B is correct. AWS Glue supports an extension of the PySpark Python dialect for scripting extract, transform, and load... C is not correct because using Lambda for ETL you will not be able to Reuse existing PySpark logic D is not correct because Kinesis is not server-less. And you can not Reuse existing PySpark logic

   Source: Adapted from community discussion — SophieSu

   References

   • AWS documentation
   • AWS documentation
   • AWS documentation

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9. Question 339 · 368 in bank

   Select 2

   A Data Scientist is building a model to predict customer churn using a dataset of 100 continuous numerical features. The Marketing team has not provided any insight about which features are relevant for churn prediction. The Marketing team wants to interpret the model and see the direct impact of relevant features on the model outcome. While training a logistic regression model, the Data Scientist observes that there is a wide gap between the training and validation set accuracy. Which methods can the Data Scientist use to improve the model performance and satisfy the Marketing team's needs? (Choose two.)

   • AAdd L1 regularization to the classifier
   • BAdd features to the dataset
   • CPerform recursive feature elimination
   • DPerform t-distributed stochastic neighbor embedding (t-SNE)
   • EPerform linear discriminant analysis

   Show answer

   Answer: AC

   Explanation

   AC - Key: logistic regression model = non linear in terms of Odds and Probability, however it is linear in terms of Log Odds. Key: Large gap between training & validation = overfitting => 5 techniques to prevent overfitting: 1. Simplifying the model | 2. Early stopping 3. Use data argumentation | 4. Use regularization | 5. Use dropouts A - yes to avoid overfitting (although i am thinking it is talking about regressor) Not B - add feature will lead to overfitting C - feature elimination - prevent overfitting Not D - t-SNE is a nonlinear dimensionality reduction technique Not E - find feature correlation only - Linear discriminant analysis (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics and other fields, to find a linear combination of features that characterizes or separates two or more classes of objects or events.

   Source: Adapted from community discussion — wisoxe8356

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10. Question 340 · 368 in bank

    An aircraft engine manufacturing company is measuring 200 performance metrics in a time-series. Engineers want to detect critical manufacturing defects in near- real time during testing. All of the data needs to be stored for offline analysis. What approach would be the MOST effective to perform near-real time defect detection?

    • AUse AWS IoT Analytics for ingestion, storage, and further analysis. Use Jupyter notebooks from within AWS IoT Analytics to carry out analysis for anomalies.
    • BUse Amazon S3 for ingestion, storage, and further analysis. Use an Amazon EMR cluster to carry out Apache Spark ML k-means clustering to determine anomalies.
    • CUse Amazon S3 for ingestion, storage, and further analysis. Use the Amazon SageMaker Random Cut Forest (RCF) algorithm to determine anomalies.
    • DUse Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random Cut Forest (RCF) to perform anomaly detection. Use Kinesis Data Firehose to store data in Amazon S3 for further analysis.

    Show answer

    Answer: D

    Explanation

    The main problem with D is that Amazon Kinesis Data Firehose can not be a source service for Amazon Kinesis Data Analytics. The answer would be correct if it said "Using Amazon Kinesis Data Stream to ingest data, using Amazon Kinesis Data Analytics for defect detection and using Amazon Kinesis Data Firehose for storing data for further Analysis" https://docs.aws.amazon.com/firehose/latest/dev/create-name.html

    Source: Adapted from community discussion — DimLam

    References

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

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MLS-C01 practice, question 331. 368 questions available.