Amazon Amazon Certifications MLS-C01 Questions & Answers

• Question 341:

  A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data

  available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.

  Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations

  appear normal compared to the rest of the sample population.

  How should the Data Scientist correct this issue?

  A. Drop all records from the dataset where age has been set to 0.

  B. Replace the age field value for records with a value of 0 with the mean or median value from the dataset.

  C. Drop the age feature from the dataset and train the model using the rest of the features.

  D. Use k-means clustering to handle missing features.

  Correct Answer: D

  Dropping the Age feature is a NOT ATOLL a good idea - as age plays a critical role in this disease as per the question Dropping 10% of data is NOT a good idea considering the fact that the number of observations is already low. The Mean or Median are a potential solutions But the question says that "Disease worsens after age 65 so there is a correlation between age and other symptoms related feature" So that means that using Unsupervised Learning we can make pretty good prediction of "Age" So the answer is D Use K-Means clustering

• Question 342:

  An Machine Learning Specialist discover the following statistics while experimenting on a model.

  

  What can the Specialist from the experiments?

  A. The model In Experiment 1 had a high variance error lhat was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal bias error in Experiment 1

  B. The model in Experiment 1 had a high bias error that was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal variance error in Experiment 1

  C. The model in Experiment 1 had a high bias error and a high variance error that were reduced in Experiment 3 by regularization Experiment 2 shows thai high bias cannot be reduced by increasing layers and neurons in the model

  D. The model in Experiment 1 had a high random noise error that was reduced in Expenment 3 by regularization Expenment 2 shows that random noise cannot be reduced by increasing layers and neurons in the model

  Correct Answer: C

• Question 343:

  While working on a neural network project, a Machine Learning Specialist discovers thai some features in the data have very high magnitude resulting in this data being weighted more in the cost function. What should the Specialist do to ensure better convergence during backpropagation?

  A. Dimensionality reduction

  B. Data normalization

  C. Model regulanzation

  D. Data augmentation for the minority class

  Correct Answer: D

• Question 344:

  A Data Science team is designing a dataset repository where it will store a large amount of training data commonly used in its machine learning models. As Data Scientists may create an arbitrary number of new datasets every day the

  solution has to scale automatically and be cost-effective. Also, it must be possible to explore the data using SQL.

  Which storage scheme is MOST adapted to this scenario?

  A. Store datasets as files in Amazon S3.

  B. Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance.

  C. Store datasets as tables in a multi-node Amazon Redshift cluster.

  D. Store datasets as global tables in Amazon DynamoDB.

  Correct Answer: A

  The most appropriate storage scheme for this scenario is option A: Store datasets as files in Amazon S3.

  Amazon S3 is a highly scalable and cost-effective object storage service that can store a large amount of data. S3 can scale automatically to accommodate a large number of datasets, making it a good option for storing the training data used in machine learning models. Additionally, S3 supports SQL querying through Amazon Athena or Amazon Redshift Spectrum, allowing data scientists to easily explore the data.

• Question 345:

  A Machine Learning Specialist is using Amazon SageMaker to host a model for a highly available customer-facing application.

  The Specialist has trained a new version of the model, validated it with historical data, and now wants to deploy it to production To limit any risk of a negative customer experience, the Specialist wants to be able to monitor the model and roll it

  back, if needed.

  What is the SIMPLEST approach with the LEAST risk to deploy the model and roll it back, if needed?

  A. Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by updating the client configuration. Revert traffic to the last version if the model does not perform as expected.

  B. Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by using a load balancer Revert traffic to the last version if the model does not perform as expected.

  C. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 5% of the traffic to the new variant. Revert traffic to the last version by resetting the weights if the model does not perform as expected.

  D. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 100% of the traffic to the new variant Revert traffic to the last version by resetting the weights if the model does not perform as expected.

  Correct Answer: C

• Question 346:

  A company has raw user and transaction data stored in AmazonS3 a MySQL database, and Amazon RedShift A Data Scientist needs to perform an analysis by joining the three datasets from Amazon S3, MySQL, and Amazon RedShift, and then calculating the average-of a few selected columns from the joined data

  Which AWS service should the Data Scientist use?

  A. Amazon Athena

  B. Amazon Redshift Spectrum

  C. AWS Glue

  D. Amazon QuickSight

  Correct Answer: A

• Question 347:

  A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target.

  What option can the Specialist use to determine whether it is overestimating or underestimating the target value?

  A. Root Mean Square Error (RMSE)

  B. Residual plots

  C. Area under the curve

  D. Confusion matrix

  Correct Answer: B

  https://docs.aws.amazon.com/machine-learning/latest/dg/regression-model-insights.html

• Question 348:

  A Data Scientist needs to create a serverless ingestion and analytics solution for high-velocity, real-time streaming data.

  The ingestion process must buffer and convert incoming records from JSON to a query-optimized, columnar format without data loss. The output datastore must be highly available, and Analysts must be able to run SQL queries against the data and connect to existing business intelligence dashboards.

  Which solution should the Data Scientist build to satisfy the requirements?

  A. Create a schema in the AWS Glue Data Catalog of the incoming data format. Use an Amazon Kinesis Data Firehose delivery stream to stream the data and transform the data to Apache Parquet or ORC format using the AWS Glue Data Catalog before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.

  B. Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and writes the data to a processed data location in Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.

  C. Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and inserts it into an Amazon RDS PostgreSQL database. Have the Analysts query and run dashboards from the RDS database.

  D. Use Amazon Kinesis Data Analytics to ingest the streaming data and perform real-time SQL queries to convert the records to Apache Parquet before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.

  Correct Answer: A

  Firehose does integrate with GLue data catalog and it also "Buffers" the data .

  "When Kinesis Data Firehose processes incoming events and converts the data to Parquet, it needs to know which schema to apply." This is achived by glue data catalog and athena and it works on real-time data ingest.See link below.

  https://aws.amazon.com/blogs/big-data/analyzing-apache-parquet-optimized-data-using-amazon-kinesis-data-firehose-amazon-athena-and-amazon-redshift/

• Question 349:

  A company is using Amazon Polly to translate plaintext documents to speech for automated company announcements However company acronyms are being mispronounced in the current documents How should a Machine Learning Specialist address this issue for future documents'?

  A. Convert current documents to SSML with pronunciation tags

  B. Create an appropriate pronunciation lexicon.

  C. Output speech marks to guide in pronunciation

  D. Use Amazon Lex to preprocess the text files for pronunciation

  Correct Answer: B

  SSML is specific to that particular doument, like W3C an be pronounced as "World Wide Web Consortium" using _W3C in that specific document and when you create a new document, you need to format again. But with LEXICONS, you can upload a lexicon file once and ALL the FUTURE documents can just have W3C and that will be pronounced as "World Wide Web Consortium".. so answer is B, because the question asks for "future" documents.

• Question 350:

  A Machine Learning Specialist is developing a daily ETL workflow containing multiple ETL jobs The workflow consists of the following processes

  1.

  Start the workflow as soon as data is uploaded to Amazon S3

  2.

  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

  3.

  Store the results of joining datasets in Amazon S3

  4.

  If one of the jobs fails, send a notification to the Administrator Which configuration will meet these requirements?

  A. Use 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

  B. Develop 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

  C. Develop 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

  D. Use 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

  Correct Answer: A

  https://docs.aws.amazon.com/step-functions/latest/dg/tutorial-cloudwatch-events-s3.html