MLS-C01 practice questions 301–310

1. Question 301 · 368 in bank

   Select 2

   A company is observing low accuracy while training on the default built-in image classification algorithm in Amazon SageMaker. The Data Science team wants to use an Inception neural network architecture instead of a ResNet architecture. Which of the following will accomplish this? (Choose two.)

   • ACustomize the built-in image classification algorithm to use Inception and use this for model training.
   • BCreate a support case with the SageMaker team to change the default image classification algorithm to Inception.
   • CBundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training.
   • DUse custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network, and use this for model training.
   • EDownload and apt-get install the inception network code into an Amazon EC2 instance and use this instance as a Jupyter notebook in Amazon SageMaker.

   Show answer

   Answer: CD

   Explanation

   You might be spent a lot of money for ask AWS A.CHANGE built-in image OR B.Create a support case. The effectial way BOTH RELATIVE TO SageMaker Estimator C.DOCKER OR BRING YOUR CODE BY D.SageMaker with TensorFlow Estimator THE BEAUTYFUL ANSWER ARE C AND D

   Source: Adapted from community discussion — DonaldCMLIN

   References

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

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2. Question 302 · 368 in bank

   A Machine Learning Specialist built an image classification deep learning model. However, the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%, respectively. How should the Specialist address this issue and what is the reason behind it?

   • AThe learning rate should be increased because the optimization process was trapped at a local minimum.
   • BThe dropout rate at the flatten layer should be increased because the model is not generalized enough.
   • CThe dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
   • DThe epoch number should be increased because the optimization process was terminated before it reached the global minimum.

   Show answer

   Answer: B

   Explanation

   NOT D. Although increase epoch could improve overfitting, but not in status of early stopping B.INCREATE DROPOUT FOR not generalize well https://developer.ibm.com/articles/image-recognition-challenge-with-tensorflow-and-keras-pt2/

   Source: Adapted from community discussion — DonaldCMLIN

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3. Question 303 · 368 in bank

   A city wants to monitor its air quality to address the consequences of air pollution. A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city. As this is a prototype, only daily data from the last year is available. Which model is MOST likely to provide the best results in Amazon SageMaker?

   • AUse the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.
   • BUse Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.
   • CUse the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.
   • DUse the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of classifier.

   Show answer

   Answer: C

   Explanation

   The reason for this choice is that the Linear Learner algorithm is a versatile algorithm that can be used for both regression and classification tasks1. Regression is a type of supervised learning that predicts a continuous numeric value, such as the air quality in parts per million2. The predictor_type parameter specifies whether the algorithm should perform regression or classification3. Since the goal is to forecast a numeric value, the predictor_type should be set to regressor.

   Source: Adapted from community discussion — Mickey321

   References

   • AWS documentation

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4. Question 304 · 368 in bank

   A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting. Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls. What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?

   • AImplement an AWS Lambda function to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
   • BUse AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
   • CImplement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrail. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
   • DUse AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Set up Amazon SNS to receive a notification when the model is overfitting

   Show answer

   Answer: B

   Explanation

   THE ANSWER SHOULD BE B. YOU DON'T NEED TO THROUGH LAMBDA TO INTERGE CLOUDTRAIL Log Amazon SageMaker API Calls with AWS CloudTrail https://docs.aws.amazon.com/sagemaker/latest/dg/logging-using-cloudtrail.html

   Source: Adapted from community discussion — DonaldCMLIN

   References

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

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5. Question 305 · 368 in bank

   A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression. During exploratory data analysis, the Specialist observes that many features are highly correlated with each other. This may make the model unstable. What should be done to reduce the impact of having such a large number of features?

   • APerform one-hot encoding on highly correlated features.
   • BUse matrix multiplication on highly correlated features.
   • CCreate a new feature space using principal component analysis (PCA)
   • DApply the Pearson correlation coefficient.

   Show answer

   Answer: C

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6. Question 306 · 368 in bank

   A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes. Which prior probability distribution should the ML Specialist use for this variable?

   • APoisson distribution
   • BUniform distribution
   • CNormal distribution
   • DBinomial distribution

   Show answer

   Answer: A

   Explanation

   A If you have information about the average (mean) number of things that happen in some given time period / interval,Poisson distribution can give you a way to predict the odds of getting some other value on a given future day

   Source: Adapted from community discussion — ComPah

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7. Question 307 · 368 in bank

   A Data Science team within a large company uses Amazon SageMaker notebooks to access data stored in Amazon S3 buckets. The IT Security team is concerned that internet-enabled notebook instances create a security vulnerability where malicious code running on the instances could compromise data privacy. The company mandates that all instances stay within a secured VPC with no internet access, and data communication traffic must stay within the AWS network. How should the Data Science team configure the notebook instance placement to meet these requirements?

   • AAssociate the Amazon SageMaker notebook with a private subnet in a VPC. Place the Amazon SageMaker endpoint and S3 buckets within the same VPC.
   • BAssociate the Amazon SageMaker notebook with a private subnet in a VPC. Use IAM policies to grant access to Amazon S3 and Amazon SageMaker.
   • CAssociate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it.
   • DAssociate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has a NAT gateway and an associated security group allowing only outbound connections to Amazon S3 and Amazon SageMaker.

   Show answer

   Answer: C

   Explanation

   NAT gateway COULD GO OUT TO THE INTERNET AND DOWNLOAD BACK MALICIOUS D. IS NOT A GOOD ANSWER. THE SAFE ONE IS ANSWER C. ASSOCIATE WITH VPC_ENDPOINT AND S3_ENDPOINT

   Source: Adapted from community discussion — DonaldCMLIN

   References

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

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8. Question 308 · 368 in bank

   Select 3

   A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data. Which of the following methods should the Specialist consider using to correct this? (Choose three.)

   • ADecrease regularization.
   • BIncrease regularization.
   • CIncrease dropout.
   • DDecrease dropout.
   • EIncrease feature combinations.
   • FDecrease feature combinations.

   Show answer

   Answer: BCF

   Explanation

   Early Stopping L1(Lasso) L2(Ridge) Drop Out BY FOLLOWING BELOW TWO ARTCLES OF https://docs.aws.amazon.com/machine-learning/latest/dg/model-fit-underfitting-vs-overfitting.html https://docs.aws.amazon.com/zh_tw/sagemaker/latest/dg/automatic-model-tuning-early-stopping.html A.REGULATION INCREASE C.DROP-OUT INCREASE F.FEATURE DECREASE

   Source: Adapted from community discussion — DonaldCMLIN

   References

   • AWS documentation
   • AWS documentation

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9. Question 309 · 368 in bank

   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?

   • ACreate 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 BI tools using the Athena Java Database Connectivity (JDBC) connector.
   • BWrite 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 BI tools using the Athena Java Database Connectivity (JDBC) connector.
   • CWrite 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.
   • DUse 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 BI tools using the Athena Java Database Connectivity (JDBC) connector.

   Show answer

   Answer: A

   Explanation

   Kinesis Data Analytics NO PARQET FORMAT, BESIDES THAT JSON NO NEED TO STORE IN S3. RDS ISN'T serverless ingestion and analytics solution ANSWER IS A.

   Source: Adapted from community discussion — DonaldCMLIN

   References

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

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10. Question 310 · 368 in bank

    An online reseller has a large, multi-column dataset with one column missing 30% of its data. A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data. Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?

    • AListwise deletion
    • BLast observation carried forward
    • CMultiple imputation
    • DMean substitution

    Show answer

    Answer: C

    Explanation

    Multiple imputation is a statistical technique for handling missing data that involves generating multiple versions of the dataset with missing values filled in, and then combining the results to produce a single, complete dataset. This approach takes into account the relationship between variables in the dataset, and uses statistical models to predict missing values based on the information in other columns. This helps to preserve the integrity of the dataset by avoiding the introduction of bias or systematic error into the results.

    Source: Adapted from community discussion — AjoseO

    References

    • AWS documentation

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MLS-C01 practice, question 301. 368 questions available.