Amazon Amazon Certifications MLS-C01 Questions & Answers

• Question 371:

  A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.

  The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:

  1.

  Real-time analytics

  2.

  Interactive analytics of historical data

  3.

  Clickstream analytics

  4.

  Product recommendations

  Which services should the Specialist use?

  A. AWS Glue as the data dialog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations

  B. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-realtime data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized product recommendations

  C. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations

  D. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations

  Correct Answer: A

  AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to move data between data stores. It can be used as a data catalog to store metadata information about the data in the data lake. Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics can be used together to collect, process, and analyze real-time streaming data. Amazon Kinesis Data Firehose can be used to deliver streaming data to destinations such as Amazon ES for clickstream analytics. Finally, Amazon EMR can be used to run big data frameworks such as Apache Spark and Apache Hadoop to generate personalized product recommendations.

• Question 372:

  A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.

  

  Based on this information, which model would have the HIGHEST accuracy?

  A. Long short-term memory (LSTM) model with scaled exponential linear unit (SELL))

  B. Logistic regression

  C. Support vector machine (SVM) with non-linear kernel

  D. Single perceptron with tanh activation function

  Correct Answer: B

  Reference: https://machinelearningmastery.com/logistic-regression-for-machine-learning/

• Question 373:

  A manufacturing company has a large set of labeled historical sales data The manufacturer would like to predict how many units of a particular part should be produced each quarter Which machine learning approach should be used to solve this problem?

  A. Logistic regression

  B. Random Cut Forest (RCF)

  C. Principal component analysis (PCA)

  D. Linear regression

  Correct Answer: D

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

• Question 374:

  A monitoring service generates 1 TB of scale metrics record data every minute A Research team performs queries on this data using Amazon Athena The queries run slowly due to the large volume of data, and the team requires better performance

  How should the records be stored in Amazon S3 to improve query performance?

  A. CSV files

  B. Parquet files

  C. Compressed JSON

  D. RecordIO

  Correct Answer: B

  https://aws.amazon.com/tw/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/

• Question 375:

  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?

  A. Listwise deletion

  B. Last observation carried forward

  C. Multiple imputation

  D. Mean substitution

  Correct Answer: C

  Reference: https://worldwidescience.org/topicpages/i/imputing+missing+values.html

• Question 376:

  A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket A Machine Learning Specialist wants to use SQL to run queries on this data. Which solution requires the LEAST effort to be able to query this data?

  A. Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.

  B. Use AWS Glue to catalogue the data and Amazon Athena to run queries

  C. Use AWS Batch to run ETL on the data and Amazon Aurora to run the quenes

  D. Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries

  Correct Answer: B

  AWS Glue is a fully managed ETL service that makes it easy to move data between data stores. It can automatically crawl, catalogue, and classify data stored in Amazon S3, and make it available for querying and analysis. With AWS Glue,

  you don't have to worry about the underlying infrastructure and can focus on your data.

  Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. It integrates with AWS Glue, so you can use the catalogued data directly in Athena without any additional data movement

  or transformation.

• Question 377:

  A Machine Learning Specialist is preparing data for training on Amazon SageMaker The Specialist is transformed into a numpy .array, which appears to be negatively affecting the speed of the training

  What should the Specialist do to optimize the data for training on SageMaker'?

  A. Use the SageMaker batch transform feature to transform the training data into a DataFrame

  B. Use AWS Glue to compress the data into the Apache Parquet format

  C. Transform the dataset into the Recordio protobuf format

  D. Use the SageMaker hyperparameter optimization feature to automatically optimize the data

  Correct Answer: C

  Most Amazon SageMaker algorithms work best when you use the optimized protobuf recordIO format for the training data. https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-training.html

• Question 378:

  The Chief Editor for a product catalog wants the Research and Development team to build a machine learning system that can be used to detect whether or not individuals in a collection of images are wearing the company's retail brand The team has a set of training data.

  Which machine learning algorithm should the researchers use that BEST meets their requirements?

  A. Latent Dirichlet Allocation (LDA)

  B. Recurrent neural network (RNN)

  C. K-means

  D. Convolutional neural network (CNN)

  Correct Answer: D

• Question 379:

  A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat. The ML Specialist performs some tests and records the following results for a neural network-based image classifier:

  Total number of images available = 1,000 Test set images = 100 (constant test set)

  The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.

  Which techniques can be used by the ML Specialist to improve this specific test error?

  A. Increase the training data by adding variation in rotation for training images.

  B. Increase the number of epochs for model training.

  C. Increase the number of layers for the neural network.

  D. Increase the dropout rate for the second-to-last layer.

  Correct Answer: A

  One of the reasons for poor accuracy could be insufficient data. This can be overcome by image augmentation.

  Image augmentation is a technique of increasing the dataset size by processing (mirroring, flipping, rotating, increasing/decreasing brightness, contrast, color) the images.

  https://medium.com/datadriveninvestor/auto-model-tuning-for-keras-on-amazon-sagemaker-plant-seedling-dataset-7b591334501e

• Question 380:

  When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Select THREE.)

  A. The training channel identifying the location of training data on an Amazon S3 bucket.

  B. The validation channel identifying the location of validation data on an Amazon S3 bucket.

  C. The 1AM role that Amazon SageMaker can assume to perform tasks on behalf of the users.

  D. Hyperparameters in a JSON array as documented for the algorithm used.

  E. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.

  F. The output path specifying where on an Amazon S3 bucket the trained model will persist.

  Correct Answer: CEF

  https://docs.aws.amazon.com/zh_tw/sagemaker/latest/dg/API_CreateTrainingJob.html .. the only "Required: Yes" attributes are:

  1.

  AlgorithmSpecification (in this TrainingInputMode is Required - i.e. File or Pipe)

  2.

  OutputDataConfig (in this S3OutputPath is Required - where the model artifacts are stored)

  3.

  ResourceConfig (in this EC2 InstanceType and VolumeSizeInGB are required)

  4.

  RoleArn (..The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf...the caller of this API must have the iam:PassRole permission.)

  5.

  StoppingCondition

  6.

  TrainingJobName (The name of the training job. The name must be unique within an AWS Region in an AWS account.)