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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q95-Q100):

NEW QUESTION # 95
A retail company intends to use machine learning to categorize new products. A labeled dataset of current products was provided to the Data Science team. The dataset includes 1,200 products.
The labeled dataset has 15 features for each product such as title dimensions, weight, and price.
Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies.
Which model should be used for categorizing new products using the provided dataset for training?

• A. A DeepAR forecasting model based on a recurrent neural network (RNN)
• B. A deep convolutional neural network (CNN) with a softmax activation function for the last layer
• C. A regression forest where the number of trees is set equal to the number of product categories
• D. AnXGBoost model where the objective parameter is set to multi:softmax

Answer: D

Explanation:
A XGBoost multi class classification.
https://medium.com/@gabrielziegler3/multiclass-multilabel-classification-with-xgboost-
66195e4d9f2d
CNN is used for image classificaiton problems.

 

NEW QUESTION # 96
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 the age feature from the dataset and train the model using the rest of the features.
• B. Drop all records from the dataset where age has been set to 0.
• C. Use k-means clustering to handle missing features
• D. Replace the age field value for records with a value of 0 with the mean or median value from the dataset

Answer: B

Explanation:
Explanation

 

NEW QUESTION # 97
A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily.
The model accuracy js acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?

• A. Switch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals.
• B. Do not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training.
• C. Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals.
• D. Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.

Answer: C

Explanation:
To improve the training speed of a time-series forecasting model using TensorFlow, the Machine Learning Specialist should change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Horovod is a free and open-source software framework for distributed deep learning training using TensorFlow, Keras, PyTorch, and Apache MXNet1. Horovod can scale up to hundreds of GPUs with upwards of 90% scaling efficiency2. Horovod is easy to use, as it requires only a few lines of Python code to modify an existing training script2. Horovod is also portable, as it runs the same for TensorFlow, Keras, PyTorch, and MXNet; on premise, in the cloud, and on Apache Spark2.
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly3. Amazon SageMaker supports Horovod as a built-in distributed training framework, which means that the Machine Learning Specialist does not need to install or configure Horovod separately4. Amazon SageMaker also provides a number of features and tools to simplify and optimize the distributed training process, such as automatic scaling, debugging, profiling, and monitoring4. By using Amazon SageMaker, the Machine Learning Specialist can parallelize the training to as many machines as needed to achieve the business goals, while minimizing coding effort and infrastructure changes.
References:
1: Horovod (machine learning) - Wikipedia
2: Home - Horovod
3: Amazon SageMaker - Machine Learning Service - AWS
4: Use Horovod with Amazon SageMaker - Amazon SageMaker

 

NEW QUESTION # 98
A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements:
* Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.
* Support event-driven ETL pipelines.
* Provide a quick and easy way to understand metadata.
Which approach meets trfese requirements?

• A. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.
• B. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.
• C. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.
• D. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.

Answer: A

 

NEW QUESTION # 99
A retail chain has been ingesting purchasing records from its network of 20,000 stores to Amazon S3 using Amazon Kinesis Data Firehose To support training an improved machine learning model, training records will require new but simple transformations, and some attributes will be combined The model needs lo be retrained daily Given the large number of stores and the legacy data ingestion, which change will require the LEAST amount of development effort?

• A. Require that the stores to switch to capturing their data locally on AWS Storage Gateway for loading into Amazon S3 then use AWS Glue to do the transformation
• B. Spin up a fleet of Amazon EC2 instances with the transformation logic, have them transform the data records accumulating on Amazon S3, and output the transformed records to Amazon S3.
• C. Insert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehouse stream that transforms raw record attributes into simple transformed values using SQL.
• D. Deploy an Amazon EMR cluster running Apache Spark with the transformation logic, and have the cluster run each day on the accumulating records in Amazon S3, outputting new/transformed records to Amazon S3

Answer: C

Explanation:
Explanation
Amazon Kinesis Data Analytics is a service that can analyze streaming data in real time using SQL or Apache Flink applications. It can also use machine learning algorithms, such as Random Cut Forest (RCF), to perform anomaly detection on streaming data. By inserting a Kinesis Data Analytics stream downstream of the Kinesis Data Firehose stream, the retail chain can transform the raw record attributes into simple transformed values using SQL queries. This can be done without changing the existing data ingestion process or deploying additional resources. The transformed records can then be outputted to another Kinesis Data Firehose stream that delivers them to Amazon S3 for training the machine learning model. This approach will require the least amount of development effort, as it leverages the existing Kinesis Data Firehose stream and the built-in SQL capabilities of Kinesis Data Analytics.
References:
Amazon Kinesis Data Analytics - Amazon Web Services
Anomaly Detection with Amazon Kinesis Data Analytics - Amazon Web Services Amazon Kinesis Data Firehose - Amazon Web Services Amazon S3 - Amazon Web Services

 

NEW QUESTION # 100
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