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Mock AWS-Certified-Machine-Learning-Specialty Exams - How to Prepare for Amazon AWS-Certified-Machine-Learning-Specialty: AWS Certified Machine Learning - Specialty
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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q315-Q320):
NEW QUESTION # 315
An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget.
What should the Specialist do to meet these requirements?
- A. Create word embedding factors that store edit distance with every other word.
- B. Produce a set of synonyms for every word using Amazon Mechanical Turk.
- C. Create one-hot word encoding vectors.
- D. Download word embedding's pre-trained on a large corpus.
Answer: D
Explanation:
Word embeddings are a type of dense representation of words, which encode semantic meaning in a vector form. These embeddings are typically pre-trained on a large corpus of text data, such as a large set of books, news articles, or web pages, and capture the context in which words are used. Word embeddings can be used as features for a nearest neighbor model, which can be used to find words used in similar contexts.
Downloading pre-trained word embeddings is a good way to get started quickly and leverage the strengths of these representations, which have been optimized on a large amount of data. This is likely to result in more accurate and reliable features than other options like one-hot encoding, edit distance, or using Amazon Mechanical Turk to produce synonyms.
NEW QUESTION # 316
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. AnXGBoost model where the objective parameter is set to multi:softmax
- B. A DeepAR forecasting model based on a recurrent neural network (RNN)
- C. A deep convolutional neural network (CNN) with a softmax activation function for the last layer
- D. A regression forest where the number of trees is set equal to the number of product categories
Answer: A
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 # 317
Given the following confusion matrix for a movie classification model, what is the true class frequency for Romance and the predicted class frequency for Adventure?
- A. The true class frequency for Romance is 77.56% * 0.78 and the predicted class frequency for Adventure is
20.85%*0.32 - B. The true class frequency for Romance is 0.78 and the predicted class frequency for Adventure is (0.47-
0.32) - C. The true class frequency for Romance is 77.56% and the predicted class frequency for Adventure is
20.85% - D. The true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is
13.12%
Answer: D
NEW QUESTION # 318
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 to 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. 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.
- B. 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.
- C. 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.
- D. Insert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehose stream that transforms raw record attributes into simple transformed values using SQL.
Answer: D
Explanation:
Explanation
NEW QUESTION # 319
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. 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.
- B. 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
- 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. 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
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 # 320
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