AWS-Certified-Machine-Learning-Specialty Free Practice Test

A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences and trends to enhance the website for better service and smart recommendations.
Which solution should the Specialist recommend?

1. A. Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in thecustomer database.
2. B. A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database
3. C. Collaborative filtering based on user interactions and correlations to identify patterns in the customer database
4. D. Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database

Correct Answer: C

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?

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

Correct Answer: A

The displayed graph is from a foresting model for testing a time series.

Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?

1. A. The model predicts both the trend and the seasonality well.
2. B. The model predicts the trend well, but not the seasonality.
3. C. The model predicts the seasonality well, but not the trend.
4. D. The model does not predict the trend or the seasonality well.

Correct Answer: D

A data scientist has developed a machine learning translation model for English to Japanese by using Amazon SageMaker's built-in seq2seq algorithm with 500,000 aligned sentence pairs. While testing with sample sentences, the data scientist finds that the translation quality is reasonable for an example as short as five words. However, the quality becomes unacceptable if the sentence is 100 words long.
Which action will resolve the problem?

1. A. Change preprocessing to use n-grams.
2. B. Add more nodes to the recurrent neural network (RNN) than the largest sentence's word count.
3. C. Adjust hyperparameters related to the attention mechanism.
4. D. Choose a different weight initialization type.

Correct Answer: C
https://docs.aws.amazon.com/sagemaker/latest/dg/seq-2-seq-howitworks.html

A Machine Learning Specialist is working for an online retailer that wants to run analytics on every customer visit, processed through a machine learning pipeline. The data needs to be ingested by Amazon Kinesis Data Streams at up to 100 transactions per second, and the JSON data blob is 100 KB in size.
What is the MINIMUM number of shards in Kinesis Data Streams the Specialist should use to successfully ingest this data?

1. A. 1 shards
2. B. 10 shards
3. C. 100 shards
4. D. 1,000 shards

Correct Answer: B