Question # 1
Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?
| A. Recall
| B. Misclassification rate
| C. Mean absolute percentage error (MAPE)
| D. Area Under the ROC Curve (AUC)
|
D. Area Under the ROC Curve (AUC)
Explanation:
Area Under the ROC Curve (AUC) is a metric that measures the performance of a binary classifier across all possible thresholds. It is also known as the probability that a randomly chosen positive example will be ranked higher than a randomly chosen negative example by the classifier. AUC is a good metric to compare different classification models because it is independent of the class distribution and the decision threshold. It also captures both the sensitivity (true positive rate) and the specificity (true negative rate) of the model.
References:
• AWS Machine Learning Specialty Exam Guide
• AWS Machine Learning Specialty Sample Questions
Question # 2
A Machine Learning Specialist is deciding between building a naive Bayesian model or a full Bayesian network for a classification problem. The Specialist computes the Pearson correlation coefficients between each feature and finds that their absolute values range between 0.1 to 0.95.
Which model describes the underlying data in this situation?
| A. A naive Bayesian model, since the features are all conditionally independent.
| B. A full Bayesian network, since the features are all conditionally independent.
| C. A naive Bayesian model, since some of the features are statistically dependent.
| D. A full Bayesian network, since some of the features are statistically dependent.
|
D. A full Bayesian network, since some of the features are statistically dependent.
Explanation:
A naive Bayesian model assumes that the features are conditionally independent given the class label. This means that the joint probability of the features and the class can be factorized as the product of the class prior and the feature likelihoods. A full Bayesian network, on the other hand, does not make this assumption and allows for modeling arbitrary dependencies between the features and the class using a directed acyclic graph. In this case, the joint probability of the features and the class is given by the product of the conditional probabilities of each node given its parents in the graph. If the features are statistically dependent, meaning that their correlation coefficients are not close to zero, then a naive Bayesian model would not capture these dependencies and would likely perform worse than a full Bayesian network that can account for them. Therefore, a full Bayesian network describes the underlying data better in this situation.
References:
• Naive Bayes and Text Classification I
• Bayesian Networks
Question # 3
A Machine Learning Specialist is planning to create a long-running Amazon EMR cluster. The EMR cluster will have 1 master node, 10 core nodes, and 20 task nodes. To save on costs, the Specialist will use Spot Instances in the EMR cluster.
Which nodes should the Specialist launch on Spot Instances?
| A. Master node
| B. Any of the core nodes
| C. Any of the task nodes
| D. Both core and task nodes
|
C. Any of the task nodes
Explanation:
The best option for using Spot Instances in a long-running Amazon EMR cluster is to use them for the task nodes. Task nodes are optional nodes that are used to increase the processing power of the cluster. They do not store any data and can be added or removed without affecting the cluster’s operation. Therefore, they are more resilient to interruptions caused by Spot Instance termination. Using Spot Instances for the master node or the core nodes is not recommended, as they store important data and metadata for the cluster. If they are terminated, the cluster may fail or lose data.
References:
• Amazon EMR on EC2 Spot Instances
• Instance purchasing options - Amazon EMR
Question # 4
A machine learning specialist stores IoT soil sensor data in Amazon DynamoDB table and stores weather event data as JSON files in Amazon S3. The dataset in DynamoDB is 10 GB in size and the dataset in Amazon S3 is 5 GB in size. The specialist wants to train a model on this data to help predict soil moisture levels as a function of weather events using Amazon SageMaker.
Which solution will accomplish the necessary transformation to train the Amazon SageMaker model with the LEAST amount of administrative overhead?
| A. Launch an Amazon EMR cluster. Create an Apache Hive external table for the DynamoDB table and S3 data. Join the Hive tables and write the results out to Amazon S3.
| B. Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output to an Amazon Redshift cluster.
| C. Enable Amazon DynamoDB Streams on the sensor table. Write an AWS Lambda function that consumes the stream and appends the results to the existing weather files in Amazon S3.
| D. Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3.
|
D. Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3.
Explanation:
The solution that will accomplish the necessary transformation to train the Amazon SageMaker model with the least amount of administrative overhead is to crawl the data using AWS Glue crawlers, write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3. This solution leverages the serverless capabilities of AWS Glue to automatically discover the schema of the data sources, and to perform the data integration and transformation without requiring any cluster management or configuration. The output in CSV format is compatible with Amazon SageMaker and can be easily loaded into a training job.
Question # 5
A finance company needs to forecast the price of a commodity. The company has compiled a dataset of historical daily prices. A data scientist must train various forecasting models on 80% of the dataset and must validate the efficacy of those models on the remaining 20% of the dataset.
What should the data scientist split the dataset into a training dataset and a validation dataset to compare model performance?
| A. Pick a date so that 80% to the data points precede the date Assign that group of data points as the training dataset. Assign all the remaining data points to the validation dataset.
| B. Pick a date so that 80% of the data points occur after the date. Assign that group of data points as the training dataset. Assign all the remaining data points to the validation dataset.
| C. Starting from the earliest date in the dataset. pick eight data points for the training dataset and two data points for the validation dataset. Repeat this stratified sampling until no data points remain.
| D. Sample data points randomly without replacement so that 80% of the data points are in the training dataset. Assign all the remaining data points to the validation dataset.
|
A. Pick a date so that 80% to the data points precede the date Assign that group of data points as the training dataset. Assign all the remaining data points to the validation dataset.
Explanation:
A Comprehensive Explanation: The best way to split the dataset into a training dataset and a validation dataset is to pick a date so that 80% of the data points precede the date and assign that group of data points as the training dataset. This method preserves the temporal order of the data and ensures that the validation dataset reflects the most recent trends and patterns in the commodity price. This is important for forecasting models that rely on time series analysis and sequential data. The other methods would either introduce bias or lose information by ignoring the temporal structure of the data.
References:
• Time Series Forecasting - Amazon SageMaker
• Time Series Splitting - scikit-learn
• Time Series Forecasting - Towards Data Science
Question # 6
A Machine Learning Specialist is creating a new natural language processing application that processes a dataset comprised of 1 million sentences The aim is to then run Word2Vec to generate embeddings of the sentences and enable different types of predictions -
Here is an example from the dataset
"The quck BROWN FOX jumps over the lazy dog "
Which of the following are the operations the Specialist needs to perform to correctly sanitize and prepare the data in a repeatable manner? (Select THREE)
| A. Perform part-of-speech tagging and keep the action verb and the nouns only
| B. Normalize all words by making the sentence lowercase
| C. Remove stop words using an English stopword dictionary.
| D. Correct the typography on "quck" to "quick."
| E. One-hot encode all words in the sentence
|
B. Normalize all words by making the sentence lowercase
C. Remove stop words using an English stopword dictionary.
Explanation:
To prepare the data for Word2Vec, the Specialist needs to perform some pre processing steps that can help reduce the noise and complexity of the data, as well as improve the quality of the embeddings. Some of the common pre processing steps for Word2Vec are:
• Normalizing all words by making the sentence lowercase: This can help reduce the vocabulary size and treat words with different capitalizations as the same word. For example, “Fox” and “fox” should be considered as the same word, not two different words.
• Removing stop words using an English stopword dictionary: Stop words are words that are very common and do not carry much semantic meaning, such as “the”, “a”, “and”, etc. Removing them can help focus on the words that are more relevant and informative for the task.
• Tokenizing the sentence into words: Tokenization is the process of splitting a sentence into smaller units, such as words or subwords. This is necessary for Word2Vec, as it operates on the word level and requires a list of words as input.
The other options are not necessary or appropriate for Word2Vec:
• Performing part-of-speech tagging and keeping the action verb and the nouns only: Part-of-speech tagging is the process of assigning a grammatical category to each word, such as noun, verb, adjective, etc. This can be useful for some natural language processing tasks, but not for Word2Vec, as it can lose some important information and context by discarding other words.
• Correcting the typography on “quck” to “quick”: Typo correction can be helpful for some tasks, but not for Word2Vec, as it can introduce errors and inconsistencies in the data. For example, if the typo is intentional or part of a dialect, correcting it can change the meaning or style of the sentence. Moreover, Word2Vec can learn to handle typos and variations in spelling by learning similar embeddings for them.
• One-hot encoding all words in the sentence: One-hot encoding is a way of representing words as vectors of 0s and 1s, where only one element is 1 and the rest are 0. The index of the 1 element corresponds to the word’s position in the vocabulary. For example, if the vocabulary is [“cat”, “dog”, “fox”], then “cat” can be encoded as [1, 0, 0], “dog” as [0, 1, 0], and “fox” as [0, 0, 1].
This can be useful for some machine learning models, but not for Word2Vec, as it does not capture the semantic similarity and relationship between words. Word2Vec aims to learn dense and low-dimensional embeddings for words, where similar words have similar vectors.
Question # 7
| Which AWS service can provide a curated selection of pre-trained embedding models to reduce the complexity and cost of vector embeddings? | | A. Amazon SageMaker Feature Store | | B. Amazon Kendra | | C. Amazon SageMaker JumpStart | | D. Amazon Comprehend |
C. Amazon SageMaker JumpStart
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