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Amazon Web Services MLS-C01 Exam Sample Questions

Question # 1

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

Answer

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

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Question # 2

A machine learning (ML) specialist wants to create a data preparation job that uses a PySpark script with complex window aggregation operations to create data for training and testing. The ML specialist needs to evaluate the impact of the number of features and the sample count on model performance. Which approach should the ML specialist use to determine the ideal data transformations for the model?

A. Add an Amazon SageMaker Debugger hook to the script to capture key metrics. Run the script as an AWS Glue job.

B. Add an Amazon SageMaker Experiments tracker to the script to capture key metrics. Run the script as an AWS Glue job.

C. Add an Amazon SageMaker Debugger hook to the script to capture key parameters. Run the script as a SageMaker processing job.

D. Add an Amazon SageMaker Experiments tracker to the script to capture key parameters. Run the script as a SageMaker processing job.

Answer

D. Add an Amazon SageMaker Experiments tracker to the script to capture key parameters. Run the script as a SageMaker processing job.

Explanation:

Amazon SageMaker Experiments is a service that helps track, compare, and evaluate different iterations of ML models. It can be used to capture key parameters such as the number of features and the sample count from a PySpark script that runs as a SageMaker processing job. A SageMaker processing job is a flexible and scalable way to run data processing workloads on AWS, such as feature engineering, data validation, model evaluation, and model interpretation.

References:

• Amazon SageMaker Experiments

• Process Data and Evaluate Models

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Question # 3

A company wants to detect credit card fraud. The company has observed that an average of 2% of credit card transactions are fraudulent. A data scientist trains a classifier on a year's worth of credit card transaction data. The classifier needs to identify the fraudulent transactions. The company wants to accurately capture as many fraudulent transactions as possible. Which metrics should the data scientist use to optimize the classifier? (Select TWO.)

A. Specificity

B. False positive rate

C. Accuracy

D. Fl score

E. True positive rate

Answer

D. Fl score

E. True positive rate

Explanation:

The F1 score is a measure of the harmonic mean of precision and recall, which are both important for fraud detection. Precision is the ratio of true positives to all predicted positives, and recall is the ratio of true positives to all actual positives. A high F1 score indicates that the classifier can correctly identify fraudulent transactions and avoid false negatives. The true positive rate is another name for recall, and it measures the proportion of fraudulent transactions that are correctly detected by the classifier. A high true positive rate means that the classifier can capture as many fraudulent transactions as possible.

References:

• Fraud Detection Using Machine Learning | Implementations | AWS Solutions

• Detect fraudulent transactions using machine learning with Amazon SageMaker | AWS

Machine Learning Blog

• 1. Introduction — Reproducible Machine Learning for Credit Card Fraud Detection

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Question # 4

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

Answer

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.

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Question # 5

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)

Answer

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

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