AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY RELIABLE EXAM TESTKING | NEW APP AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY SIMULATIONS

AWS-Certified-Machine-Learning-Specialty Reliable Exam Testking | New APP AWS-Certified-Machine-Learning-Specialty Simulations

AWS-Certified-Machine-Learning-Specialty Reliable Exam Testking | New APP AWS-Certified-Machine-Learning-Specialty Simulations

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q188-Q193):

NEW QUESTION # 188
A chemical company has developed several machine learning (ML) solutions to identify chemical process abnormalities. The time series values of independent variables and the labels are available for the past 2 years and are sufficient to accurately model the problem.
The regular operation label is marked as 0. The abnormal operation label is marked as 1 . Process abnormalities have a significant negative effect on the companys profits. The company must avoid these abnormalities.
Which metrics will indicate an ML solution that will provide the GREATEST probability of detecting an abnormality?

  • A. Precision = 0.61
    Recall = 0.98
  • B. Precision = 0.91
    Recall = 0.6
  • C. Precision = 0.98
    Recall = 0.8
  • D. Precision = 0.7
    Recall = 0.9

Answer: A

Explanation:
The metrics that will indicate an ML solution that will provide the greatest probability of detecting an abnormality are precision and recall. Precision is the ratio of true positives (TP) to the total number of predicted positives (TP + FP), where FP is false positives. Recall is the ratio of true positives (TP) to the total number of actual positives (TP + FN), where FN is false negatives. A high precision means that the ML solution has a low rate of false alarms, while a high recall means that the ML solution has a high rate of true detections. For the chemical company, the goal is to avoid process abnormalities, which are marked as 1 in the labels. Therefore, the company needs an ML solution that has a high recall for the positive class, meaning that it can detect most of the abnormalities and minimize the false negatives. Among the four options, option B has the highest recall for the positive class, which is 0.98. This means that the ML solution can detect 98% of the abnormalities and miss only 2%. Option B also has a reasonable precision for the positive class, which is 0.61. This means that the ML solution has a false alarm rate of 39%, which may be acceptable for the company, depending on the cost and benefit analysis. The other options have lower recall for the positive class, which means that they have higher false negative rates, which can be more detrimental for the company than false positive rates.
References:
1: AWS Certified Machine Learning - Specialty Exam Guide
2: AWS Training - Machine Learning on AWS
3: AWS Whitepaper - An Overview of Machine Learning on AWS
4: Precision and recall


NEW QUESTION # 189
A data engineer is preparing a dataset that a retail company will use to predict the number of visitors to stores.
The data engineer created an Amazon S3 bucket. The engineer subscribed the S3 bucket to an AWS Data Exchange data product for general economic indicators. The data engineer wants to join the economic indicator data to an existing table in Amazon Athena to merge with the business data. All these transformations must finish running in 30-60 minutes.
Which solution will meet these requirements MOST cost-effectively?

  • A. Use an S3 event on the AWS Data Exchange S3 bucket to invoke an AWS Lambda Function Program the Lambda function to run an AWS Glue job that will merge the existing business data with the Athena table Write the results back to Amazon S3.
  • B. Configure the AWS Data Exchange product as a producer for an Amazon Kinesis data stream. Use an Amazon Kinesis Data Firehose delivery stream to transfer the data to Amazon S3 Run an AWS Glue job that will merge the existing business data with the Athena table. Write the result set back to Amazon S3.
  • C. Provision an Amazon Redshift cluster. Subscribe to the AWS Data Exchange product and use the product to create an Amazon Redshift Table Merge the data in Amazon Redshift. Write the results back to Amazon S3.
  • D. Use an S3 event on the AWS Data Exchange S3 bucket to invoke an AWS Lambda function. Program the Lambda function to use Amazon SageMaker Data Wrangler to merge the existing business data with the Athena table. Write the result set back to Amazon S3.

Answer: D

Explanation:
The most cost-effective solution is to use an S3 event to trigger a Lambda function that uses SageMaker Data Wrangler to merge the data. This solution avoids the need to provision and manage any additional resources, such as Kinesis streams, Firehose delivery streams, Glue jobs, or Redshift clusters. SageMaker Data Wrangler provides a visual interface to import, prepare, transform, and analyze data from various sources, including AWS Data Exchange products. It can also export the data preparation workflow to a Python script that can be executed by a Lambda function. This solution can meet the time requirement of 30-60 minutes, depending on the size and complexity of the data.
References:
* Using Amazon S3 Event Notifications
* Prepare ML Data with Amazon SageMaker Data Wrangler
* AWS Lambda Function


NEW QUESTION # 190
A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users.
What should the Specialist do to meet this objective?

  • A. Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
  • B. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.
  • C. Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
  • D. Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR.

Answer: A


NEW QUESTION # 191
A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset Which tool should be used to improve the validation accuracy?

  • A. Natural Language Toolkit (NLTK) stemming and stop word removal
  • B. Amazon SageMaker BlazingText allow mode
  • C. Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers
  • D. Amazon Comprehend syntax analysts and entity detection

Answer: C

Explanation:
Explanation
Term frequency-inverse document frequency (TF-IDF) is a technique that assigns a weight to each word in a document based on how important it is to the meaning of the document. The term frequency (TF) measures how often a word appears in a document, while the inverse document frequency (IDF) measures how rare a word is across a collection of documents. The TF-IDF weight is the product of the TF and IDF values, and it is high for words that are frequent in a specific document but rare in the overall corpus. TF-IDF can help improve the validation accuracy of a sentiment analysis model by reducing the impact of common words that have little or no sentiment value, such as "the", "a", "and", etc. Scikit-learn is a popular Python library for machine learning that provides a TF-IDF vectorizer class that can transform a collection of text documents into a matrix of TF-IDF features. By using this tool, the Data Scientist can create a more informative and discriminative feature representation for the sentiment analysis task.
References:
TfidfVectorizer - scikit-learn
Text feature extraction - scikit-learn
TF-IDF for Beginners | by Jana Schmidt | Towards Data Science
Sentiment Analysis: Concept, Analysis and Applications | by Susan Li | Towards Data Science


NEW QUESTION # 192
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. Replace the age field value for records with a value of 0 with the mean or median value from the dataset.
  • B. Drop the age feature from the dataset and train the model using the rest of the features.
  • C. Drop all records from the dataset where age has been set to 0.
  • D. Use k-means clustering to handle missing features.

Answer: A

Explanation:
The best way to handle the missing values in the patient age feature is to replace them with the mean or median value from the dataset. This is a common technique for imputing missing values that preserves the overall distribution of the data and avoids introducing bias or reducing the sample size. Dropping the records or the feature would result in losing valuable information and reducing the accuracy of the model. Using k-means clustering would not be appropriate for handling missing values in a single feature, as it is a method for grouping similar data points based on multiple features.
References:
Effective Strategies to Handle Missing Values in Data Analysis
How To Handle Missing Values In Machine Learning Data With Weka
How to handle missing values in Python - Machine Learning Plus


NEW QUESTION # 193
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