Exam AWS Certified Machine Learning - Specialty All Questions

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Exam AWS Certified Machine Learning - Specialty topic 1 question 65 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty

Question #: 65
Topic #: 1

[All AWS Certified Machine Learning - Specialty Questions]

Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors. While exploring the data, the
Specialist notices that the magnitude of the input features vary greatly. The Specialist does not want variables with a larger magnitude to dominate the model.
What should the Specialist do to prepare the data for model training?

A. Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution.
B. Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude.
C. Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude.
D. Apply the orthogonal sparse bigram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.

Show Suggested Answer

Suggested Answer: C 🗳️

by rsimham at Dec. 10, 2019, 5:39 a.m.

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rsimham

Highly Voted 2 years, 9 months ago

Ans: C; Normalization is correct

upvoted 34 times

gcpwhiz

2 years, 8 months ago

Ans is not C. What is listed there is the definition of STANDARDIZATION. Normalization just scales and is not useful for reducing the effect of outliers

upvoted 4 times

gcpwhiz

2 years, 7 months ago

nevermind ignore this

upvoted 4 times

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Phong

Highly Voted 2 years, 8 months ago

Guys, I passed the exam today. It is a tough one but there are many questions here. Good luck everyone! Thank examtopics

upvoted 14 times

haison8x

2 years, 8 months ago

Hi Phong! Please add my skype: haison8x

upvoted 2 times

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Mickey321

Most Recent 10 months, 1 week ago

Selected Answer: C

Ans: C; Normalization is correct

upvoted 2 times

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kaike_reis

11 months ago

C (Yep, STANDARDIZATION is the correct name) That's an odd question for me

upvoted 1 times

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OssamaAbdelatif

1 year, 7 months ago

Selected Answer: C

ans C is correct.

upvoted 1 times

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Deepsachin

2 years, 7 months ago

ANS should be C as Normalization work best in case of amplitude diff

upvoted 1 times

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grandgale

2 years, 8 months ago

Hi, guys, First thanks this website for the information it provided. However, the ML exam has updated most of the questions. only 20+ questions here are included in today's test. Anyway, it is still helpful. GOOD LUCK EVERYONE!

upvoted 10 times

joker34

2 years, 8 months ago

So there are 40+ other questions on the exam that aren't included in Examtopics?

upvoted 2 times

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nez15

2 years, 9 months ago

QUESTION 69 A large consumer goods manufacturer has the following products on sale: • 34 different toothpaste variants • 48 different toothbrush variants • 43 different mouthwash variants The entire sales history of all these products is available in Amazon S3. Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products. The company wants to predict the demand for a new product that will soon be launched. Which solution should a Machine Learning Specialist apply? A. Train a custom ARIMA model to forecast demand for the new product. B. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product. C. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product. D. Train a custom XGBoost model to forecast demand for the new product. Correct Answer: B

upvoted 4 times

VB

2 years, 8 months ago

https://aws.amazon.com/blogs/machine-learning/forecasting-time-series-with-dynamic-deep-learning-on-aws/ Answer: B

upvoted 1 times

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nez15

2 years, 9 months ago

QUESTION 68 An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen. Which combination of algorithms would provide the appropriate insights? (Select TWO.) A. The factorization machines (FM) algorithm B. The Latent Dirichlet Allocation (LDA) algorithm C. The principal component analysis (PCA) algorithm D. The k-means algorithm E. The Random Cut Forest (RCF) algorithm Correct Answer: CD

upvoted 5 times

VB

2 years, 8 months ago

https://aws.amazon.com/blogs/machine-learning/analyze-us-census-data-for-population-segmentation-using-amazon-sagemaker/ Answer: C and D

upvoted 4 times

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cybe001

2 years, 9 months ago

I think the answer is A and B. The census question and answer will be in text. Use LDA (unsupervised algorithm) which takes the census question/answer and groups them into categories. Use the categorization to group the people and identify similar people. Use the Factorization Machine to group the people. For each person identify if they answer a question or not. Find the total questions they answered and that will be the Target variable. Now the problem is similar to movie recommendation (consider each question a movie and the total number of questions answered will be the Rating). Based on the questions a Person answered, Factorization Machine groups the people. Findings from both the algorithms can be used to compare and identify the people for the social programs.

upvoted 2 times

kaike_reis

11 months ago

it's CD

upvoted 1 times

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jasonsunbao

2 years, 9 months ago

FM is mainly used in recommendation system to find hidden variables between two known variables to find correlation between two variables.

upvoted 1 times

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nez15

2 years, 9 months ago

QUESTION 67 A. Use AWS Lambda to trigger an AWS Step Functions workflow to wait for dataset uploads to complete in Amazon S3. Use AWS Glue to join the datasets. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure. B. Develop the ETL workflow using AWS Lambda to start an Amazon SageMaker notebook instance. Use a lifecycle configuration script to join the datasets and persist the results in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure. C. Develop the ETL workflow using AWS Batch to trigger the start of ETL jobs when data is uploaded to Amazon S3. Use AWS Glue to join the datasets in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure. D. Use AWS Lambda to chain other Lambda functions to read and join the datasets in Amazon S3 as soon as the data is uploaded to Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure. Correct Answer: A

upvoted 6 times

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nez15

2 years, 9 months ago

QUESTION 67 A Machine Learning Specialist is developing a daily ETL workflow containing multiple ETL jobs. The workflow consists of the following processes: • Start the workflow as soon as data is uploaded to Amazon S3. • When all the datasets are available in Amazon S3, start an ETL job to join the uploaded datasets with multiple terabyte-sized datasets already stored in Amazon S3. • Store the results of joining datasets in Amazon S3. • If one of the jobs fails, send a notification to the Administrator. Which configuration will meet these requirements?

upvoted 3 times

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nez15

2 years, 9 months ago

QUESTION 66 A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena. The dataset contains more than 800,000 records stored as plaintext CSV files. Each record contains 200 columns and is approximately 1.5 MB in size. Most queries will span 5 to 10 columns only. How should the Machine Learning Specialist transform the dataset to minimize query runtime? A. Convert the records to Apache Parquet format. B. Convert the records to JSON format. C. Convert the records to GZIP CSV format. D. Convert the records to XML format. Correct Answer: A

upvoted 11 times

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