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Training Options for MLS-C01 Exam

If you don't have much hands-on experience in machine learning, it is recommended to enroll in the course and gain it before going for the AWS Certified Machine Learning – Specialty exam. There are five options offered by AWS itself, some of them are as follows:

• The Machine Learning Pipeline on AWS (by AWS Training)During these in-classroom or virtual sessions, the candidates will come across exceptional knowledge about how to use the machine learning pipeline to solve real business problems. It is the best project-based studying environment for individuals that are passionate about working with ML models using Amazon SageMaker. At the end of the course, students will be able to solve any issues related to fraud detection, flight delays, recommendation engines, etc. It leads the applicants to the path of overcoming any challenges effectively and get knowledge of machine learning thoroughly to take the test. You will find this 4-days course easy if you have prior experience in the field as well as knowledge of Python and Statistics.
• Deep Learning on AWSThis course also lasts for only one day but still has comprehensive content. It emphasizes the AWS DL (Deep Learning) solutions as well as the use of MXNet and Amazon SageMaker. Also, the candidates are going to understand how to deploy DL models by utilizing AWS services and build intelligent systems. This training can be taken in either live-classroom form or live-virtual.
• Process Model: CRISP-DM on the AWS Stack (by AWS Training)In contrast to the previous option, this training is just a one-day course. The candidates can follow it in different languages like English, French, Simplified Chinese, Indonesian, Japanese, and Korean. It focuses on the CRISP-DM model in relation to data science, including all its six phases and framework and methodology. The course also shows applicants how to use CRISP-DM to resolve various problems within their daily work. The good thing about this training is that it's free.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q29-Q34):

NEW QUESTION # 29
A company needs to quickly make sense of a large amount of data and gain insight from it. The data is in different formats, the schemas change frequently, and new data sources are added regularly. The company wants to use AWS services to explore multiple data sources, suggest schemas, and enrich and transform the dat a. The solution should require the least possible coding effort for the data flows and the least possible infrastructure management.
Which combination of AWS services will meet these requirements?

• A. Amazon EMR for data discovery, enrichment, and transformation
  Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights
• B. AWS Data Pipeline for data transfer
  AWS Step Functions for orchestrating AWS Lambda jobs for data discovery, enrichment, and transformation Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL
• C. AWS Glue for data discovery, enrichment, and transformation
  Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights
• D. Amazon Kinesis Data Analytics for data ingestion
  Amazon EMR for data discovery, enrichment, and transformation
  Amazon Redshift for querying and analyzing the results in Amazon S3

Answer: A Explanation:
Amazon QuickSight for reporting and getting insights
NEW QUESTION # 30
A company is using Amazon Polly to translate plaintext documents to speech for automated company announcements. However, company acronyms are being mispronounced in the current documents.
How should a Machine Learning Specialist address this issue for future documents?

• A. Use Amazon Lex to preprocess the text files for pronunciation
• B. Output speech marks to guide in pronunciation.
• C. Create an appropriate pronunciation lexicon.
• D. Convert current documents to SSML with pronunciation tags.

Answer: D Explanation:
https://docs.aws.amazon.com/polly/latest/dg/ssml.html
NEW QUESTION # 31
A Machine Learning Specialist deployed a model that provides product recommendations on a company's website. Initially, the model was performing very well and resulted in customers buying more products on average. However, within the past few months, the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less.
The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago.
Which method should the Specialist try to improve model performance?

• A. The model needs to be completely re-engineered because it is unable to handle product inventory changes.
• B. The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes
• C. The model should be periodically retrained using the original training data plus new data as product inventory changes.
• D. The model's hyperparameters should be periodically updated to prevent drift.

Answer: C
NEW QUESTION # 32
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: B Explanation:
Explanation
Many developers want to implement the famous Amazon model that was used to power the "People who bought this also bought these items" feature on Amazon.com. This model is based on a method called Collaborative Filtering. It takes items such as movies, books, and products that were rated highly by a set of users and recommending them to other users who also gave them high ratings. This method works well in domains where explicit ratings or implicit user actions can be gathered and analyzed.
NEW QUESTION # 33
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? (Choose two.)

• A. The k-means algorithm
• B. The Latent Dirichlet Allocation (LDA) algorithm
• C. The principal component analysis (PCA) algorithm
• D. The factorization machines (FM) algorithm
• E. The Random Cut Forest (RCF) algorithm

Answer: A,C Explanation:
The PCA and K-means algorithms are useful in collection of data using census form.
NEW QUESTION # 34
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