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Who should take the Amazon MLS exam

Candidates who have the following should take this exam:

• Select and justify the appropriate ML approach for a given business problem
• Design and implement scalable, cost-optimized, reliable, and secure ML solutions
• Identify appropriate AWS services to implement ML solutions

If a candidate wants significant improvement in career growth needs enhanced knowledge, skills, and talents. TThe AWS Certified Solutions Architect - Associate examination is intended for individuals who perform a solutions architect role and have one or more years of hands-on experience designing available, cost-efficient, fault-tolerant, and scalable distributed systems on AWS.

It asks for following experience:

• The ability to express the intuition behind basic ML algorithms
• The ability to follow deployment and operational best practices
• Experience performing basic hyperparameter optimization
• Experience with ML and deep learning frameworks

Exam Topics

As for the MLS-C01 certification test, there are 4 domains that are presented in the exam content. All in all, the topics you need to focus on when preparing for this test are highlighted below:

• Machine Learning Implementation and Operations

  This is the last objective of the certification exam. It mainly includes the concepts of machine learning services as well as implementation. It carries about 20% weightage in the test. One of the most prominent sections covered within this domain is building machine learning solutions for availability, fault tolerance, resiliency, and performance. It also evaluates the candidates’ knowledge and skills in implementing and recommending the correct machine learning services particularly for a given problem. In addition, the examinees should be ready to demonstrate that they know about applying basic Amazon Web Services (AWS) security practices to specific machine learning solutions. Also, this part requires that the IT pros attempting this test make evident that they have the skills and knowledge required to deploy and operationalize machine learning solutions.

• Exploratory Data Analysis

  The second subject area covers about 24% of the total weightage and requires that the learners know about data for machine learning. They should know more about machine modeling concepts as well. The questions in this section demand that the candidates possess the skills in preparing data & sanitizing that particular data for modeling. Other items seek to verify their expertise in performing feature engineering. Last but not least, the applicants should demonstrate their understanding of how to perform visualization and canalization of data for machine learning.

• Modeling

  This is the most prominent part of this exam with a weightage of 36%. This means that it is impossible to get the passing score if you have not mastered this topic. It covers the following sections, including framing business problems as machine learning problems and training machine learning model. In addition, the test takers need to be ready to demonstrate their competence in selecting an appropriate model for specific machine learning problems. They have to possess the ability to perform hyperparameter optimization as well as perform the task of evaluating machine learning models.

• Data Engineering

  This domain makes up about 20% of the total questions in the entire exam content. It measures the test takers’ ability to accomplish the technical tasks such as creating machine and learning data repositories. Another section that is included in this topic is implementing & identifying the data ingestion solution. Besides that, the students are also required to demonstrate their skills in implementing and identifying data transformation solutions.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q126-Q131):

NEW QUESTION # 126
A machine learning specialist needs to analyze comments on a news website with users across the globe. The specialist must find the most discussed topics in the comments that are in either English or Spanish.
What steps could be used to accomplish this task? (Choose two.)

• A. Use an Amazon SageMaker seq2seq algorithm to translate from Spanish to English, if necessary. Use a SageMaker Latent Dirichlet Allocation (LDA) algorithm to find the topics.
• B. Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Comprehend topic modeling to find the topics.
• C. Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon SageMaker Neural Topic Model (NTM) to find the topics.
• D. Use an Amazon SageMaker BlazingText algorithm to find the topics independently from language. Proceed with the analysis.
• E. Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Lex to extract topics form the content.

Answer: A

NEW QUESTION # 127
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 XML format.
• D. Convert the records to GZIP CSV format.

Answer: A

Explanation:
Using compressions will reduce the amount of data scanned by Amazon Athena, and also reduce your S3 bucket storage. It's a Win-Win for your AWS bill. Supported formats: GZIP, LZO, SNAPPY (Parquet) and ZLIB.
https://www.cloudforecast.io/blog/using-parquet-on-athena-to-save-money-on-aws/

NEW QUESTION # 128
A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers. Currently, the company has the following data in Amazon Aurora:
* Profiles for all past and existing customers
* Profiles for all past and existing insured pets
* Policy-level information
* Premiums received
* Claims paid
What steps should be taken to implement a machine learning model to identify potential new customers on social media?

• A. Use clustering on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
• B. Use regression on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
• C. Use a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.
• D. Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.

Answer: C

NEW QUESTION # 129
A Machine Learning Specialist is assigned to a Fraud Detection team and must tune an XGBoost model, which is working appropriately for test dat a. However, with unknown data, it is not working as expected. The existing parameters are provided as follows.

Which parameter tuning guidelines should the Specialist follow to avoid overfitting?

• A. Increase the max_depth parameter value.
• B. Lower the min_child_weight parameter value.
• C. Lower the max_depth parameter value.
• D. Update the objective to binary:logistic.

Answer: C

NEW QUESTION # 130
A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.
Which architecture changes would ensure that provisioned resources are being utilized effectively?

• A. Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.
• B. Redeploy the model as a batch transform job on an M5 instance.
• C. Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.
• D. Redeploy the model on a P3dn instance.

Answer: C

Explanation:
https://aws.amazon.com/machine-learning/elastic-inference/

NEW QUESTION # 131
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