AWS Certified Machine Learning Specialty
3 PRACTICE EXAMS

Detailed AWS Machine Learning (MLS-C01) certification experience

Since it feels like everybody who passes a certification has to post this on reddit, here is my post :)

Passed the MLS-C01 certification recently. There is not that much detailed info on the exam compared to other popular AWS certifications, so I want to give as detailed information as possible so everybody who is looking into this certification will have a better idea what he can expect from the exam.

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While preparing for SAA-C02 has a gold standard by using adrian cantrill's and/or stephane maarek's course in conjunction with john bonso's exam questions there is nothing comparable for the AWS Machine Learning Certification. I used both available courses from Linux Academy as well as ACloudGuru. Neither of those alone will get you the certification, but both give a very good overview of topics contained in the exam.

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Stuff I already knew:

• did a data science bootcamp a while ago so I have a good understanding of the whole data science lifecycle and already completed couple data science projects myself
• already have the SAA-C02 certification which helps a lot when it comes to dismissing answers in the exam
• 10+ years experience with programming languages, RDS, various developing patterns and IT best practices

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Stuff that I used:

• LA MLS course (good overview)
• ACG MLS course (good overview)
• AWS SageMaker Documentation (main resource)
• Sagemaker DeepDive Playlist - https://www.youtube.com/watch?v=uQc8Itd4UTs&list=PLhr1KZpdzukcOr6jzmSrvYnLUtgqsZz (very good imo)
• Bunch of SageMaker presentations from re:invent (mainly 3xx/4xx)
• AWS Blog (imo most of the scenario based questions in the exam are coming from case studies from the blog)
• Udemy Practice Exam by Abhishek Singh [https://www.udemy.com/course/aws-certified-machine-learning-specialty-full-practice-exams/]
• => very, very good questions which come pretty close to the style of the real exam questions
• Udemy Practice Exam by Frank Kane [https://www.udemy.com/course/aws-machine-learning-practice-exam/]
• => imo a lot weaker (not as close to the exam questions) exam than the one from Singh, still worth the money to find blank spots

Stuff that got mentioned in the exam I had no idea what it does:

• AWS Service Catalog
• AWS Connect
• AWS Alexa Business

Stuff that got asked in the exam

• no questions about hyperparameter, input types, parallelization of built-in algorithms
• LOTS of questions regarding pre-processing of datasets
• dropping/imputation, oversampling
• dealing with skewed datasets (log-transform, binning, etc)
• what to do with correlating/depending features in linear regression
• how to scale and split a dataset correctly (split then scale training and fit test/validation vs scale all and split afterwards, etc)
• mitigation of high/low correlation in datasets with lots of raw features
• what to look for in features (high correlation vs low correlation, etc)
• lots of questions about dealing with over- and underfitting in general and specifically in neural nets
• dropout, early stopping, decrease number of hidden layers,... in all variations and scenarios
• regularization (L1 vs L2)
• evaluation metrics
• trick question with switching positive/negative observations so you have to adjust to that
• business implications of mis-classification (FN more/less impact on cost of business, etc)
• calculate accuracy and precision
• interpret 3x3 confusion matrix
• visualization
• best visualization types for various situations
• visualization for correlation of features (scatter plots)
• custom algorithms
• docker container (which services are used ECR? ECS? both? S3?)
• process of deploying an algorithm in a custom docker container
• docker related questions about entrypoints, paths (/opt/ml,...)
• transfer learning
• hyperparamter optimization
• xgBoost init statement - which hyperparameter to optimize when overfitting
• neural net - learning rate/batch size tuning
• scaling/load balancing
• Endpoint Configuration calculate InvokePerInstance based on given numbers
• TensorFlow scaling horovod
• 2 tricky question with IoT devices and managing endpoints vs using Neo
• algorithm choices
• business scenarios, which algo to use
  • regression scenario
  • recommendation scenario
  • binary classification
• anomaly detection scenario - which algorithm to use
• chaining of AWS Services (most of them regarding ETL)
• scenarios where you should chain services/algorithms as solutions (transcribe, translate,..)
• classical ETL questions: Glue vs Data Pipeline vs Kinesis (in combination with Lambda, Elasticsearch,...)
• EMR related questions \[PySpark integrated solutions, "EMR legacy solution" inclusion, ...\]
• SageMaker Security
• company has certain standards regarding tags, instance-types - how can this accomplished? (aws service catalog vs python script vs cloudformation script vs ...)
• generic question
• optimized filetypes for Athena
• Normal vs Poisson-Distribution
• Baysian Network/Naive Bayes/Pearson co-effcient
• Classification Scenario: Which algorithm to use ? (classic SVM RBF Kernel plot - probably all you need to know about SVM)
• Question regarding activiation function of NN in certain scenario (Softmax vs ReLu vs ...)

I just took the first of these, and I think it was generally pretty fair:

https://www.udemy.com/course/aws-certified-machine-learning-specialty-full-practice-exams/

Recommendations for AWS Certified Machine Learning Speciality exam preparation?

I'd like materials targeted at the exam.

I would particularly like practice tests, with clear explanations for right and wrong answers. I have found these, and will appreciate appraisals of their quality: Sundog@Udemy, Singh@Udemy, Ahmed@Udemy, ExamRealSkills@Udemy (Course&ExamPrep), Frlez/Udemy, Whizlabs

I'll appreciate appraisals of course materials. I have found: Sundog@Udemy , ACloudGuru, AWS's own training, Coursera, and Linux Academy

Is there a good book? Mengle Mastering Machined Learning on AWS is the only one that I have found