AWS Certified Machine Learning Specialty Sample Exam Questions
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AWS Machine Learning Specialty
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Question 1 of 10
1. Question
You work for a startup shirt manufacturer that has come up with a new manufacturing process for shirts that is very stylish and has become very popular since your company ran an online kickstarter fundraiser and shipped its first line of shirts. You now want to use machine learning to classify your shirt styles as either conservative or not based on customer feedback on your website. This classification information will help your designers target new designs based on the customer perception of your current offerings.
You have gathered your data from your website comments and ratings. You have also performed feature engineering of your data. You are now ready to run several model tuning jobs, as many as needed even if you have to run hundreds of them, to find the best version of your XGBoost model. You plan to do this by running many hyperparameter tuning jobs that test the range of hyperparameters you have available to you. Since you have decided on using a binary classifier algorithm and based on the business problem you are trying to solve, you have decided you need to measure the success of a hyperparameter tuning job based on precision and recall.
Which XGBoost metric is the best objective on which to evaluate your model?Correct
Option A is incorrect. The accuracy metric only measures (right cases)/(all cases), which doesn’t give you precision or recall, which are the two metrics you wish to use to evaluate your model.
Option B is incorrect. The error metric only measures (wrong cases)/(all cases), which doesn’t give you precision or recall, which are the two metrics you wish to use to evaluate your model.
Option C is correct. The f1 metric combines precision and recall into one metric. It represents the harmonic mean of precision and recall. Its formula: 2*precision*recall/(precision+recall).
Option D is incorrect. The mae metric finds the absolute value of the error between the predicted and target values. This doesn’t give you precision or recall, which are the two metrics you wish to use to evaluate your model.
Option E is incorrect. The map metric finds the mean average precision. This doesn’t give you recall, which is one of the two metrics you wish to use to evaluate your model.
Option F is incorrect. The merror metric is a multiclass classification error rate, which is represented as (wrong cases)/(all cases). This doesn’t give you recall, which is one of the two metrics you wish to use to evaluate your model. Also, this metric is used for multiclass classification problems, you are trying to solve a binary classification problem.
Reference:
Please see the Amazon SageMaker developer guide titled Tune an XGBoost Model, the XGBoost docs page titled XGBoost Parameters, and the article titled 20 Popular Machine Learning Metrics. Part 1: Classification & Regression Evaluation MetricsIncorrect
Option A is incorrect. The accuracy metric only measures (right cases)/(all cases), which doesn’t give you precision or recall, which are the two metrics you wish to use to evaluate your model.
Option B is incorrect. The error metric only measures (wrong cases)/(all cases), which doesn’t give you precision or recall, which are the two metrics you wish to use to evaluate your model.
Option C is correct. The f1 metric combines precision and recall into one metric. It represents the harmonic mean of precision and recall. Its formula: 2*precision*recall/(precision+recall).
Option D is incorrect. The mae metric finds the absolute value of the error between the predicted and target values. This doesn’t give you precision or recall, which are the two metrics you wish to use to evaluate your model.
Option E is incorrect. The map metric finds the mean average precision. This doesn’t give you recall, which is one of the two metrics you wish to use to evaluate your model.
Option F is incorrect. The merror metric is a multiclass classification error rate, which is represented as (wrong cases)/(all cases). This doesn’t give you recall, which is one of the two metrics you wish to use to evaluate your model. Also, this metric is used for multiclass classification problems, you are trying to solve a binary classification problem.
Reference:
Please see the Amazon SageMaker developer guide titled Tune an XGBoost Model, the XGBoost docs page titled XGBoost Parameters, and the article titled 20 Popular Machine Learning Metrics. Part 1: Classification & Regression Evaluation MetricsUnattempted
Option A is incorrect. The accuracy metric only measures (right cases)/(all cases), which doesn’t give you precision or recall, which are the two metrics you wish to use to evaluate your model.
Option B is incorrect. The error metric only measures (wrong cases)/(all cases), which doesn’t give you precision or recall, which are the two metrics you wish to use to evaluate your model.
Option C is correct. The f1 metric combines precision and recall into one metric. It represents the harmonic mean of precision and recall. Its formula: 2*precision*recall/(precision+recall).
Option D is incorrect. The mae metric finds the absolute value of the error between the predicted and target values. This doesn’t give you precision or recall, which are the two metrics you wish to use to evaluate your model.
Option E is incorrect. The map metric finds the mean average precision. This doesn’t give you recall, which is one of the two metrics you wish to use to evaluate your model.
Option F is incorrect. The merror metric is a multiclass classification error rate, which is represented as (wrong cases)/(all cases). This doesn’t give you recall, which is one of the two metrics you wish to use to evaluate your model. Also, this metric is used for multiclass classification problems, you are trying to solve a binary classification problem.
Reference:
Please see the Amazon SageMaker developer guide titled Tune an XGBoost Model, the XGBoost docs page titled XGBoost Parameters, and the article titled 20 Popular Machine Learning Metrics. Part 1: Classification & Regression Evaluation Metrics -
Question 2 of 10
2. Question
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?Correct
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.
Reference:
https://aws.amazon.com/blogs/big-data/building-a-recommendation-engine-with-spark-ml-on-amazon-emr-using-zeppelin/Incorrect
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.
Reference:
https://aws.amazon.com/blogs/big-data/building-a-recommendation-engine-with-spark-ml-on-amazon-emr-using-zeppelin/Unattempted
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.
Reference:
https://aws.amazon.com/blogs/big-data/building-a-recommendation-engine-with-spark-ml-on-amazon-emr-using-zeppelin/ -
Question 3 of 10
3. Question
A city wants to monitor its air quality to address the consequences of air pollution. A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city. As this is a prototype, only daily data from the last year is available.
Which model is MOST likely to provide the best results in Amazon SageMaker?Correct
Reference:
https://aws.amazon.com/blogs/machine-learning/build-a-model-to-predict-the-impact-of-weather-on-urban-air-quality-using-amazon-sagemaker/? ref=Welcome.AIIncorrect
Reference:
https://aws.amazon.com/blogs/machine-learning/build-a-model-to-predict-the-impact-of-weather-on-urban-air-quality-using-amazon-sagemaker/? ref=Welcome.AIUnattempted
Reference:
https://aws.amazon.com/blogs/machine-learning/build-a-model-to-predict-the-impact-of-weather-on-urban-air-quality-using-amazon-sagemaker/? ref=Welcome.AI -
Question 4 of 10
4. Question
A Data Engineer needs to build a model using a dataset containing customer credit card information
How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?Correct
The best approach to ensure the data remains encrypted and the credit card information is secure is:D. Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.
Here’s a breakdown of why this is the best approach:
AWS KMS:
- Encryption: AWS KMS provides robust encryption capabilities to protect sensitive data at rest.
- Key Management: It allows you to manage encryption keys securely and rotate them regularly.
- Integration: It integrates seamlessly with other AWS services like S3 and SageMaker, ensuring data is encrypted both at rest and in transit.
AWS Glue:
- Data Transformation: AWS Glue can be used to transform and clean data, including redacting sensitive information like credit card numbers.
- Data Security: It can be configured to handle sensitive data securely, ensuring compliance with data privacy regulations.
By combining AWS KMS and AWS Glue, you can effectively protect credit card information while still enabling data analysis and modeling.
Here’s why the other options are not as effective:
- A and C: Using SageMaker for data encryption and transformation is not the optimal approach. SageMaker is primarily designed for machine learning and AI workloads, not data security.
- B: While using IAM policies and Kinesis can help with access control and data flow, they don’t directly address the core issue of encrypting and redacting sensitive data.
By following the approach outlined in option D, the Data Engineer can ensure that the credit card information remains secure and compliant with data privacy regulations.
Reference:
https://docs.aws.amazon.com/sagemaker/latest/dg/pca.htmlIncorrect
The best approach to ensure the data remains encrypted and the credit card information is secure is:D. Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.
Here’s a breakdown of why this is the best approach:
AWS KMS:
- Encryption: AWS KMS provides robust encryption capabilities to protect sensitive data at rest.
- Key Management: It allows you to manage encryption keys securely and rotate them regularly.
- Integration: It integrates seamlessly with other AWS services like S3 and SageMaker, ensuring data is encrypted both at rest and in transit.
AWS Glue:
- Data Transformation: AWS Glue can be used to transform and clean data, including redacting sensitive information like credit card numbers.
- Data Security: It can be configured to handle sensitive data securely, ensuring compliance with data privacy regulations.
By combining AWS KMS and AWS Glue, you can effectively protect credit card information while still enabling data analysis and modeling.
Here’s why the other options are not as effective:
- A and C: Using SageMaker for data encryption and transformation is not the optimal approach. SageMaker is primarily designed for machine learning and AI workloads, not data security.
- B: While using IAM policies and Kinesis can help with access control and data flow, they don’t directly address the core issue of encrypting and redacting sensitive data.
By following the approach outlined in option D, the Data Engineer can ensure that the credit card information remains secure and compliant with data privacy regulations.
Reference:
https://docs.aws.amazon.com/sagemaker/latest/dg/pca.htmlUnattempted
The best approach to ensure the data remains encrypted and the credit card information is secure is:D. Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.
Here’s a breakdown of why this is the best approach:
AWS KMS:
- Encryption: AWS KMS provides robust encryption capabilities to protect sensitive data at rest.
- Key Management: It allows you to manage encryption keys securely and rotate them regularly.
- Integration: It integrates seamlessly with other AWS services like S3 and SageMaker, ensuring data is encrypted both at rest and in transit.
AWS Glue:
- Data Transformation: AWS Glue can be used to transform and clean data, including redacting sensitive information like credit card numbers.
- Data Security: It can be configured to handle sensitive data securely, ensuring compliance with data privacy regulations.
By combining AWS KMS and AWS Glue, you can effectively protect credit card information while still enabling data analysis and modeling.
Here’s why the other options are not as effective:
- A and C: Using SageMaker for data encryption and transformation is not the optimal approach. SageMaker is primarily designed for machine learning and AI workloads, not data security.
- B: While using IAM policies and Kinesis can help with access control and data flow, they don’t directly address the core issue of encrypting and redacting sensitive data.
By following the approach outlined in option D, the Data Engineer can ensure that the credit card information remains secure and compliant with data privacy regulations.
Reference:
https://docs.aws.amazon.com/sagemaker/latest/dg/pca.html -
Question 5 of 10
5. Question
A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance’s Amazon EBS volume, and needs to take a snapshot of that EBS volume. However, the ML Specialist cannot find the Amazon SageMaker notebook instance’s EBS volume or Amazon EC2 instance within the VPC.
Why is the ML Specialist not seeing the instance visible in the VPC? -
Question 6 of 10
6. Question
A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker. The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant.
Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test? -
Question 7 of 10
7. Question
A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist.
Which machine learning model type should the Specialist use to accomplish this task?Correct
The goal of classification is to determine to which class or category a data point (customer in our case) belongs to. For classification problems, data scientists would use historical data with predefined target variables AKA labels (churner/non-churner) “” answers that need to be predicted “” to train an algorithm. With classification, businesses can answer the following questions:
? Will this customer churn or not?
? Will a customer renew their subscription?
? Will a user downgrade a pricing plan?
? Are there any signs of unusual customer behavior?
Reference:
https://www.kdnuggets.com/2019/05/churn-prediction-machine-learning.htmlIncorrect
The goal of classification is to determine to which class or category a data point (customer in our case) belongs to. For classification problems, data scientists would use historical data with predefined target variables AKA labels (churner/non-churner) “” answers that need to be predicted “” to train an algorithm. With classification, businesses can answer the following questions:
? Will this customer churn or not?
? Will a customer renew their subscription?
? Will a user downgrade a pricing plan?
? Are there any signs of unusual customer behavior?
Reference:
https://www.kdnuggets.com/2019/05/churn-prediction-machine-learning.htmlUnattempted
The goal of classification is to determine to which class or category a data point (customer in our case) belongs to. For classification problems, data scientists would use historical data with predefined target variables AKA labels (churner/non-churner) “” answers that need to be predicted “” to train an algorithm. With classification, businesses can answer the following questions:
? Will this customer churn or not?
? Will a customer renew their subscription?
? Will a user downgrade a pricing plan?
? Are there any signs of unusual customer behavior?
Reference:
https://www.kdnuggets.com/2019/05/churn-prediction-machine-learning.html -
Question 8 of 10
8. Question
A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.
Based on this information, which model would have the HIGHEST accuracy?
-
Question 9 of 10
9. Question
A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains
Personally Identifiable Information (PII).
The dataset:
? Must be accessible from a VPC only.
? Must not traverse the public internet.
How can these requirements be satisfied?Correct
Reference:
https://docs.aws.amazon.com/AmazonS3/latest/dev/example-bucket-policies-vpc-endpoint.htmlIncorrect
Reference:
https://docs.aws.amazon.com/AmazonS3/latest/dev/example-bucket-policies-vpc-endpoint.htmlUnattempted
Reference:
https://docs.aws.amazon.com/AmazonS3/latest/dev/example-bucket-policies-vpc-endpoint.html -
Question 10 of 10
10. Question
During mini-batch training of a neural network for a classification problem, a Data Scientist notices that training accuracy oscillates.
What is the MOST likely cause of this issue?Correct
Reference:
https://towardsdatascience.com/deep-learning-personal-notes-part-1-lesson-2-8946fe970b95Incorrect
Reference:
https://towardsdatascience.com/deep-learning-personal-notes-part-1-lesson-2-8946fe970b95Unattempted
Reference:
https://towardsdatascience.com/deep-learning-personal-notes-part-1-lesson-2-8946fe970b95
- We are offering 574 latest real AWS Certified Machine Learning Specialty Exam Questions for practice, which will help you to score higher in your exam.
- Aim for above 85% or above in our mock exams before giving the main exam.
- Do review wrong & right answers and thoroughly go through explanations provided to each question which will help you understand the question.
- Master Cheat Sheet was prepared by instructors which contains personal notes of them for all exam objectives. Carefully written to help you all understand the topics easily. It is recommended to use the Master cheat sheet as a final step of preparation to cram the important topics before the exam.
- Weekly updates: We have a dedicated team updating our question bank on a regular basis, based on the feedback of students on what appeared on the actual exam, as well as through external benchmarking.
The AWS Certified Machine Learning Specialty Exam Questions certification is intended for individuals who perform a development or data science role. It validates a candidate’s ability to design, implement, deploy, and maintain machine learning (ML) solutions for given business problems.
Abilities Validated by the Certification
- Select and justify the appropriate ML approach for a given business problem
- Identify appropriate AWS services to implement ML solutions
- Design and implement scalable, cost-optimized, reliable, and secure ML solutions
It is recommended to have below knowledge when attempting AWS Certified Machine Learning Specialty Exam Questions
- Data Engineering (20%): S3 (and VPC Endpoint Gateway), Kinesis (Streams, FireHose, Data Analytics, Video), Glue (Data Catalog and Crawler), Athena, AWS Data Stores (Redshift, RDS/Aurora, DynamoDB, ElasticSearch, ElastiCache), AWS Data Pipelines, AWS Batch, AWS DMS, and AWS Step Functions.
- Exploratory Data Analysis (24%): Data Types and Distribution, Time Series, Amazon Athena, Quicksight, Ground Truth, EMR, Spark, Data binning, Transforming, Encoding, Scaling and Shuffling, Dealing with Missing data, Imbalanced data, and Outliers.
- Modeling (36%): CNN, RNN, Tuning neural networks, Regularization, Gradient descent method, L1 and L2 regularization, Confusion matrix (Precision, Recall, F1, AUC), Ensemble methods (Bagging and Boosting), Amazon Sagemaker, Amazon Algorithms (Linear Learner, XGBoost, Seq2Seq, BlazingText, DeepAR, Object2Vec, ObjectDetection, Image Classification, Semantic Segmentation, RCF, LDA, KNN, K-Means, PCA, Factorization Machine), and Amazon AI Services (Comprehend, Translate, Transcribe, Polly, Rekognition, Forecast, Lex etc).
- ML Implementations and Operations (20%): SageMaker Production Variants, Neo, IoT Greengrass, Encryption at Rest and in Transit, VPC, IAM, Logging, Monitoring, Instance Types and Spot Instances, Elastic Inference, Auto-Scaling, Availability Zones, Inference Pipelines etc
Recommended Knowledge and Experience
- 1-2 years of experience developing, architecting, or running ML/deep learning workloads on the AWS Cloud
- The ability to express the intuition behind basic ML algorithms
- Experience performing basic hyperparameter optimization
- Experience with ML and deep learning frameworks
- The ability to follow model-training best practices
- The ability to follow deployment and operational best practices
For Exam Overview: https://aws.amazon.com/training/learning-paths/machine-learning/exam-preparation/
Refer our FAQ incase of further questions : https://skillcertpro.com/faq
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These questions will really help to clear the exam. I took this exam bunch until I scored 80% in all tests. Please read the reasons for wrongs answers , that will help someone to clear. I cleared machine learning recently. Thanks you!!
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Passed the exam today! These 5 practice exams definitely helped me reviewing at least 95% of the points that I need to know. I took all the practice exams and read each detailed explanation carefully, if you do the same you should be good to go!
Prashant Hardikar –
Passed the Machine Learning certification yesterday with a score of 894 in the first attempt. The tests and explanations of the answers were very useful. I would strongly recommend to anyone who want to take the ML certification, that they should take these practice tests.
Vandana Bansal –
The explanations of the questions are clear and nicely written. These practice tests and their explanations helped me a lot to pass Machine learning certification. I would highly recommend to anyone planning to take certification.
Kristopher Angelo –
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chintamani modak –
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Dinesh Gopinathan –
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Viranjan Sanjeewa Simon –
Very well constructed questions with very well constructed answers on why the correct answer is best AND why each of the other answers is not correct or not best. Also, additional material presented in the answer section for each question which reinforces learnings and directs the candidate to authoritative materials for remedial study.
Ekapol Wongsuparatkul –
I cleared the real exam on September 12, 2023, on my first attempt. I attempted all the questions and carefully read the explanations to gain knowledge. I kept retaking all the questions until I confidently scored over 90% on all practice test sets before taking the real exam. However, I noticed that some questions had incorrect answers, and some did not display images to answer.
Skillcertpro Support –
Congrats on passing the exam. Thank you for sharing the feedback. Instructor has made necessary changes to the exam.