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Guide for AWS Certified Machine Learning Engineer - Associate

Quiz AWS Certified Machine Learning Engineer - Associate

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Quiz

1/10
You are a machine learning engineer at a fintech company tasked with developing and deploying an end-to-end machine learning workflow for fraud detection. The workflow involves multiple steps, including data extraction, preprocessing, feature engineering, model training, hyperparameter tuning, and deployment. The company requires the solution to be scalable, support complex dependencies between tasks, and provide robust monitoring and versioning capabilities. Additionally, the workflow needs to integrate seamlessly with existing AWS services. Which deployment orchestrator is the MOST SUITABLE for managing and automating your ML workflow?
Select the answer
1 correct answer
A.
Use AWS Step Functions to build a serverless workflow that integrates with SageMaker for model training and deployment, ensuring scalability and fault tolerance
B.
Use AWS Lambda functions to manually trigger each step of the ML workflow, enabling flexible execution without needing a predefined orchestration tool
C.
Use Amazon SageMaker Pipelines to orchestrate the entire ML workflow, leveraging its built-in integration with SageMaker features like training, tuning, and deployment
D.
Use Apache Airflow to define and manage the workflow with custom DAGs (Directed Acyclic Graphs), integrating with AWS services through operators and hooks

Quiz

2/10
You are tasked with building a predictive model for customer lifetime value (CLV) using Amazon SageMaker. Given the complexity of the model, it’s crucial to optimize hyperparameters to achieve the best possible performance. You decide to use SageMaker’s automatic model tuning (hyperparameter optimization) with Random Search strategy to fine-tune the model. You have a large dataset, and the tuning job involves several hyperparameters, including the learning rate, batch size, and dropout rate. During the tuning process, you observe that some of the trials are not converging effectively, and the results are not as expected. You suspect that the hyperparameter ranges or the strategy you are using may need adjustment. Which of the following approaches is MOST LIKELY to improve the effectiveness of the hyperparameter tuning process?
Select the answer
1 correct answer
A.
Decrease the number of total trials but increase the number of parallel jobs to speed up the tuning process
B.
Switch from the Random Search strategy to the Bayesian Optimization strategy and narrow the range of critical hyperparameters
C.
Use the Grid Search strategy with a wide range for all hyperparameters and increase the number of total trials
D.
Increase the number of hyperparameters being tuned and widen the range for all hyperparameters

Quiz

3/10
A company stores its training datasets on Amazon S3 in the form of tabular data running into millions of rows. The company needs to prepare this data for Machine Learning jobs. The data preparation involves data selection, cleansing, exploration, and visualization using a single visual interface. Which Amazon SageMaker service is the best fit for this requirement?
Select the answer
1 correct answer
A.
Amazon SageMaker Feature Store
B.
Amazon SageMaker Data Wrangler
C.
SageMaker Model Dashboard
D.
Amazon SageMaker Clarify

Quiz

4/10
Which of the following strategies best aligns with the defense-in-depth security approach for generative AI applications on AWS?
Select the answer
1 correct answer
A.
Relying solely on data encryption to protect the AI training data
B.
Applying multiple layers of security measures including input validation, access controls, and continuous monitoring to address vulnerabilities
C.
Using a single authentication mechanism for all users and services accessing the AI models
D.
Implementing a single-layer firewall to block unauthorized access to the AI models

Quiz

5/10
You are an ML engineer at an e-commerce company tasked with building an automated recommendation system that scales during peak shopping seasons. The solution requires provisioning multiple compute resources, including SageMaker for model training, EC2 instances for data preprocessing, and an RDS database for storing user interaction data. You need to automate the deployment and management of these resources, ensuring that the stacks can communicate effectively. The company prioritizes infrastructure as code (IaC) to maintain consistency and scalability across environments. Which approach is the MOST SUITABLE for automating the provisioning of compute resources and ensuring seamless communication between stacks?
Select the answer
1 correct answer
A.
Manually provision the SageMaker, EC2, and RDS resources using the AWS Management Console, ensuring that communication is established by manually updating security groups and networking configurations
B.
Use AWS Elastic Beanstalk to deploy the entire ML solution, relying on its built-in environment management to handle the provisioning and communication between resources automatically
C.
Use AWS CDK (Cloud Development Kit) to define the infrastructure in a high-level programming language, deploying each service as an independent stack without configuring inter-stack communication
D.
Use AWS CloudFormation with nested stacks to automate the provisioning of SageMaker, EC2, and RDS resources, and configure outputs from one stack as inputs to another to enable communication between them

Quiz

6/10
You are a data scientist at a healthcare startup tasked with developing a machine learning model to predict the likelihood of patients developing a specific chronic disease within the next five years. The dataset available includes patient demographics, medical history, lab results, and lifestyle factors, but it is relatively small, with only 1,000 records. Additionally, the dataset has missing values in some critical features, and the class distribution is highly imbalanced, with only 5% of patients labeled as having developed the disease. Given the data limitations and the complexity of the problem, which of the following approaches is the MOST LIKELY to determine the feasibility of an ML solution and guide your next steps?
Select the answer
1 correct answer
A.
Proceed with training a deep neural network (DNN) model using the available data, as DNNs can handle small datasets by learning complex patterns
B.
Increase the dataset size by generating synthetic data and then train a simple logistic regression model to avoid overfitting
C.
Conduct exploratory data analysis (EDA) to understand the data distribution, address missing values, and assess the class imbalance before determining if an ML solution is feasible
D.
Immediately apply an oversampling technique to balance the dataset, then train an XGBoost model to maximize performance on the minority class

Quiz

7/10
You are a lead machine learning engineer at a growing tech startup that is developing a recommendation system for a mobile app. The recommendation engine must be able to scale quickly as the user base grows, remain cost-effective to align with the startup’s budget constraints, and be easy to maintain by a small team of engineers. The company has decided to use AWS for the ML infrastructure. Your goal is to design an infrastructure that meets these needs, ensuring that it can handle rapid scaling, remains within budget, and is simple to update and monitor. Which combination of practices and AWS services is MOST LIKELY to result in a maintainable, scalable, and cost-effective ML infrastructure?
Select the answer
1 correct answer
A.
Implement Amazon SageMaker for model training, deploy the models using Amazon EC2 with manual scaling to handle inference, and use AWS CloudFormation for managing infrastructure as code to ensure repeatability
B.
Use Amazon SageMaker for both training and deployment, leverage auto-scaling endpoints for real- time inference, and apply SageMaker Pipelines for orchestrating end-to-end ML workflows, ensuring scalability and automation
C.
Use Amazon SageMaker for training, deploy models on Amazon ECS for flexible scaling, and implement infrastructure monitoring with a combination of CloudWatch and AWS Systems Manager to ensure maintainability
D.
Train models using Amazon EMR for cost efficiency, deploy the models using AWS Lambda for serverless inference, and manually monitor the system using CloudWatch to reduce operational overhead

Quiz

8/10
You are working on a machine learning project for a financial services company, developing a model to predict credit risk. After deploying the initial version of the model using Amazon SageMaker, you find that its performance, measured by the AUC (Area Under the Curve), is not meeting the company’s accuracy requirements. Your team has gathered more data and believes that the model can be further optimized. You are considering various methods to improve the model’s performance, including feature engineering, hyperparameter tuning, and trying different algorithms. However, given the limited time and computational resources, you need to prioritize the most impactful strategies. Which of the following approaches are the MOST LIKELY to lead to a significant improvement in model performance? (Select two)
Select the answer
2 correct answers
A.
Increase the size of the training dataset by incorporating synthetic data and then retrain the existing model
B.
Perform hyperparameter tuning using Bayesian optimization and increase the number of trials to explore a broader search space
C.
Switch to a more complex algorithm, such as deep learning, and use transfer learning to leverage pre-trained models
D.
Use Amazon SageMaker Debugger to debug and improve model performance by addressing underlying problems such as overfitting, saturated activation functions, and vanishing gradients
E.
Focus on feature engineering by creating

Quiz

9/10
You are a data scientist at a healthcare company developing a machine learning model to analyze medical imaging data, such as X-rays and MRIs, for disease detection. The dataset consists of 10 million high-resolution images stored in Amazon S3, amounting to several terabytes of data. The training process requires processing these images efficiently to avoid delays due to I/O bottlenecks, and you must ensure that the chosen data access method aligns with the large dataset size and the high throughput requirements of the model. Given the size and nature of the dataset, which SageMaker input mode and AWS Cloud Storage configuration is the MOST SUITABLE for this use case?
Select the answer
1 correct answer
A.
Select the Pipe input mode to stream the data directly from Amazon S3 to the training instances, allowing the model to start processing data immediately without requiring local storage for the entire dataset
B.
Use the File input mode with EFS (Amazon Elastic File System) to mount the dataset across multiple instances, ensuring data is shared and accessible during distributed training
C.
Implement the FastFile input mode with FSx for Lustre, to enable on-demand streaming of data chunks from Amazon S3 with low latency and high throughput
D.
Use the File input mode to download the entire dataset from Amazon S3 to the training instances' local storage before starting the training process, ensuring that all data is available locally during training

Quiz

10/10
You are working as a machine learning engineer for a startup that provides image recognition services. The service is currently in its beta phase, and the company expects varying levels of traffic, with some days having very few requests and other days experiencing sudden spikes. The company wants to minimize costs during low-traffic periods while still being able to handle large, infrequent spikes of requests efficiently. Given these requirements, you are considering using Amazon SageMaker for your deployment. Which of the following statements is the BEST recommendation for the given scenario?
Select the answer
1 correct answer
A.
Use Amazon SageMaker Asynchronous Inference that minimizes costs during low-traffic periods while managing large infrequent spikes of requests efficiently
B.
Use Batch transform to run inference with Amazon SageMaker that minimizes costs during low- traffic periods while managing large infrequent spikes of requests efficiently
C.
Use Amazon SageMaker Serverless Inference that minimizes costs during low-traffic periods while managing large infrequent spikes of requests efficiently
D.
Use Amazon SageMaker Real-time Inference that minimizes costs during low-traffic periods while managing large infrequent spikes of requests efficiently
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  • Quiz name:AWS Certified Machine Learning Engineer - Associate
  • Total number of questions:75
  • Number of questions for the test:50
  • Pass score:80%

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