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| Section | Weight | Objectives |
|---|---|---|
| Model Development | 30% | - Algorithm selection and model architecture - Distributed training - Hyperparameter optimization - ML frameworks (SageMaker, built-in algorithms) - Training and validation strategies - Transfer learning and fine-tuning |
| Data Processing | 22% | - Handling imbalanced data - Data ingestion and transformation - Data preprocessing and feature engineering - Data pipelining with AWS services ( Glue, Data Brew, etc.) - Data validation and quality assessment |
| Model Deployment and Inference | 20% | - Inference optimization (latency, throughput) - A/B testing and shadow mode deployment - Model versioning and rollback - Model deployment strategies (real-time, batch) - SageMaker endpoints configuration |
| MLOps and Monitoring | 28% | - Model lineage and reproducibility - Model monitoring and drift detection - CI/CD pipelines for ML - Cost optimization for ML workloads - Incident response and remediation - Security and access management for ML |
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NEW QUESTION # 155
A company uses a training job on Amazon SageMaker Al to train a neural network. The job first trains a model and then evaluates the model's performance ag test dataset. The company uses the results from the evaluation phase to decide if the trained model will go to production.
The training phase takes too long. The company needs solutions that can shorten training time without decreasing the model's final performance.
Select the correct solutions from the following list to meet the requirements for each description. Select each solution one time or not at all. (Select THREE.)
. Change the epoch count.
. Choose an Amazon EC2 Spot Fleet.
Change the batch size.
. Use early stopping on the training job.
Use the SageMaker Al distributed data parallelism (SMDDP) library.
. Stop the training job.
Answer:
Explanation:
Explanation:
Change the number of samples used in each iteration of training
Correct selection:
Change the batch size
Why:
Increasing the batch size reduces the number of iterations per epoch, which can significantly shorten training time while maintaining model quality when tuned appropriately. AWS explicitly recommends batch size tuning as a primary performance optimization.
Increase the number of instances used during training
Correct selection:
Use the SageMaker AI distributed data parallelism (SMDDP) library
Why:
SMDDP is designed to efficiently distribute training data across multiple GPU instances with optimized gradient synchronization. This accelerates training without affecting model convergence or accuracy, unlike naive scaling approaches.
Stop training before the maximum number of epochs are reached if performance is sufficient and not improving Correct selection:
Use early stopping on the training job
Why:
Early stopping automatically terminates training when validation metrics stop improving. AWS recommends this to reduce wasted compute time while preserving optimal model performance.
NEW QUESTION # 156
A company is developing a new ML model that uses the XGBoost algorithm. The company will train the model on data that is stored in an Amazon S3 bucket. The data is in a nested JSON format.
An ML engineer needs to convert the JSON files into a tabular format.
Which solution will meet this requirement with the LEAST operational overhead?
Answer: A
Explanation:
The AWS Glue PySpark Relationalize transform is purpose-built to convert nested JSON into tabular format with minimal operational overhead. It automates the flattening process without requiring custom code or complex infrastructure, making it the most efficient solution for preparing the data for XGBoost training.
NEW QUESTION # 157
A company has used Amazon SageMaker to deploy a predictive ML model in production. The company is using SageMaker Model Monitor on the model. After a model update, an ML engineer notices data quality issues in the Model Monitor checks.
What should the ML engineer do to mitigate the data quality issues that Model Monitor has identified?
Answer: A
NEW QUESTION # 158
A medical company ingests streams of data from devices that monitor patients' vital signs. The company uses Amazon SageMaker and plans to prepare ML models to predict adverse events for patients. The dataset is large with thousands of features.
An ML engineer needs to run several hundred training iterations with different sets of features, different algorithms, and many potential parameters. The ML engineer must implement a solution to log the characteristics and results of each training iteration.
Which solution will meet these requirements with the LEAST implementation effort?
Answer: A
Explanation:
SageMaker Experiments is specifically designed to track and organize ML experiments, including characteristics such as features, algorithms, parameters, and results. It provides experiment tracking with minimal implementation effort, making it the best fit for logging and comparing multiple training iterations.
NEW QUESTION # 159
A financial company receives a high volume of real-time market data streams from an external provider. The streams consist of thousands of JSON records every second.
The company needs to implement a scalable solution on AWS to identify anomalous data points.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: B
Explanation:
The key requirements are real-time processing, high throughput, and minimal operational overhead. Amazon Kinesis Data Streams is designed for ingesting thousands of events per second with low latency.
For anomaly detection on streaming data, Amazon Managed Service for Apache Flink provides a built-in Random Cut Forest (RCF) function. RCF is an unsupervised anomaly detection algorithm that works well on numerical streaming data and does not require labeled training data.
This fully managed combination eliminates the need to deploy or maintain SageMaker endpoints, EC2 instances, or custom ML pipelines. Options B and C introduce unnecessary infrastructure and model management overhead. Option D is batch-oriented and unsuitable for real-time anomaly detection.
Therefore, using Kinesis Data Streams with Flink's built-in Random Cut Forest is the most scalable and low- overhead solution.
NEW QUESTION # 160
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