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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Model Development | 30% | - Distributed training - Training and validation strategies - ML frameworks (SageMaker, built-in algorithms) - Hyperparameter optimization - Algorithm selection and model architecture - Transfer learning and fine-tuning |
| Topic 2: MLOps and Monitoring | 28% | - Model monitoring and drift detection - Cost optimization for ML workloads - Model lineage and reproducibility - Security and access management for ML - CI/CD pipelines for ML - Incident response and remediation |
| Topic 3: Model Deployment and Inference | 20% | - Inference optimization (latency, throughput) - Model deployment strategies (real-time, batch) - SageMaker endpoints configuration - Model versioning and rollback - A/B testing and shadow mode deployment |
| Topic 4: Data Processing | 22% | - Data preprocessing and feature engineering - Data validation and quality assessment - Handling imbalanced data - Data ingestion and transformation - Data pipelining with AWS services ( Glue, Data Brew, etc.) |
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NEW QUESTION # 51
An ML engineering team has a data processing pipeline that ingests sensor data from IoT devices into an Amazon S3 bucket. The pipeline then processes the data by using AWS Glue extract, transform, and load (ETL) jobs for ML modeling. The team noticed throttling errors in the ETL jobs. The data ingestion process has also been slower than normal.
What is the cause of the problem?
Answer: C
Explanation:
The correct answer is A. The AWS Glue service quotas have been reached. AWS Glue is commonly used in ML data preparation pipelines to perform ETL operations before model training or feature engineering. When AWS Glue workloads exceed account-level or Region-level service quotas, jobs can be delayed, queued, throttled, or fail. AWS documentation states that AWS Glue has service quotas, also called limits, for resources and operations in each account and Region. These include quotas for concurrent compute capacity measured in DPUs, crawlers, jobs, triggers, queued job runs, and other Glue resources.
The key clue in the question is "throttling errors in the ETL jobs." AWS specifically explains that Glue API requests are throttled on a per-account, per-Region basis to maintain service performance. If the team is running many jobs, processing a large amount of sensor data, or starting too many Glue job runs concurrently, the workload can exceed the permitted quota and cause throttling.
Option B is less likely because insufficient network bandwidth between IoT devices and the AWS Region would mainly explain slower ingestion into S3, but it would not directly explain AWS Glue ETL throttling errors. Option C might cause poor job performance, but lack of parallel optimization usually causes long runtimes, not service-level throttling. Option D is incorrect because missing Amazon S3 permissions would typically cause access-denied or authorization failures, not throttling. Therefore, the best cause is that the AWS Glue service quotas, such as concurrent job runs, API rate limits, or DPU capacity limits, have been reached.
NEW QUESTION # 52
A machine learning engineer is preparing a data frame for a supervised learning task with the Amazon SageMaker Linear Learner algorithm. The ML engineer notices the target label classes are highly imbalanced and multiple feature columns contain missing values. The proportion of missing values across the entire data frame is less than 5%.
What should the ML engineer do to minimize bias due to missing values?
Answer: A
Explanation:
Use supervised learning to predict missing values based on the values of other features. Different supervised learning approaches might have different performances, but any properly implemented supervised learning approach should provide the same or better approximation than mean or median approximation, as proposed in responses A and C. Supervised learning applied to the imputation of missing values is an active field of research.
NEW QUESTION # 53
A company is building a deep learning model on Amazon SageMaker. The company uses a large amount of data as the training dataset. The company needs to optimize the model's hyperparameters to minimize the loss function on the validation dataset.
Which hyperparameter tuning strategy will accomplish this goal with the LEAST computation time?
Answer: D
Explanation:
Hyperband is a hyperparameter tuning strategy designed to minimize computation time by adaptively allocating resources to promising configurations and terminating underperforming ones early. It efficiently balances exploration and exploitation, making it ideal for large datasets and deep learning models where training can be computationally expensive.
NEW QUESTION # 54
A company is developing an ML model by using Amazon SageMaker AI. The company must monitor bias in the model and display the results on a dashboard. An ML engineer creates a bias monitoring job.
How should the ML engineer capture bias metrics to display on the dashboard?
Answer: A
Explanation:
Amazon SageMaker Clarify is the AWS service used to detect and quantify bias and fairness metrics in ML models. When bias monitoring jobs run, Clarify publishes bias metrics directly to Amazon CloudWatch.
CloudWatch metrics can be visualized using CloudWatch dashboards or integrated into other monitoring tools, making them ideal for real-time or periodic bias reporting.
CloudTrail logs API activity and does not capture ML metrics. EventBridge and SNS are used for event routing and notifications, not metric visualization.
AWS documentation explicitly states that Clarify bias metrics are emitted to Amazon CloudWatch, which is the correct source for dashboards.
Therefore, Option B is the correct and AWS-verified answer.
NEW QUESTION # 55
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 # 56
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