You are developing a machine learning pipeline using the Kubeflow Pipelines (KFP) SDK that will run on Agent Platform Pipelines. The pipeline includes a preprocessing component that takes two hours to transform a large dataset. You are currently iterating on the code for the subsequent model_training component and need to re-submit the pipeline frequently for testing. You need to ensure that the preprocessing component only re-runs if its inputs or parameters have changed.
You want to minimize effort, costs, and total execution time. What should you do?

• A. Ensure that execution caching is enabled for the preprocessing component by including the set_caching_options(enable_caching=True) method.
• B. Track the run lineage using Agent Platform Experiments, and pass the output artifact URI from the previous run into the training component.
• C. Configure the pipeline to write the preprocessed data to a persistent volume claim (PVC) that is shared across pipeline runs.
• D. Add a dsl.Condition step to the pipeline that checks if the preprocessing step has executed in the last hour.

You are building an application that extracts information from invoices and receipts. You want to implement this application with minimal custom code and training. What should you do?

• A. Use Vertex AI Agent Builder with the pre-built Layout Parser model to extract information from the invoices and receipts.
• B. Use the Cloud Document AI API to extract information from the invoices and receipts.
• C. Train an AutoML Natural Language model to classify and extract information from the invoices and receipts.
• D. Use the Cloud Vision API with TEXT_DETECTIONtype to extract text from the invoices and receipts, and use a pre-built natural language processing (NLP) model to parse the extracted text.

You are training an object detection model using a Cloud TPU v2. Training time is taking longer than expected. Based on this simplified trace obtained with a Cloud TPU profile, what action should you take to decrease training time in a cost-efficient way?

• A. Rewrite your input function using parallel reads, parallel processing, and prefetch.
• B. Move from Cloud TPU v2 to Cloud TPU v3 and increase batch size.
• C. Move from Cloud TPU v2 to 8 NVIDIA V100 GPUs and increase batch size.
• D. Rewrite your input function to resize and reshape the input images.

You are using Kubeflow Pipelines to develop an end-to-end PyTorch-based MLOps pipeline. The pipeline reads data from BigQuery, processes the data, conducts feature engineering, model training, model evaluation, and deploys the model as a binary file to Cloud Storage. You are writing code for several different versions of the feature engineering and model training steps, and running each new version in Vertex AI Pipelines. Each pipeline run is taking over an hour to complete. You want to speed up the pipeline execution to reduce your development time, and you want to avoid additional costs. What should you do?

• A. Delegate feature engineering to BigQuery and remove it from the pipeline.
• B. Enable caching in all the steps of the Kubeflow pipeline.
• C. Add a GPU to the model training step.
• D. Comment out the part of the pipeline that you are not currently updating.

You are training a ResNet-50 computer vision model on Gemini Enterprise Agent Platform using a custom PyTorch container. The training job is configured with a generic machine type and one NVIDIA A100 GPU. During the training, you monitor the resource utilization charts. You observe that the GPU utilization is volatile, frequently dropping to 0% for short intervals, while the CPU utilization remains consistently above 90%. You want to maximize GPU utilization to reduce training time. What should you do?

• A. Update the DataLoader utility to increase the num_workers parameter and enable pin_memory.
• B. Increase the training batch size to saturate the GPU memory.
• C. Enable automatic mixed precision (AMP) to speed up the matrix multiplications.
• D. Switch to a machine type with a higher number of GPUs, and implement the torch.nn.parallel.DistributedDataParallel strategy in the training code.