The best option to scale your training workload quickly while minimizing cost when using 4 V100 GPUs is
✅ "C. Package your code with Setuptools, and use a pre-built container. Train your model with Vertex AI using a custom tier that contains the required GPUs".
✅ Here's why:
❌ A. Create a Google Kubernetes Engine cluster with a node pool that has 4 V100 GPUs. Prepare and submit a TFJob operator to this node pool.: This option would require managing a Kubernetes cluster, which adds unnecessary complexity and potential extra costs. Moreover, the question doesn't mention TensorFlow as the tool, but PyTorch, making this option less suitable.
❌ B. Create a Vertex AI Workbench user-managed notebooks instance with 4 V100 GPUs, and use it to train your model.: While this option does involve using GPUs and Vertex AI, it doesn't specify how to scale up the workload or handle the large dataset, and maintaining a user-managed notebook instance can be more labor-intensive and costly.
❌ D. Configure a Compute Engine VM with all the dependencies that launches the training. Train your model with Vertex AI using a custom tier that contains the required GPUs.: This option involves manually managing VMs and dependencies, which is less efficient and could incur more costs. The use of Vertex AI for training in this option is correct, but the process to get there is more complex and less cost-effective than option C.
The model you should choose is
✅ "C. The model with the highest recall where precision is greater than 0.5".
✅ Here's why:
❌ A. The model with the highest area under the receiver operating characteristic curve (AUC ROC) and precision greater than 0.5: While a high AUC ROC generally indicates a good model, it does not specifically prioritize detection of imminent failures (high recall) as required by the problem statement.
❌ B. The model with the lowest root mean squared error (RMSE) and recall greater than 0.5: RMSE is a regression metric, not a classification metric, so it's not applicable for a binary classification problem like predicting machine failure.
❌ D. The model with the highest precision where recall is greater than 0.5: This option prioritizes precision over recall. However, the problem statement indicates that detection of failures (high recall) is more important than avoiding false positives (high precision), given the high cost of a missed machine failure.
The best course of action would be
✅ "A. Add synthetic training data where those phrases are used in non-toxic ways".
✅ Here's why:
❌ B. Remove the model and replace it with human moderation: This option is likely to be costly and time-consuming, which is not suitable given the team's limited budget and overextended status. Additionally, it may not scale well as the online message board grows.
❌ C. Replace your model with a different text classifier: This option could potentially be costly and time-consuming, and there's no guarantee that a different model would perform better on the specific problem at hand.
❌ D. Raise the threshold for comments to be considered toxic or harmful: While this could potentially reduce the number of false positives, it might also increase the number of false negatives (toxic comments that are not flagged), thus potentially exacerbating the problem of toxic language and bullying on the message board.
The best course of action would be
✅ "C. Use BigQuery to calculate the descriptive statistics. Use Vertex AI Workbench user-managed notebooks to visualize the time plots and run the statistical analyses."
✅ Here's why:
❌ A. Visualize the time plots in Google Data Studio. Import the dataset into Vertex AI Workbench user-managed notebooks. Use this data to calculate the descriptive statistics and run the statistical analyses: Loading a large dataset into a notebook for calculation might be computationally expensive and time-consuming. Google Data Studio is not the best option for visualizing time plots when you need to conduct complex statistical analysis in the same environment.
❌ B. Spin up a Vertex AI Workbench user-managed notebooks instance and import the dataset. Use this data to create statistical and visual analyses: Importing the entire dataset into a notebook could be computationally intensive and may not be the most efficient way to perform descriptive statistics on a large dataset.
❌ D. Use BigQuery to calculate the descriptive statistics, and use Google Data Studio to visualize the time plots. Use Vertex AI Workbench user-managed notebooks to run the statistical analyses: While this method utilizes BigQuery for efficient calculation of descriptive statistics, using Google Data Studio for visualization could create a disjointed workflow as it separates the visualization from the statistical analysis process which is performed in the Vertex AI Workbench.
The most effective action to quickly lower the serving latency would be
✅ "A. Switch from CPU to GPU serving."
✅ Here's why:
❌ B. Apply quantization to your SavedModel by reducing the floating point precision to tf.float16: While quantization could potentially reduce the model size and improve the serving speed, it also might degrade the model performance. Moreover, it wouldn't be the first step to take as it requires additional work and expertise, compared to simply switching to GPU serving.
❌ C. Increase the dropout rate to 0.8 and retrain your model: While dropout can help to prevent overfitting during the training process, it does not have a direct impact on serving latency. Moreover, retraining the model is a time-consuming process and might not guarantee improved latency.
❌ D. Increase the dropout rate to 0.8 in _PREDICT mode by adjusting the TensorFlow Serving parameters: Dropout is not generally applied during prediction, as it can introduce uncertainty into the predictions. Increasing the dropout rate during prediction would not improve the serving latency.
The optimal method for reducing the sensitivity of the dataset before training your model would be
✅ "B. Use the Cloud Data Loss Prevention (DLP) API to scan for sensitive data, and use Dataflow with the DLP API to encrypt sensitive values with Format Preserving Encryption."
✅ Here's why:
❌ A. Using Dataflow, ingest the columns with sensitive data from BigQuery, and then randomize the values in each sensitive column: Randomizing the values in each sensitive column could potentially alter the inherent patterns in the data, which might negatively affect the performance of the trained model.
❌ C. Use the Cloud Data Loss Prevention (DLP) API to scan for sensitive data, and use Dataflow to replace all sensitive data by using the encryption algorithm AES-256 with a salt: This method does not preserve the format of the data. This could be problematic if the model needs the original format or pattern of the data to make accurate predictions.
❌ D. Before training, use BigQuery to select only the columns that do not contain sensitive data. Create an authorized view of the data so that sensitive values cannot be accessed by unauthorized individuals: This option is not feasible as the question states that every column is critical to the model. By ignoring the columns that contain sensitive data, we would be excluding potentially important information that the model needs to learn from.
The best solution when receiving this specific error would be to
✅ "B. Ensure that the required GPU is available in the selected region."
✅ Here's why:
❌ A. Ensure that you have GPU quota in the selected region: While it is essential to have enough GPU quota, the error message suggests that the GPU itself was not found in the region, not that there is a quota issue.
❌ C. Ensure that you have preemptible GPU quota in the selected region: This option is not relevant to the error message. The error doesn't pertain to preemptible GPU quota, but to the availability of a specific GPU in the chosen region.
❌ D. Ensure that the selected GPU has enough GPU memory for the workload: Although ensuring sufficient GPU memory is important for the workload, the error message indicates an issue with the availability of the GPU in the region, not with its memory capacity.