Passing exam with Professional-Machine-Learning-Engineer latest training materials

You need to write a generic test to verify whether Dense Neural Network (DNN) models automatically released by your team have a sufficient number of parameters to learn the task for which they were built. What should you do?

• A. Train the model for a few iterations, and verify that the loss is constant.
• B. Train a simple linear model, and determine if the DNN model outperforms it.
• C. Train the model with no regularization, and verify that the loss function is close to zero.
• D. Train the model for a few iterations, and check for NaN values.

You are developing a natural language processing model that analyzes customer feedback to identify positive, negative, and neutral experiences. During the testing phase, you notice that the model demonstrates a significant bias against certain demographic groups, leading to skewed analysis results. You want to address this issue following Google's responsible AI practices. What should you do?

• A. Implement a more complex model architecture that can capture nuanced patterns in language to reduce bias.
• B. Use Vertex Explainable AI to generate explanations and systematically adjust the predictions to address identified biases.
• C. Audit the training dataset to identify underrepresented groups and augment the dataset with additional samples before retraining the model.
• D. Use Vertex AI's model evaluation to assess bias in the model's predictions, and use post- processing to adjust outputs for identified demographic discrepancies.

You built a custom Vertex AI pipeline job that preprocesses images and trains an object detection model. The pipeline currently uses 1 n1-standard-8 machine with 1 NVIDIA Tesla V100 GPU.
You want to reduce the model training time without compromising model accuracy. What should you do?

• A. Update the WorkerPoolSpecto use a machine with 24 vCPUs and 3 NVIDIA Tesla V100 GPUs.
• B. Reduce the number of layers in your object detection model.
• C. Update the WorkerPoolSpecto use a machine with 24 vCPUs and 1 NVIDIA Tesla V100 GPU.
• D. Train the same model on a stratified subset of your dataset.

You work at an organization that maintains a cloud-based communication platform that integrates conventional chat, voice, and video conferencing into one platform. The audio recordings are stored in Cloud Storage. All recordings have an 8 kHz sample rate and are more than one minute long. You need to implement a new feature in the platform that will automatically transcribe voice call recordings into a text for future applications, such as call summarization and sentiment analysis. How should you implement the voice call transcription feature following Google- recommended best practices?

• A. Upsample the audio recordings to 16 kHz, and transcribe the audio by using the Speech-to-Text API with asynchronous recognition.
• B. Upsample the audio recordings to 16 kHz, and transcribe the audio by using the Speech-to-Text API with synchronous recognition.
• C. Use the original audio sampling rate, and transcribe the audio by using the Speech-to-Text API with synchronous recognition.
• D. Use the original audio sampling rate, and transcribe the audio by using the Speech-to-Text API with asynchronous recognition.

You work at a bank. You have a custom tabular ML model that was provided by the bank's vendor. The training data is not available due to its sensitivity. The model is packaged as a Vertex AI Model serving container, which accepts a string as input for each prediction instance. In each string, the feature values are separated by commas. You want to deploy this model to production for online predictions and monitor the feature distribution over time with minimal effort. What should you do?

• A. 1. Refactor the serving container to accept key-value pairs as input format
  2. Upload the model to Vertex AI Model Registry, and deploy the model to a Vertex AI endpoint
  3. Create a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective.
• B. 1. Refactor the serving container to accept key-value pairs as input format
  2. Upload the model to Vertex AI Model Registry, and deploy the model to a Vertex AI endpoint
  3. Create a Vertex AI Model Monitoring job with feature skew detection as the monitoring objective
• C. 1. Upload the model to Vertex AI Model Registry, and deploy the model to a Vertex AI endpoint
  2. Create a Vertex AI Model Monitoring job with feature skew detection as the monitoring objective, and provide an instance schema
• D. 1. Upload the model to Vertex AI Model Registry, and deploy the model to a Vertex AI endpoint
  2. Create a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective, and provide an instance schema