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| Section | Objectives |
|---|---|
| Topic 1: Monitoring ML solutions | - Performance monitoring and drift detection - Logging and alerting (Cloud Monitoring) - Model retraining strategies |
| Topic 2: Serving and scaling models | - Batch prediction - Online prediction (Vertex AI Prediction) - Hardware accelerators (GPU/TPU) in serving - Model optimization (Quantization, Distillation) |
| Topic 3: Architecting low-code ML solutions | - AutoML capabilities and implementation - Implementing BigQuery ML for basic models - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) |
| Topic 4: Scaling prototypes into ML models | - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) - Training at scale (Distributed training, TPUs) - Hyperparameter tuning |
| Topic 5: Collaborating within and across teams to manage data and models | - Data management and governance - Collaboration between Data Scientists, Data Engineers, and ML Engineers - Version control and reproducibility (e.g., DVC, MLOps) |
| Topic 6: Automating and orchestrating ML pipelines | - CI/CD for ML systems - Triggering and scheduling pipelines - Vertex AI Pipelines (Kubeflow Pipelines) |
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NEW QUESTION # 116
Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time. What should they use to track and report their experiments while minimizing manual effort?
Answer: D
Explanation:
AI Platform Training is a service that allows you to run your machine learning experiments on Google Cloud using various features, model architectures, and hyperparameters. You can use AI Platform Training to scale up your experiments, leverage distributed training, and access specialized hardware such as GPUs and TPUs 1
. Cloud Monitoring is a service that collects and analyzes metrics, logs, and traces from Google Cloud, AWS, and other sources. You can use Cloud Monitoring to create dashboards, alerts, and reports based on your data
2 . The Monitoring API is an interface that allows you to programmatically access and manipulate your monitoring data 3 .
By using AI Platform Training and Cloud Monitoring, you can track and report your experiments while minimizing manual effort. You can write the accuracy metrics from your experiments to Cloud Monitoring usin g the AI Platform Training Python package 4 . You can then query the results using the Monitoring API and compare the performance of different experiments. You can also visualize the metrics in the Cloud Console or create custom dashboards and alerts 5 . Therefore, using AI Platform Training and Cloud Monitoring is the best option for this use case.
References:
AI Platform Training documentation
Cloud Monitoring documentation
Monitoring API overview
Using Cloud Monitoring with AI Platform Training
Viewing evaluation metrics
NEW QUESTION # 117
A Data Science team within a large company uses Amazon SageMaker notebooks to access data stored in Amazon S3 buckets. The IT Security team is concerned that internet-enabled notebook instances create a security vulnerability where malicious code running on the instances could compromise data privacy. The company mandates that all instances stay within a secured VPC with no internet access, and data communication traffic must stay within the AWS network.
How should the Data Science team configure the notebook instance placement to meet these requirements?
Answer: D
NEW QUESTION # 118
You work for an online grocery store. You recently developed a custom ML model that recommends a recipe when a user arrives at the website. You chose the machine type on the Vertex AI endpoint to optimize costs by using the queries per second (QPS) that the model can serve, and you deployed it on a single machine with 8 vCPUs and no accelerators.
A holiday season is approaching and you anticipate four times more traffic during this time than the typical daily traffic. You need to ensure that the model can scale efficiently to the increased demand. What should you do?
Answer: A
NEW QUESTION # 119
You are an ML engineer at a bank that has a mobile application. Management has asked you to build an ML-based biometric authentication for the app that verifies a customer's identity based on their fingerprint.
Fingerprints are considered highly sensitive personal information and cannot be downloaded and stored into the bank databases. Which learning strategy should you recommend to train and deploy this ML model?
Answer: D
Explanation:
Federated learning is a machine learning technique that enables organizations to train AI models on decentralized data without centralizing or sharing it1. It allows data privacy, continual learning, and better performance on end-user devices2. Federated learning works by sending the model parameters to the devices, where they are updated locally on the device's data, and then aggregating the updated parameters on a central server to form a global model3. This way, the data never leaves the device and the model can learn from a large and diverse dataset.
Federated learning is suitable for the use case of building an ML-based biometric authentication for the bank's mobile app that verifies a customer's identity based on their fingerprint. Fingerprints are considered highly sensitive personal information and cannot be downloaded and stored into the bank databases. By using federated learning, the bank can train and deploy an ML model that can recognize fingerprints without compromising the data privacy of the customers. The model can also adapt to the variations and changes in the fingerprints over time and improve its accuracy and reliability. Therefore, federated learning is the best learning strategy for this use case.
NEW QUESTION # 120
You developed a Transformer model in TensorFlow to translate text Your training data includes millions of documents in a Cloud Storage bucket. You plan to use distributed training to reduce training time. You need to configure the training job while minimizing the effort required to modify code and to manage the clusters configuration. What should you do?
Answer: D
Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to "configure and optimize model training jobs". Cloud TPU VMs2 are a new way to access Cloud TPUs directly on the TPU host machines, offering a simpler and more flexible user experience. Cloud TPU VMs are optimized for ML model training and can reduce training time and cost. You can use Cloud TPU VMs to train Transformer models in TensorFlow by using the tf.distribute.TPUStrategy3, which handles the distribution of computations across the TPU cores. The other options are not relevant or optimal for this scenario. Reference:
Professional ML Engineer Exam Guide
Cloud TPU VMs
Distributed training with TPUStrategy
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
NEW QUESTION # 121
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