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| Section | Objectives |
|---|---|
| Collaborating within and across teams to manage data and models | - Collaboration between Data Scientists, Data Engineers, and ML Engineers - Data management and governance - Version control and reproducibility (e.g., DVC, MLOps) |
| Serving and scaling models | - Batch prediction - Model optimization (Quantization, Distillation) - Online prediction (Vertex AI Prediction) - Hardware accelerators (GPU/TPU) in serving |
| Monitoring ML solutions | - Logging and alerting (Cloud Monitoring) - Model retraining strategies - Performance monitoring and drift detection |
| Automating and orchestrating ML pipelines | - Vertex AI Pipelines (Kubeflow Pipelines) - Triggering and scheduling pipelines - CI/CD for ML systems |
| 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) |
| Scaling prototypes into ML models | - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) - Training at scale (Distributed training, TPUs) - Hyperparameter tuning |
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NEW QUESTION # 326
A company is observing low accuracy while training on the default built-in image classification algorithm in Amazon SageMaker. The Data Science team wants to use an Inception neural network architecture instead of a ResNet architecture.
Which of the following will accomplish this? (Choose two.)
Answer: A,C
NEW QUESTION # 327
You lead an ML team at a financial technology company. Your team is building a system to detect potentially fraudulent credit card transactions in real-time. The model must return a prediction with very low latency (under 200ms) to approve or deny the transaction. The system must also scale to handle millions of transactions per day. Your dataset consists of structured, tabular transaction data stored in BigQuery. Your team is proficient in data science but wants to avoid the complexity of managing and optimizing a real-time model serving infrastructure. What should you do?
Answer: A
Explanation:
Agent Platform AutoML can train an accurate classification model directly from structured BigQuery data while minimizing custom model-development effort. Deploying the trained model to a managed Agent Platform endpoint provides low-latency online predictions, autoscaling, and managed serving infrastructure suitable for millions of real-time transaction requests.
NEW QUESTION # 328
You are working on a prototype of a text classification model in a managed Vertex AI Workbench notebook.
You want to quickly experiment with tokenizing text by using a Natural Language Toolkit (NLTK) library.
How should you add the library to your Jupyter kernel?
Answer: C
NEW QUESTION # 329
You work for a gaming company that develops massively multiplayer online (MMO) games. You built a TensorFlow model that predicts whether players will make in-app purchases of more than $10 in the next two weeks. The model's predictions will be used to adapt each user's game experience. User data is stored in BigQuery. How should you serve your model while optimizing cost, user experience, and ease of management?
Answer: A
Explanation:
The best option to serve the model while optimizing cost, user experience, and ease of management is to deploy the model to Vertex AI Prediction, which is a managed service that can scale up or down according to the demand and provide low latency and high availability. Vertex AI Prediction can also handle TensorFlow models natively, without requiring any additional steps or conversions. By using batch prediction, the model can process large volumes of data efficiently and periodically, without affecting the user experience. The data can be read from Cloud Bigtable, which is a scalable and performant NoSQL database that can store user data in a flexible schema. The predictions can then be pushed to Cloud SQL, which is a fully managed relational database that can store the predictions in a structured format and enable easy querying and analysis. This option also simplifies the management of the model and the data, as it leverages the existing Google Cloud services and does not require any additional infrastructure or code.
The other options are not optimal for the following reasons:
* A. Importing the model into BigQuery ML is not a good option, as it requires converting the TensorFlow model into a format that BigQuery ML can understand, which can introduce errors and reduce the performance. Moreover, BigQuery ML is not designed for serving real-time predictions, but rather for training and evaluating models using SQL queries. Reading and writing data from BigQuery and Cloud SQL can also incur additional costs and latency, as they are both relational databases that require schema definition and data transformation.
* C. Embedding the model in the mobile application is not a good option, as it increases the size and complexity of the application, and requires updating the application every time the model changes.
Moreover, it exposes the model to the users, which can pose security and privacy risks, as well as potential misuse or abuse. Additionally, it does not leverage the benefits of the cloud, such as scalability, reliability, and performance.
* D. Embedding the model in the streaming Dataflow pipeline is not a good option, as it requires building and maintaining a custom pipeline that can handle the model inference and data processing. This can increase the development and operational costs and complexity, as well as the potential for errors and failures. Moreover, it does not take advantage of the batch prediction feature of Vertex AI Prediction, which can optimize the resource utilization and cost efficiency.
References:
* Professional ML Engineer Exam Guide
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
* Google Cloud launches machine learning engineer certification
* Vertex AI Prediction documentation
* Cloud Bigtable documentation
* Cloud SQL documentation
NEW QUESTION # 330
You work for a gaming company that manages a popular online multiplayer game where teams with 6 players play against each other in 5-minute battles. There are many new players every day. You need to build a model that automatically assigns available players to teams in real time. User research indicates that the game is more enjoyable when battles have players with similar skill levels. Which business metrics should you track to measure your model's performance? (Choose One Correct Answer)
Answer: D
Explanation:
The best business metric to track to measure the model's performance is user engagement as measured by the number of battles played daily per user. This metric reflects the main goal of the model, which is to enhance the user experience and satisfaction by creating balanced and fair battles. If the model is successful, it should increase the user retention and loyalty, as well as the word-of-mouth and referrals. This metric is also easy to measure and interpret, as it can be directly obtained from the user activity data.
The other options are not optimal for the following reasons:
* A. Average time players wait before being assigned to a team is not a good metric, as it does not capture the quality or outcome of the battles. It only measures the efficiency of the model, which is not the primary objective. Moreover, this metric can be influenced by external factors, such as the availability and demand of players, the network latency, and the server capacity.
* B. Precision and recall of assigning players to teams based on their predicted versus actual ability is not a good metric, as it is difficult to measure and interpret. It requires having a reliable and consistent way of estimating the player's ability, which can be subjective and dynamic. It also requires having a ground truth label for each assignment, which can be costly and impractical to obtain. Moreover, this metric does not reflect the user feedback or satisfaction, which is the ultimate goal of the model.
* D. Rate of return as measured by additional revenue generated minus the cost of developing a new model is not a good metric, as it is not directly related to the model's performance. It measures the
* profitability of the model, which is a secondary objective. Moreover, this metric can be affected by many other factors, such as the market conditions, the pricing strategy, the marketing campaigns, and the competition.
References:
* Professional ML Engineer Exam Guide
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
* Google Cloud launches machine learning engineer certification
* How to measure user engagement
* How to choose the right metrics for your machine learning model
NEW QUESTION # 331
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