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| Section | Objectives |
|---|---|
| Scaling prototypes into ML models | - Hyperparameter tuning - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) - Training at scale (Distributed training, TPUs) |
| Automating and orchestrating ML pipelines | - CI/CD for ML systems - Vertex AI Pipelines (Kubeflow Pipelines) - Triggering and scheduling pipelines |
| Collaborating within and across teams to manage data and models | - Version control and reproducibility (e.g., DVC, MLOps) - Collaboration between Data Scientists, Data Engineers, and ML Engineers - Data management and governance |
| Monitoring ML solutions | - Logging and alerting (Cloud Monitoring) - Performance monitoring and drift detection - Model retraining strategies |
| 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) |
| Serving and scaling models | - Model optimization (Quantization, Distillation) - Online prediction (Vertex AI Prediction) - Hardware accelerators (GPU/TPU) in serving - Batch prediction |
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NEW QUESTION # 245
While performing exploratory data analysis on a dataset, you find that an important categorical feature has 5% null values. You want to minimize the bias that could result from the missing values. How should you handle the missing values?
Answer: C
Explanation:
This approach is often referred to as "imputing" missing values, and it is a common technique for dealing with missing data in categorical features. By using a placeholder category, you explicitly indicate that the value is missing, rather than assuming that the missing value is a particular category. This can help to minimize bias in downstream analyses, as it does not introduce any assumptions about the missing data that could bias your results.
NEW QUESTION # 246
You have recently created a proof-of-concept (POC) deep learning model. You are satisfied with the overall architecture, but you need to determine the value for a couple of hyperparameters. You want to perform hyperparameter tuning on Vertex AI to determine both the appropriate embedding dimension for a categorical feature used by your model and the optimal learning rate. You configure the following settings:
For the embedding dimension, you set the type to INTEGER with a minValue of 16 and maxValue of 64.
For the learning rate, you set the type to DOUBLE with a minValue of 10e-05 and maxValue of 10e-02.
You are using the default Bayesian optimization tuning algorithm, and you want to maximize model accuracy.
Training time is not a concern. How should you set the hyperparameter scaling for each hyperparameter and the maxParallelTrials?
Answer: D
Explanation:
The best option for performing hyperparameter tuning on Vertex AI to determine the appropriate embedding dimension and the optimal learning rate is to use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a large number of parallel trials. This option has the following advantages:
* It matches the appropriate scaling type for each hyperparameter, based on their range and distribution.
The embedding dimension is an integer hyperparameter that varies linearly between 16 and 64, so using UNIT_LINEAR_SCALE makes sense. The learning rate is a double hyperparameter that varies exponentially between 10e-05 and 10e-02, so using UNIT_LOG_SCALE is more suitable.
* It maximizes the exploration of the hyperparameter space, by using a large number of parallel trials.
Since training time is not a concern, using more trials can help find the best combination of hyperparameters that maximizes model accuracy. The default Bayesian optimization tuning algorithm can efficiently sample the hyperparameter space and converge to the optimal values.
The other options are less optimal for the following reasons:
* Option B: Using UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a small number of parallel trials, reduces the exploration of the hyperparameter space, by using a small number of parallel trials. Since training time is not a concern, using fewer trials can miss some potentially good combinations of hyperparameters that maximize model accuracy. The default Bayesian optimization tuning algorithm can benefit from more trials to sample the hyperparameter space and converge to the optimal values.
* Option C: Using UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a large number of parallel trials, mismatches the appropriate scaling type for each hyperparameter, based on their range and distribution. The embedding dimension is an integer hyperparameter that varies linearly between 16 and 64, so using UNIT_LOG_SCALE is not suitable.
The learning rate is a double hyperparameter that varies exponentially between 10e-05 and 10e-02, so using UNIT_LINEAR_SCALE makes less sense.
* Option D: Using UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a small number of parallel trials, combines the drawbacks of option B and option C. It mismatches the appropriate scaling type for each hyperparameter, based on their range and distribution, and reduces the exploration of the hyperparameter space, by using a small number of parallel trials.
:
[Vertex AI: Hyperparameter tuning overview]
[Vertex AI: Configuring the hyperparameter tuning job]
NEW QUESTION # 247
You have trained a text classification model in TensorFlow using AI Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead.
What should you do?
Answer: A
Explanation:
https://cloud.google.com/bigquery-ml/docs/making-predictions-with-imported-tensorflow-models#importing_models
NEW QUESTION # 248
You need to deploy a scikit-learn classification model to production. The model must be able to serve requests
24/7 and you expect millions of requests per second to the production application from 8 am to 7 pm. You need to minimize the cost of deployment What should you do?
Answer: D
Explanation:
The best option for deploying a scikit-learn classification model to production is to deploy an online Vertex AI prediction endpoint and set the max replica count to 100. This option allows you to leverage the power and scalability of Google Cloud to serve requests 24/7 and handle millions of requests per second. Vertex AI is a unified platform for building and deploying machine learning solutions on Google Cloud. Vertex AI can deploy a trained scikit-learn model to an online prediction endpoint, which can provide low-latency predictions for individual instances. An online prediction endpoint consists of one or more replicas, which are copies of the model that run on virtual machines. The max replica count is a parameter that determines the maximum number of replicas that can be created for the endpoint. By setting the max replica count to 100, you can enable the endpoint to scale up to 100 replicas when the traffic increases, and scale down to zero replicas when the traffic decreases. This can help minimize the cost of deployment, as you only pay for the resources that you use. Moreover, you can use the autoscaling algorithm option to optimize the scaling behavior of the endpoint based on the latency and utilization metrics 1 .
The other options are not as good as option B, for the following reasons:
* Option A: Deploying an online Vertex AI prediction endpoint and setting the max replica count to 1 would not be able to serve requests 24/7 and handle millions of requests per second. Setting the max replica count to 1 would limit the endpoint to only one replica, which can cause performance issues and service disruptions when the traffic increases. Moreover, se tting the max replica count to 1 would prevent the endpoint from scaling down to zero replicas when the traffic decreases, which can increase the cost of deployment, as you pay for the resources that you do not use 1 .
* Option C: Deploying an online Vertex AI prediction endpoint with one GPU per replica and setting the max replica count to 1 would not be able to serve requests 24/7 and handle millions of requests per second, and would increase the cost of deployment. Adding a GPU to each replica would increase the computational power of the endpoint, but it would also in crease the cost of deployment, as GPUs are more expensive than CPUs. Moreover, setting the max replica count to 1 would limit the endpoint to only one replica, which can cause performance issues and service disruptions whe n the traffic increases, and prevent the endpoint from scaling down to zero replicas when the traffic decreases 1
. Furthermore, scikit-learn models do not benefit from GPUs, as scikit-learn is not optimized for GPU acceleration 2 .
* Option D: Deploying an online Vertex AI prediction endpoint with one GPU per replica and setting the max replica count to 100 would be able to serve requests 24/7 and handle millions of requests per second, but it would increase the cost of deployment. Adding a GPU to each replica would increase the computational power of the endpoint, but it would also increase the cost of deployment, as GPUs are more expensive than CPUs. Setting the max replica count to 100 would enable the endpoint to scale up to 100 replicas when the traffic increases, and scale down to zero replicas when the traffic decreases, which can help minimize the cost of deployment. However , scikit-learn models do not benefit from GPUs, as scikit-learn is not optimized for GPU acceleration 2 . Therefore, using GPUs for scikit-learn models would be unnecessary and wasteful.
References:
Preparing for Google Cloud Certification: Machine Learning Engineer , Course 3: Production ML Systems, Week 2: Serving ML Predictions Google Cloud Professional Machine Learning Engineer Exam Guide , Section 3: Scaling ML models in production, 3.1 Deploying ML models to production Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 6: Production ML Systems, Section 6.2: Serving ML Predictions Online prediction Scaling online prediction scikit-learn FAQ
NEW QUESTION # 249
A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a machine learning specialist will build a binary classifier based on two features: age of account, denoted by x, and transaction month, denoted by y. The class distributions are illustrated in the provided figure. The positive class is portrayed in red, while the negative class is portrayed in black.
Which model would have the HIGHEST accuracy?
Answer: C
NEW QUESTION # 250
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