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| Section | Objectives |
|---|---|
| Collaborating within and across teams to manage data and models | - Data management and governance - Version control and reproducibility (e.g., DVC, MLOps) - Collaboration between Data Scientists, Data Engineers, and ML Engineers |
| Serving and scaling models | - Online prediction (Vertex AI Prediction) - Batch prediction - Model optimization (Quantization, Distillation) - Hardware accelerators (GPU/TPU) in serving |
| Monitoring ML solutions | - Model retraining strategies - Logging and alerting (Cloud Monitoring) - Performance monitoring and drift detection |
| Automating and orchestrating ML pipelines | - Triggering and scheduling pipelines - CI/CD for ML systems - Vertex AI Pipelines (Kubeflow Pipelines) |
| Scaling prototypes into ML models | - Training at scale (Distributed training, TPUs) - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) - Hyperparameter tuning |
| 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) |
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NEW QUESTION # 381
A Machine Learning Specialist uploads a dataset to an Amazon S3 bucket protected with server-side encryption using AWS KMS.
How should the ML Specialist define the Amazon SageMaker notebook instance so it can read the same dataset from Amazon S3?
Answer: D
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/encryption-at-rest.html
NEW QUESTION # 382
You are developing a custom TensorFlow classification model based on tabular data. Your raw data is stored in BigQuery. contains hundreds of millions of rows, and includes both categorical and numerical features. You need to use a MaxMin scaler on some numerical features, and apply a one-hot encoding to some categorical features such as SKU names. Your model will be trained over multiple epochs. You want to minimize the effort and cost of your solution. What should you do?
Answer: C
NEW QUESTION # 383
You are working with a dataset that contains customer transactions. You need to build an ML model to predict customer purchase behavior. You plan to develop the model in BigQuery ML, and export it to Cloud Storage for online prediction. You notice that the input data contains a few categorical features, including product category and payment method. You want to deploy the model as quickly as possible. What should you do?
Answer: B
NEW QUESTION # 384
You recently developed a wide and deep model in TensorFlow. You generated training datasets using a SQL script that preprocessed raw data in BigQuery by performing instance-level transformations of the data. You need to create a training pipeline to retrain the model on a weekly basis. The trained model will be used to generate daily recommendations. You want to minimize model development and training time. How should you develop the training pipeline?
Answer: D
Explanation:
* Explanation: TensorFlow Extended (TFX) is a platform for building end-to-end machine learning pipelines using TensorFlow. TFX provides a set of components that can be orchestrated using either the TFX SDK or Kubeflow Pipelines. TFX components can handle different aspects of the pipeline, such as data ingestion, data validation, data transformation, model training, model evaluation, model serving, and more. TFX components can also leverage other Google Cloud services, such as BigQuery, Dataflow, and Vertex AI.
* Why not A: Using the Kubeflow Pipelines SDK to implement the pipeline is a valid option, but using the BigQueryJobOp component to run the preprocessing script is not optimal. This would require writing and maintaining a separate SQL script for data transformation, which could introduce inconsistencies and errors. It would also make it harder to reuse the same preprocessing logic for both training and serving.
* Why not B: Using the Kubeflow Pipelines SDK to implement the pipeline is a valid option, but using the DataflowPythonJobOp component to preprocess the data is not optimal. This would require writing and maintaining a separate Python script for data transformation, which could introduce inconsistencies and errors. It would also make it harder to reuse the same preprocessing logic for both training and serving.
* Why not D: Using the TensorFlow Extended SDK to implement the pipeline is a valid option, but implementing the preprocessing steps as part of the input_fn of the model is not optimal. This would
* make the preprocessing logic tightly coupled with the model code, which could reduce modularity and flexibility. It would also make it harder to reuse the same preprocessing logic for both training and serving.
NEW QUESTION # 385
Your data science team is training a PyTorch model for image classification based on a pre-trained RestNet model. You need to perform hyperparameter tuning to optimize for several parameters. What should you do?
Answer: A
Explanation:
AI Platform supports hyperparameter tuning for PyTorch models using custom containers. This allows you to use any Python dependencies and libraries that are not included in the pre-built AI Platform Training runtime versions. You can also use a pre-trained model such as ResNet as a base for your custom model. To run a hyperparameter tuning job on AI Platform using custom containers, you need to do the following steps:
Create a Dockerfile that defines the container image for your training application. The Dockerfile should install PyTorch and any other dependencies, copy your training code and configuration files, and set the entrypoint for the container.
Build the container image and push it to Container Registry or another accessible registry.
Create a YAML file that defines the configuration for your hyperparameter tuning job. The YAML file should specify the container image URI, the training input and output paths, the hyperparameters to tune, the metric to optimize, and the tuning algorithm and budget.
Submit the hyperparameter tuning job to AI Platform using the gcloud command-line tool or the AI Platform Training API.
Reference:
Hyperparameter tuning overview
Using custom containers
PyTorch on AI Platform Training
NEW QUESTION # 386
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