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| Section | Objectives |
|---|---|
| Architecting low-code ML solutions | - Implementing BigQuery ML for basic models - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) - AutoML capabilities and implementation |
| Serving and scaling models | - Batch prediction - Model optimization (Quantization, Distillation) - Online prediction (Vertex AI Prediction) - Hardware accelerators (GPU/TPU) in serving |
| Monitoring ML solutions | - Model retraining strategies - Performance monitoring and drift detection - Logging and alerting (Cloud Monitoring) |
| Collaborating within and across teams to manage data and models | - Collaboration between Data Scientists, Data Engineers, and ML Engineers - Version control and reproducibility (e.g., DVC, MLOps) - Data management and governance |
| Automating and orchestrating ML pipelines | - Triggering and scheduling pipelines - Vertex AI Pipelines (Kubeflow Pipelines) - CI/CD for ML systems |
| Scaling prototypes into ML models | - Training at scale (Distributed training, TPUs) - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) - Hyperparameter tuning |
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NEW QUESTION # 306
You work for a food product company. Your company's historical sales data is stored in BigQuery You need to use Vertex Al's custom training service to train multiple TensorFlow models that read the data from BigQuery and predict future sales You plan to implement a data preprocessing algorithm that performs min- max scaling and bucketing on a large number of features before you start experimenting with the models. You want to minimize preprocessing time, cost and development effort How should you configure this workflow?
Answer: D
Explanation:
The best option for configuring the workflow is to add the transformations as a preprocessing layer in the TensorFlow models. This option allows you to leverage the power and simplicity of TensorFlow to preprocess and transform the data with simple Python code. TensorFlow is a framework for building and training machine learning models. TensorFlow provides various tools and libraries for data analysis and machine learning. A preprocessing layer is a type of layer in TensorFlow that can perform data preprocessing and feature engineering operations on the input data. A preprocessing layer can help you customize the data transformation and preprocessing logic, and handle complex or non-standard data formats. A preprocessing layer can also help you minimize the preprocessing time, cost, and development effort, as you only need to write a few lines of code to implement the preprocessing layer, and you do not need to create any intermediate data sources or pipelines. By adding the transformations as a preprocessing layer in the TensorFlow models, you can use Vertex AI's custom training service to train multiple TensorFlow models that read the data from BigQuery and predict future sales1.
The other options are not as good as option C, for the following reasons:
* Option A: Writing the transformations into Spark that uses the spark-bigquery-connector and using Dataproc to preprocess the data would require more skills and steps than using a preprocessing layer in TensorFlow. Spark is a framework for distributed data processing and machine learning. Spark can read and write data from BigQuery by using the spark-bigquery-connector, which is a library that allows Spark to communicate with BigQuery. Dataproc is a service that can create and manage Spark clusters on Google Cloud. Dataproc can help you run Spark jobs on Google Cloud, and scale the clusters according to the workload. However, writing the transformations into Spark that uses the spark- bigquery-connector and using Dataproc to preprocess the data would require more skills and steps than using a preprocessing layer in TensorFlow. You would need to write code, create and configure the Spark cluster, install and import the spark-bigquery-connector, load and preprocess the data, and write the data back to BigQuery. Moreover, this option would create an intermediate data source in BigQuery, which can increase the storage and computation costs2.
* Option B: Writing SQL queries to transform the data in-place in BigQuery would not allow you to use Vertex AI's custom training service to train multiple TensorFlow models that read the data from BigQuery and predict future sales. BigQuery is a service that can perform data analysis and machine learning by using SQL queries. BigQuery can perform data transformation and preprocessing by using SQL functions and clauses, such as MIN, MAX, CASE, and TRANSFORM. BigQuery can also perform machine learning by using BigQuery ML, which is a feature that can create and train machine learning models by using SQL queries. However, writing SQL queries to transform the data in-place in BigQuery would not allow you to use Vertex AI's custom training service to train multiple TensorFlow models that read the data from BigQuery and predict future sales. Vertex AI's custom training service is a service that can run your custom machine learning code on Vertex AI. Vertex AI's custom training service can support various machine learning frameworks, such as TensorFlow, PyTorch, and scikit- learn. Vertex AI's custom training service cannot support SQL queries, as SQL is not a machine learning framework. Therefore, if you want to use Vertex AI's custom training service, you cannot use SQL queries to transform the data in-place in BigQuery3.
* Option D: Creating a Dataflow pipeline that uses the BigQueryIO connector to ingest the data, process it, and write it back to BigQuery would require more skills and steps than using a preprocessing layer in TensorFlow. Dataflow is a service that can create and run data processing and machine learning pipelines on Google Cloud. Dataflow can read and write data from BigQuery by using the BigQueryIO connector, which is a library that allows Dataflow to communicate with BigQuery. Dataflow can perform data transformation and preprocessing by using Apache Beam, which is a framework for distributed data processing and machine learning. However, creating a Dataflow pipeline that uses the BigQueryIO connector to ingest the data, process it, and write it back to BigQuery would require more skills and steps than using a preprocessing layer in TensorFlow. You would need to write code, create and configure the Dataflow pipeline, install and import the BigQueryIO connector, load and preprocess the data, and write the data back to BigQuery. Moreover, this option would create an intermediate data source in BigQuery, which can increase the storage and computation costs4.
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 2: Developing ML models, 2.1 Developing ML models by using TensorFlow
* Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 4:
Developing ML Models, Section 4.1: Developing ML Models by Using TensorFlow
* TensorFlow Preprocessing Layers
* Spark and BigQuery
* Dataproc
* BigQuery ML
* Dataflow and BigQuery
* Apache Beam
NEW QUESTION # 307
You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent. Which data transformation strategy would likely improve the performance of your classifier?
Answer: B
Explanation:
Oversampling is a technique for dealing with imbalanced datasets, where the majority class dominates the minority class. It balances the distribution of classes by increasing the number of samplesin the minority class.
Oversampling can improve the performance of a classifier by reducing the bias towards the majority class and increasing the sensitivity to the minority class.
In this case, the dataset includes transactions, of which 1% are identified as fraudulent. This means that the fraudulent transactions are the minority class and the non-fraudulent transactions are the majority class. A random forest model trained on this dataset might have a low recall for the fraudulent transactions, meaning that it might miss many of them and fail to detect fraud. This could have a high cost for the bank and its customers.
One way to overcome this problem is to oversample the fraudulent transactions 10 times, meaning that each fraudulent transaction is duplicated 10 times in the training dataset. This would increase the proportion of fraudulent transactions from 1% to about 10%, making the dataset more balanced. This would also make the random forest model more aware of the patterns and features that distinguish fraudulent transactions from non-fraudulent ones, and thus improve its accuracy and recall for the minority class.
For more information about oversampling and other techniques for imbalanced data, see the following references:
* Random Oversampling and Undersampling for Imbalanced Classification
* Exploring Oversampling Techniques for Imbalanced Datasets
NEW QUESTION # 308
Your team has been tasked with creating an ML solution in Google Cloud to classify support requests for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the classifier so that you have full control of the model's code, serving, and deployment. You will use Kubeflow pipelines for the ML platform. To save time, you want to build on existing resources and use managed services instead of building a completely new model. How should you build the classifier?
Answer: C
Explanation:
Transfer learning is a technique that leverages the knowledge and weights of a pre-trained model and adapts them to a new task or domain1. Transfer learning can save time and resources by avoiding training a model from scratch, and can also improve the performance and generalization of the model by using a larger and more diverse dataset2. AI Platform provides several established text classification models that can be used for transfer learning, such as BERT, ALBERT, or XLNet3. These models are based on state-of-the-art natural language processing techniques and can handle various text classification tasks, such as sentiment analysis, topic classification, or spam detection4. By using one of these models on AI Platform, you can customize the model's code, serving, and deployment, and use Kubeflow pipelines for the ML platform. Therefore, using an established text classification model on AI Platform to perform transfer learning is the best option for this use case.
References:
* Transfer Learning - Machine Learning's Next Frontier
* A Comprehensive Hands-on Guide to Transfer Learning with Real-World Applications in Deep Learning
* Text classification models
* Text Classification with Pre-trained Models in TensorFlow
NEW QUESTION # 309
You are working on a Neural Network-based project. The dataset provided to you has columns with different ranges. While preparing the data for model training, you discover that gradient optimization is having difficulty moving weights to a good solution. What should you do?
Answer: B
NEW QUESTION # 310
You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation dat a. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?
Answer: C
NEW QUESTION # 311
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