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問題 #196
You work at a leading healthcare firm developing state-of-the-art algorithms for various use cases You have unstructured textual data with custom labels You need to extract and classify various medical phrases with these labels What should you do?

• A. Use a BERT-based model to fine-tune a medical entity extraction model.
• B. Use TensorFlow to build a custom medical entity extraction model.
• C. Use the Healthcare Natural Language API to extract medical entities.
• D. Use AutoML Entity Extraction to train a medical entity extraction model.

答案：A

解題說明：
Medical entity extraction is a task that involves identifying and classifying medical terms or concepts from unstructured textual data, such as electronic health records, clinical notes, or research papers. Medical entity extraction can help with various use cases, such as information retrieval, knowledge discovery, decision support, and data analysis1.
One possible approach to perform medical entity extraction is to use a BERT-based model to fine-tune a medical entity extraction model. BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained language model that can capture the contextual information from both left and right sides of a given token2. BERT can be fine-tuned on a specific downstream task, such as medical entity extraction, by adding a task-specific layer on top of the pre-trained model and updating the model parameters with a small amount of labeled data3.
A BERT-based model can achieve high performance on medical entity extraction by leveraging the large-scale pre-training on general-domain corpora and the fine-tuning on domain-specific data. Forexample, Nesterov and Umerenkov4 proposed a novel method of doing medical entity extraction from electronic health records as a single-step multi-label classification task by fine-tuning a transformer model pre-trained on a large EHR dataset. They showed that their model can achieve human-level quality for most frequent entities.
References:
* 1: Medical Named Entity Recognition from Un-labelled Medical Records based on Pre-trained Language Models and Domain Dictionary | Data Intelligence | MIT Press
* 2: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
* 3: Fine-tuning BERT for Medical Entity Extraction
* 4: Distantly supervised end-to-end medical entity extraction from electronic health records with human-level quality

 

問題 #197
You built a custom ML model using scikit-learn. Training time is taking longer than expected. You decide to migrate your model to Vertex AI Training, and you want to improve the model's training time. What should you try out first?

• A. Migrate your model to TensorFlow, and train it using Vertex AI Training.
• B. Train your model using Vertex AI Training with GPUs.
• C. Train your model with DLVM images on Vertex AI, and ensure that your code utilizes NumPy and SciPy internal methods whenever possible.
• D. Train your model in a distributed mode using multiple Compute Engine VMs.

答案：B

解題說明：
* Option A is incorrect because migrating your model to TensorFlow, and training it using Vertex AI Training, is not the easiest way to improve the model's training time. TensorFlow is a framework that allows you to create and train ML models using Python or other languages. Vertex AI Training is a service that allows you to train and optimize ML models using built-in algorithms or custom containers.
However, this option requires significant code changes, as TensorFlow and scikit-learn have different APIs and functionalities. Moreover, this option does not leverage the parallelism or the scalability of the cloud, as it only uses a single instance.
* Option B is incorrect because training your model in a distributed mode using multiple Compute Engine VMs, is not the most convenient way to improve the model's training time. Compute Engine is a service that allows you to create and manage virtual machines that run on Google Cloud. You can use Compute Engine to run your scikit-learn model in a distributed mode, by using libraries such as Dask or Joblib.
However, this option requires more effort and resources than option D, as it involves creating and configuring the VMs, installing and maintaining the libraries, and writing and running the distributed code.
* Option C is incorrect because training your model with DLVM images on Vertex AI, and ensuring that your code utilizes NumPy and SciPy internal methods whenever possible, is not the most effective way to improve the model's training time. DLVM (Deep Learning Virtual Machine) images are preconfigured VM images that include popular ML frameworks and tools, such as TensorFlow, PyTorch, or scikit-learn1. You can use DLVM images on Vertex AI to train your scikit-learn model, by using a custom container. NumPy and SciPy are libraries that provide numerical and scientific computing functionalities for Python. You can use NumPy and SciPy internal methods to optimize your scikit-learn code, as they are faster and more efficient than pure Python code2. However, this option does not leverage the parallelism or the scalability of the cloud, as it only uses a single instance. Moreover, this option may not have a significant impact on the training time, as scikit-learn already relies on NumPy and SciPy for most of its operations3.
* Option D is correct because training your model using Vertex AI Training with GPUs, is the best way to improve the model's training time. A GPU (Graphics Processing Unit) is a hardware accelerator that can perform parallel computations faster than a CPU (Central Processing Unit)4. Vertex AI Training is a service that allows you to train and optimize ML models using built-in algorithms or custom containers. You can use Vertex AI Training with GPUs to train your scikit-learn model, by using a custom container and specifying the accelerator type and count5. By using Vertex AI Training with GPUs, you can leverage the parallelism and the scalability of the cloud, and speed up the training process significantly, without changing your code.
References:
* DLVM images
* NumPy and SciPy
* scikit-learn dependencies
* GPU overview
* Vertex AI Training with GPUs
* [scikit-learn overview]
* [TensorFlow overview]
* [Compute Engine overview]
* [Dask overview]
* [Joblib overview]
* [Vertex AI Training overview]

 

問題 #198
You have been asked to productionize a proof-of-concept ML model built using Keras. The model was trained in a Jupyter notebook on a data scientist's local machine. The notebook contains a cell that performs data validation and a cell that performs model analysis. You need to orchestrate the steps contained in the notebook and automate the execution of these steps for weekly retraining. You expect much more training data in the future. You want your solution to take advantage of managed services while minimizing cost.
What should you do?

• A. Extract the steps contained in the Jupyter notebook as Python scripts, wrap each script in an Apache Airflow BashOperator, and run the resulting directed acyclic graph (DAG) in Cloud Composer.
• B. Write the code as a TensorFlow Extended (TFX) pipeline orchestrated with Vertex AI Pipelines. Use standard TFX components for data validation and model analysis, and use Vertex AI Pipelines for model retraining.
• C. Move the Jupyter notebook to a Notebooks instance on the largest N2 machine type, and schedule the execution of the steps in the Notebooks instance using Cloud Scheduler.
• D. Rewrite the steps in the Jupyter notebook as an Apache Spark job, and schedule the execution of the job on ephemeral Dataproc clusters using Cloud Scheduler.

答案：B

解題說明：
The best option for productionizing a Keras model is to use TensorFlow Extended (TFX), a framework for building end-to-end machine learning pipelines that can handle large-scale data and complex workflows. TFX provides standard components for data ingestion, transformation, validation, analysis, training, tuning, serving, and monitoring. TFX pipelines can be orchestrated with Vertex AI Pipelines, a managed service that runs on Google Cloud Platform and leverages Kubernetes and Argo. Vertex AI Pipelines allows you to automate the execution of your TFX pipeline steps, schedule retraining jobs, and scale up or down the resources as needed. By using TFX and Vertex AI Pipelines, you can take advantage of the following benefits:
* You can reuse the existing code in your Jupyter notebook, as TFX supports Keras as a first-class citizen. You can also use the Keras Tuner to optimize your model hyperparameters.
* You can ensure data quality and consistency by using the TFX Data Validation component, which can detect anomalies, drift, and skew in your data. You can also use the TFX SchemaGen component to generate a schema for your data and enforce it throughout the pipeline.
* You can analyze your model performance and fairness by using the TFX Model Analysis component, which can produce various metrics and visualizations. You can also use the TFX Model Validation component to compare your new model with a baseline model and set thresholds for deploying the model to production.
* You can deploy your model to various serving platforms by using the TFX Pusher component, which can push your model to Vertex AI, Cloud AI Platform, TensorFlow Serving, or TensorFlow Lite. You can also use the TFX Model Registry to manage the versions and metadata of your models.
* You can monitor your model performance and health by using the TFX Model Monitor component, which can detect data drift, concept drift, and prediction skew in your model. You can also use the TFX Evaluator component to compute metrics and validate your model against a baseline or a slice of data.
* You can reduce the cost and complexity of managing your own infrastructure by using Vertex AI Pipelines, which provides a serverless environment for running your TFX pipeline. You can also use the Vertex AI Experiments and Vertex AI TensorBoard to track and visualize your pipeline runs.
References:
* [TensorFlow Extended (TFX)]
* [Vertex AI Pipelines]
* [TFX User Guide]

 

問題 #199
You are an ML engineer on an agricultural research team working on a crop disease detection tool to detect leaf rust spots in images of crops to determine the presence of a disease. These spots, which can vary in shape and size, are correlated to the severity of the disease. You want to develop a solution that predicts the presence and severity of the disease with high accuracy. What should you do?

• A. Develop a template matching algorithm using traditional computer vision libraries.
• B. Develop an image segmentation ML model to locate the boundaries of the rust spots.
• C. Develop an image classification ML model to predict the presence of the disease.
• D. Create an object detection model that can localize the rust spots.

答案：B

解題說明：
The best option for developing a solution that predicts the presence and severity of the disease with high accuracy is to develop an image segmentation ML model to locate the boundaries of the rust spots. Image segmentation is a technique that partitions an image into multiple regions, each corresponding to a different object or semantic category. Image segmentation can be used to detect and localize the rust spots in the images of crops, and measure their shape and size. This information can then be used to determine the presence and severity of the disease, as the rust spots are correlated to the disease symptoms. Image segmentation can also handle the variability of the rust spots, as it does not rely on predefined templates or thresholds. Image segmentation can be implemented using deep learning models, such as U-Net, Mask R-CNN, or DeepLab, which can learn from large-scale datasets and achieve high accuracy and robustness. The other options are not as suitable for developing a solution that predicts the presence and severity of the disease with high accuracy, because:
* Creating an object detection model that can localize the rust spots would only provide the bounding boxes of the rust spots, not their exact boundaries. This would result in less precise measurements of the shape and size of the rust spots, and might affect the accuracy of the disease prediction. Object detection models are also more complex and computationally expensive than image segmentation models, as they have to perform both classification and localization tasks.
* Developing a template matching algorithm using traditional computer vision libraries would require manually designing and selecting the templates for the rust spots, which might not capture the diversity and variability of the rust spots. Template matching algorithms are also sensitive to noise, occlusion,
* rotation, and scale changes, and might fail to detect the rust spots in different scenarios. Template matching algorithms are also less accurate and robust than deep learning models, as they do not learn from data.
* Developing an image classification ML model to predict the presence of the disease would only provide a binary or categorical output, not the location or severity of the disease. Image classification models are also less informative and interpretable than image segmentation models, as they do not provide any spatial information or visual explanation for the prediction. Image classification models might also suffer from class imbalance or mislabeling issues, as the presence of the disease might not be consistent or clear across the images. References:
* Image Segmentation | Computer Vision | Google Developers
* Crop diseases and pests detection based on deep learning: a review | Plant Methods | Full Text
* Using Deep Learning for Image-Based Plant Disease Detection
* Computer Vision, IoT and Data Fusion for Crop Disease Detection Using ...
* On Using Artificial Intelligence and the Internet of Things for Crop ...
* Crop Disease Detection Using Machine Learning and Computer Vision

 

問題 #200
You want to train an AutoML model to predict house prices by using a small public dataset stored in BigQuery. You need to prepare the data and want to use the simplest most efficient approach. What should you do?

• A. Use a Vertex Al Workbench notebook instance to preprocess the data by using the pandas library Export the data as CSV files, and use those files to create a Vertex Al managed dataset.
• B. Write a query that preprocesses the data by using BigQuery Export the query results as CSV files and use those files to create a Vertex Al managed dataset.
• C. Write a query that preprocesses the data by using BigQuery and creates a new table Create a Vertex Al managed dataset with the new table as the data source.
• D. Use Dataflow to preprocess the data Write the output in TFRecord format to a Cloud Storage bucket.

答案：C

解題說明：
The simplest and most efficient approach for preparing the data for AutoML is to use BigQuery and Vertex AI. BigQuery is a serverless, scalable, and cost-effective data warehouse that can perform fast and interactive queries on large datasets. BigQuery can preprocess the data by using SQL functions such as filtering, aggregating, joining, transforming, and creating new features. The preprocessed data can be stored in a new table in BigQuery, which can be used as the data source for Vertex AI. Vertex AI is a unified platform for building and deploying machine learning solutions on Google Cloud. Vertex AI can create a managed dataset from a BigQuery table, which can be used to train an AutoML model. Vertex AI can also evaluate, deploy, and monitor the AutoML model, and provide online or batch predictions. By using BigQuery and Vertex AI, users can leverage the power and simplicity of Google Cloud to train an AutoML model to predict house prices.
The other options are not as simple or efficient as option A, for the following reasons:
* Option B: Using Dataflow to preprocess the data and write the output in TFRecord format to a Cloud Storage bucket would require more steps and resources than using BigQuery and Vertex AI. Dataflow is a service that can create scalable and reliable pipelines to process large volumes of data from various sources. Dataflow can preprocess the data by using Apache Beam, a programming model for defining and executing data processing workflows. TFRecord is a binary file format that can store sequential data efficiently. However, using Dataflow and TFRecord would require writing code, setting up a pipeline, choosing a runner, and managing the output files. Moreover, TFRecord is not a supported format for Vertex AI managed datasets, so the data would need to be converted to CSV or JSONL files before creating a Vertex AI managed dataset.
* Option C: Writing a query that preprocesses the data by using BigQuery and exporting the query results as CSV files would require more steps and storage than using BigQuery and Vertex AI. CSV is a text file format that can store tabular data in a comma-separated format. Exporting the query results as CSV files would require choosing a destination Cloud Storage bucket, specifying a file name or a wildcard, and setting the export options. Moreover, CSV files can have limitations such as size, schema, and encoding, which can affect the quality and validity of the data. Exporting the data as CSV files would also incur additional storage costs and reduce the performance of the queries.
* Option D: Using a Vertex AI Workbench notebook instance to preprocess the data by using the pandas library and exporting the data as CSV files would require more steps and skills than using BigQuery and Vertex AI. Vertex AI Workbench is a service that provides an integrated development environment for data science and machine learning. Vertex AI Workbench allows users to create and run Jupyter notebooks on Google Cloud, and access various tools and libraries for data analysis and machine learning. Pandas is a popular Python library that can manipulate and analyze data in a tabular format.
However, using Vertex AI Workbench and pandas would require creating a notebook instance, writing Python code, installing and importing pandas, connecting to BigQuery, loading and preprocessing the data, and exporting the data as CSV files. Moreover, pandas can have limitations such as memory usage, scalability, and compatibility, which can affect the efficiency and reliability of the data processing.
References:
* Preparing for Google Cloud Certification: Machine Learning Engineer, Course 2: Data Engineering for ML on Google Cloud, Week 1: Introduction to Data Engineering for ML
* Google Cloud Professional Machine Learning Engineer Exam Guide, Section 1: Architecting low-code ML solutions, 1.3 Training models by using AutoML
* Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 4: Low- code ML Solutions, Section 4.3: AutoML
* BigQuery
* Vertex AI
* Dataflow
* TFRecord
* CSV
* Vertex AI Workbench
* Pandas

 

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