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To be eligible for the Professional Machine Learning Engineer certification, candidates must have a strong foundation in machine learning concepts, programming languages such as Python, and cloud computing technologies. Additionally, candidates must have experience working with Google Cloud Platform and its various machine learning tools such as TensorFlow, AutoML, and BigQuery. It is recommended that candidates have at least three years of experience in the field of machine learning before attempting this certification.
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Google Professional Machine Learning Engineer certification exam is designed to test individuals’ proficiency in designing and developing scalable machine learning models on Google Cloud Platform. Professional-Machine-Learning-Engineer Exam is intended for professionals with experience in implementing and managing machine learning models on the Google Cloud Platform. Google Professional Machine Learning Engineer certification exam focuses on assessing the individual’s ability to design, develop, train, evaluate, and deploy machine learning models.
NEW QUESTION # 222
You are training a Resnet model on Al Platform using TPUs to visually categorize types of defects in automobile engines. You capture the training profile using the Cloud TPU profiler plugin and observe that it is highly input-bound. You want to reduce the bottleneck and speed up your model training process. Which modifications should you make to the tf .data dataset?
Choose 2 answers
Answer: A,D
Explanation:
The tf.data dataset is a TensorFlow API that provides a way to create and manipulate data pipelines for machine learning. The tf.data dataset allows you to apply various transformations to the data, such as reading, shuffling, batching, prefetching, and interleaving. These transformations can affect the performance and efficiency of the model training process1 One of the common performance issues in model training is input-bound, which means that the model is waiting for the input data to be ready and is not fully utilizing the computational resources. Input-bound can be caused by slow data loading, insufficient parallelism, or large data size. Input-bound can be detected by using the Cloud TPU profiler plugin, which is a tool that helps you analyze the performance of your model on Cloud TPUs. The Cloud TPU profiler plugin can show you the percentage of time that the TPU cores are idle, which indicates input-bound2 To reduce the input-bound bottleneck and speed up the model training process, you can make some modifications to the tf.data dataset. Two of the modifications that can help are:
Use the interleave option for reading data. The interleave option allows you to read data from multiple files in parallel and interleave their records. This can improve the data loading speed and reduce the idle time of the TPU cores. The interleave option can be applied by using the tf.data.Dataset.interleave method, which takes a function that returns a dataset for each input element, and a number of parallel calls3 Set the prefetch option equal to the training batch size. The prefetch option allows you to prefetch the next batch of data while the current batch is being processed by the model. This can reduce the latency between batches and improve the throughput of the model training. The prefetch option can be applied by using the tf.data.Dataset.prefetch method, which takes a buffer size argument. The buffer size should be equal to the training batch size, which is the number of examples per batch4 The other options are not effective or counterproductive. Reducing the value of the repeat parameter will reduce the number of epochs, which is the number of times the model sees the entire dataset. This can affect the model's accuracy and convergence. Increasing the buffer size for the shuffle option will increase the randomness of the data, but also increase the memory usage and the data loading time. Decreasing the batch size argument in your transformation will reduce the number of examples per batch, which can affect the model's stability and performance.
NEW QUESTION # 223
You are an ML engineer responsible for designing and implementing training pipelines for ML models. You need to create an end-to-end training pipeline for a TensorFlow model. The TensorFlow model will be trained on several terabytes of structured dat a. You need the pipeline to include data quality checks before training and model quality checks after training but prior to deployment. You want to minimize development time and the need for infrastructure maintenance. How should you build and orchestrate your training pipeline?
Answer: A
NEW QUESTION # 224
You have a large corpus of written support cases that can be classified into 3 separate categories: Technical Support, Billing Support, or Other Issues. You need to quickly build, test, and deploy a service that will automatically classify future written requests into one of the categories. How should you configure the pipeline?
Answer: A
Explanation:
AutoML is easier and faster and "you need to quickly build, test, and deploy". Also the REST API part fits our use case.
NEW QUESTION # 225
You work for a retail company that is using a regression model built with BigQuery ML to predict product sales. This model is being used to serve online predictions Recently you developed a new version of the model that uses a different architecture (custom model) Initial analysis revealed that both models are performing as expected You want to deploy the new version of the model to production and monitor the performance over the next two months You need to minimize the impact to the existing and future model users How should you deploy the model?
Answer: D
Explanation:
Vertex AI Model Registry is a central repository where you can manage the lifecycle of your ML models1. You can import models from various sources, such as BigQuery ML, AutoML, or custom models, and assign them to different versions and aliases1. You can also deploy models to endpoints, which are resources that provide a service URL for online prediction2.
By importing the new model to the same Vertex AI Model Registry as a different version of the existing model, you can keep track of the model versions and compare their performance metrics1. You can also use aliases to label the model versions according to their readiness for production, such as default or staging1.
By deploying the new model to the same Vertex AI endpoint as the existing model, you can use traffic splitting to gradually shift the production traffic from the old model to the new model2. Traffic splitting is a feature that allows you to specify the percentage of prediction requests that each deployed model in an endpoint should handle2. This way, you can minimize the impact to the existing and future model users, and monitor the performance of the new model over time2.
The other options are not suitable for your scenario, because they either require creating a separate endpoint or a Cloud Run service, which would increase the complexity and maintenance of your deployment, or they do not allow you to use traffic splitting, which would create a sudden change in your prediction results.
References:
* Introduction to Vertex AI Model Registry | Google Cloud
* Deploy a model to an endpoint | Vertex AI | Google Cloud
NEW QUESTION # 226
You are an AI engineer that works for a popular video streaming platform. You built a classification model using PyTorch to predict customer churn. Each week, the customer retention team plans to contact customers that have been identified as at risk of churning with personalized offers. You want to deploy the model while minimizing maintenance effort. What should you do?
Answer: D
Explanation:
Deploying the model to a Vertex AI endpoint allows you to use Vertex AI's managed infrastructure, minimizing maintenance effort.
Batch prediction is ideal for this use case, as the churn predictions are required weekly, not in real-time. It is cost-efficient and straightforward to schedule batch jobs to generate predictions for the entire customer dataset at regular intervals.
Scheduling the batch prediction weekly aligns perfectly with the customer retention team's plan to act on predictions weekly.
NEW QUESTION # 227
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