Machine Learning Engineer Professional-Machine-Learning-Engineer Exam Dumps

 The best option for training an ML model on a large dataset, using a TPU to accelerate the training process, and discovering that the TPU is not reaching its full capacity, is to increase the batch size. This option allows you to leverage the power and simplicity of TPUs to train your model faster and more efficiently. A TPU is a custom-developed application-specific integrated circuit (ASIC) that can accelerate machine learning workloads. A TPU can provide high performance and scalability for various types of models, such as linear regression, logistic regression, k-means clustering, matrix factorization, and deep neural networks. A TPU can also support various tools and frameworks, such as TensorFlow, PyTorch, and JAX. A batch size is a parameter that specifies the number of training examples in one forward/backward pass. A batch size can affect the speed and accuracy of the training process. A larger batch size can help you utilize the parallel processing power of the TPU, and reduce the communication overhead between the TPU and the host CPU. A larger batch size can also help you avoid overfitting, as it can reduce the variance of the gradient updates. By increasing the batch size, you can train your model on a large dataset faster and more efficiently, and make full use of the TPU capacity1.

The other options are not as good as option D, for the following reasons:

• Option A: Increasing the learning rate would not help you utilize the parallel processing power of the TPU, and could cause errors or poor performance. A learning rate is a parameter that controls how much the model is updated in each iteration. A learning rate can affect the speed and accuracy of the training process. A larger learning rate can help you converge faster, but it can also cause instability, divergence, or oscillation. By increasing the learning rate, you may not be able to find the optimal solution, and your model may perform poorly on the validation or test data2.
• Option B: Increasing the number of epochs would not help you utilize the parallel processing power of the TPU, and could increase the complexity and cost of the training process. An epoch is a measure of the number of times all of the training examples are used once in the training process. An epoch can affect the speed and accuracy of the training process. A larger number of epochs can help you learn more from the data, but it can also cause overfitting, underfitting, or diminishing returns. By increasing the number of epochs, you may not be able to improve the model performance significantly, and your training process may take longer and consume more resources3.
• Option C: Decreasing the learning rate would not help you utilize the parallel processing power of the TPU, and could slow down the training process. A learning rate is a parameter that controls how much the model is updated in each iteration. A learning rate can affect the speed and accuracy of the training process. A smaller learning rate can help you find a more precise solution, but it can also cause slow convergence or local minima. By decreasing the learning rate, you may not be able to reach the optimal solution in a reasonable time, and your training process may take longer2.

References:

• Preparing for Google Cloud Certification: Machine Learning Engineer, Course 2: ML Models and Architectures, Week 1: Introduction to ML Models and Architectures
• Google Cloud Professional Machine Learning Engineer Exam Guide, Section 2: Architecting ML solutions, 2.1 Designing ML models
• Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 4: ML Models and Architectures, Section 4.1: Designing ML Models
• Use TPUs
• Triose phosphate utilization and beyond: from photosynthesis to end …
• Cloud TPU performance guide
• Google TPU: Architecture and Performance Best Practices - Run

TFRecord is a binary file format that stores your data as a sequence of binary strings1. TFRecord files are efficient, scalable, and easy to process1. Sharding is a technique that splits a large file into smaller files, which can improve parallelism and performance2. Dataflow is a service that allows you to create and run data processing pipelines on Google Cloud3. Dataflow can create sharded TFRecord files from your images in a Cloud Storage directory4.

tf.data.TFRecordDataset is a class that allows you to read and parse TFRecord files in TensorFlow. You can use this class to create a tf.data.Dataset object that represents your input data for training. tf.data.Dataset is a high-level API that provides various methods to transform, batch, shuffle, and prefetch your data.

Vertex AI Training is a service that allows you to train your custom models on Google Cloud using various hardware accelerators, such as GPUs. Vertex AI Training supports TensorFlow models and can read data from Cloud Storage. You can use Vertex AI Training to train your image classification model by using a V100 GPU, which is a powerful and fast GPU for deep learning.

References:

• TFRecord and tf.Example | TensorFlow Core
• Sharding | TensorFlow Core
• Dataflow | Google Cloud
• Creating sharded TFRecord files | Google Cloud
• [tf.data.TFRecordDataset | TensorFlow Core v2.6.0]
• [tf.data: Build TensorFlow input pipelines | TensorFlow Core]
• [Vertex AI Training | Google Cloud]
• [NVIDIA Tesla V100 GPU | NVIDIA]

This answer is correct because it allows you to use the trained TensorFlow model for batch predictions on text data stored in BigQuery without any additional processing or overhead. Al Platform provides a service for running batch prediction jobs that can take input data from BigQuery or Cloud Storage and write the output to BigQuery or Cloud Storage. You can use the SavedModel format to export your TensorFlow model to Cloud Storage and then submit a batch prediction job that points to the model location and the input data location. Al Platform will handle the scaling and distribution of the prediction requests and return the results in the specified output location. References:

• [Al Platform: Batch prediction overview]
• [Al Platform: Exporting a SavedModel for prediction]

According to the official exam guide1, one of the skills assessed in the exam is to “design, build, and productionalize ML models to solve business challenges using Google Cloud technologies”. Cloud Data Loss Prevention (DLP) API2 is a service that provides programmatic access to a powerful detection engine for personally identifiable information and other privacy-sensitive data in unstructured data streams, such as text blocks and images. Cloud DLP API helps you discover, classify, and protect your sensitive data by using techniques such as de-identification, masking, tokenization, and bucketing. You can use Cloud DLP API to de-identify the PII data before performing data exploration and preprocessing, and retain the data utility for ML purposes. Therefore, option A is the best way to perform data exploration and preprocessing while ensuring the security and privacy of sensitive fields. The other options are not relevant or optimal for this scenario. References:

• Professional ML Engineer Exam Guide
• Cloud Data Loss Prevention (DLP) API
• Google Professional Machine Learning Certification Exam 2023
• Latest Google Professional Machine Learning Engineer Actual Free Exam Questions