Google Updated Professional-Machine-Learning-Engineer Exam Questions and Answers by prince

BigQuery is a service that allows you to store and query large amounts of data in a scalable and cost-effective way. You can use BigQuery to ingest the Avro files from the Cloud Storage bucket and perform analytics on the structured data. Avro is a binary file format that can store complex data types and schemas. You can use the bq load command or the BigQuery API to load the Avro files into a BigQuery table. You can then use SQL queries to analyze the data and generate insights. Dataflow is a service that allows you to create and run scalable and portable data processing pipelines on Google Cloud. You can use Dataflow to create the features for your ML models, such as transforming, aggregating, and encoding the data. You can use the Apache Beam SDK to write your Dataflow pipeline code in Python or Java. You can also use the built-in transforms or custom transforms to apply the feature engineering logic to your data. Vertex AI Feature Store is a service that allows you to store and manage your ML features on Google Cloud. You can use Vertex AI Feature Store to host the features that your ML models use for online prediction. Online prediction is a type of prediction that provides low-latency responses to individual or small batches of input data. You can use the Vertex AI Feature Store API to write the features from your Dataflow pipeline to a feature store entity type. You can then use the Vertex AI Feature Store online serving API to read the features from the feature store and pass them to your ML models for online prediction. By using BigQuery, Dataflow, and Vertex AI Feature Store, you can configure a pipeline that performs analytics, creates features, and hosts the features that your ML models use for online prediction.  References :

BigQuery documentation

Dataflow documentation

Vertex AI Feature Store documentation

Preparing for Goo gle Cloud Certification: Machine Learning Engineer Professional Certificate

 This answer is correct because it allows AutoML Tables to handle the time signal in the data and split the data accordingly. This ensures that the model is trained on the historical data and evaluated on the more recent data, which is consistent with the prediction task. AutoML Tables can automatically detect and handle temporal features in the data, such as date, time, and duration. By specifying the Time column, AutoML Tables can also perform time-series forecasting and use the time signal to generate additional features, such as seasonality and trend.  References:

[AutoML Tables: Preparing your training data]

[AutoML Tables: Time-series forecasting]

According to the official exam guide 1 , one of the skills assessed in the exam is to “configure and optimize model training jobs”.  Cloud TPU VMs 2  are a new way to access Cloud TPUs directly on the TPU host machines, offering a simpler and more flexible user experience. Cloud TPU VMs are optimized for ML model training and can reduce training time and cost.  You can use Cloud TPU VMs to train Transformer models in TensorFlow by using the tf.distribute.TPUStrategy 3 , which handles the distribution of computations across the TPU cores. The other options are not relevant or optimal for this scenario.  References :

Professional ML Engineer Exam Guide

Cloud TPU VMs

Distributed training with TPUStrategy

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Ques tions

 Online inference is a process where you send a single or a small number of predict ion requests to a model and get immediate responses 1 . Online inference is suitable for scenarios where you need timely predictions, such as detecting cheating in online games.  Online inference requires that the model is deployed to an endpoint, which is a resource that prov ides a service URL for prediction requests 2 .

Vertex AI Model Registry is a central repository where you can manage the lifecycle of your ML models 3 .  You can import models from various sources, such as custom models or AutoML models, and assign them to different versions and aliases 3 .  You can also deploy models to endpoints, which are resources that provide a service URL for online prediction 2 .

By importing the model into Vertex AI Model Registry, you can leverage the Vertex AI features to monitor and update the model 3 . You can use Vertex AI Experiments to track and compare the metrics of different model versions, such as accuracy, precision, recall, and AUC. You can also use Vertex AI Explainable AI to generate feature attributions that show how much each input feature contributed to the model’s prediction.

By creating a Vertex AI endpoint that hosts the model, you can use the Vertex AI Prediction service to serve online inference requests 2 .  Vertex AI Prediction provides various benefits, such as scalability, re liability, security, and logging 2 .  You can use the Vertex AI API or the Google Cloud console to send online inference requests to the endpoint and get immediate classifications 4 .

Therefore, the best option for your scenario is to import the model into Vertex AI Model Registry, create a Vertex AI endpoint that hosts the model, and make online inference requests.

The other options are not suitable for your scenario, because they either do not provide immediate classifications, such as using batch prediction or loading the model files each time, or they do not use Vertex AI Prediction, which would require more development and maintenance effort, such as creating a Cloud Function or a VM.