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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:

Section	Objectives
ML model development	- Model training and tuning 1. Hyperparameter tuning and optimization 2. Train models using TensorFlow / Vertex AI - Evaluation 1. Model validation strategies 2. Evaluate model performance metrics
ML pipeline automation and orchestration	- Pipeline design 1. Use Vertex AI Pipelines 2. Build end-to-end ML pipelines
Data preparation and processing	- Feature engineering 1. Feature selection and representation techniques 2. Transform and preprocess datasets - Data ingestion and pipelines 1. Build data pipelines for training and serving 2. Use BigQuery and data processing services
Designing ML solutions	- Framing ML problems 1. Define success metrics and evaluation criteria 2. Translate business problems into ML tasks - ML architecture design 1. Design scalable ML systems on GCP 2. Select appropriate ML models and approaches
Deployment and operations	- Monitoring and maintenance 1. Monitor model drift and performance 2. Retraining and lifecycle management - Model deployment 1. Deploy models using Vertex AI endpoints 2. Batch and online prediction systems

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Google Professional Machine Learning Engineer Sample Questions (Q241-Q246):

NEW QUESTION # 241
A retail company intends to use machine learning to categorize new products. A labeled dataset of current products was provided to the Data Science team. The dataset includes 1,200 products. The labeled dataset has 15 features for each product such as title dimensions, weight, and price. Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies.
Which model should be used for categorizing new products using the provided dataset for training?

A. A DeepAR forecasting model based on a recurrent neural network (RNN)
B. AnXGBoost model where the objective parameter is set to multi:softmax
C. A regression forest where the number of trees is set equal to the number of product categories
D. A deep convolutional neural network (CNN) with a softmax activation function for the last layer

Answer: D

NEW QUESTION # 242
You are creating a model training pipeline to predict sentiment scores from text-based product reviews. You want to have control over how the model parameters are tuned, and you will deploy the model to an endpoint after it has been trained You will use Vertex Al Pipelines to run the pipeline You need to decide which Google Cloud pipeline components to use What components should you choose?

Answer: D

Explanation:
According to the web search results, Vertex AI Pipelines is a serverless orchestrator for running ML pipelines, using either the KFP SDK or TFX1. Vertex AI Pipelines provides a set of prebuilt components that can be used to perform common ML tasks, such as training, evaluation, deployment, and more2. Vertex AI ModelEvaluationOp and ModelDeployOp are two such components that can be used to evaluate and deploy a model to an endpoint for online inference3. However, Vertex AI Pipelines does not provide a prebuilt component for hyperparameter tuning. Therefore, to have control over how the model parameters are tuned, you need to use a custom component that calls the Vertex AI HyperparameterTuningJob service4. Therefore, option A is the best way to decide which Google Cloud pipeline components to use for the given use case, as it includes a custom component for hyperparameter tuning, and prebuilt components for model evaluation and deployment. The other options are not relevant or optimal for this scenario. References:
* Vertex AI Pipelines
* Google Cloud Pipeline Components
* Vertex AI ModelEvaluationOp and ModelDeployOp
* Vertex AI HyperparameterTuningJob
* Google Professional Machine Learning Certification Exam 2023
* Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

NEW QUESTION # 243
You work at a mobile gaming startup that creates online multiplayer games Recently, your company observed an increase in players cheating in the games, leading to a loss of revenue and a poor user experience. You built a binary classification model to determine whether a player cheated after a completed game session, and then send a message to other downstream systems to ban the player that cheated Your model has performed well during testing, and you now need to deploy the model to production You want your serving solution to provide immediate classifications after a completed game session to avoid further loss of revenue. What should you do?

A. Import the model into Vertex Al Model Registry. Use the Vertex Batch Prediction service to run batch inference jobs.
B. Import the model into Vertex Al Model Registry Create a Vertex Al endpoint that hosts the model and make online inference requests.
C. Save the model files in a Cloud Storage Bucket Create a Cloud Function to read the model files and make online inference requests on the Cloud Function.
D. Save the model files in a VM Load the model files each time there is a prediction request and run an inference job on the VM.

Answer: B

Explanation:
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.
:
Online versus batch prediction | Vertex AI | Google Cloud
Deploy a model to an endpoint | Vertex AI | Google Cloud
Introduction to Vertex AI Model Registry | Google Cloud
Get online predictions | Vertex AI | Google Cloud

NEW QUESTION # 244
You have trained a text classification model in TensorFlow using Al Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead. What should you do?

A. Submit a batch prediction job on Al Platform that points to the model location in Cloud Storage.
B. Export the model to BigQuery ML.
C. Deploy and version the model on Al Platform.
D. Use Dataflow with the SavedModel to read the data from BigQuery

Answer: A

NEW QUESTION # 245
You developed a BigQuery ML linear regressor model by using a training dataset stored in a BigQuery table. New data is added to the table every minute. You are using Cloud Scheduler and Vertex AI Pipelines to automate hourly model training, and use the model for direct inference. The feature preprocessing logic includes quantile bucketization and MinMax scaling on data received in the last hour. You want to minimize storage and computational overhead. What should you do?

A. Create a component in the Vertex AI Pipelines directed acyclic graph (DAG) to calculate the required statistics, and pass the statistics on to subsequent components.
B. Use the TRANSFORM clause in the CREATE MODELstatement in the SQL query to calculate the required statistics.
C. Preprocess and stage the data in BigQuery prior to feeding it to the model during training and inference.
D. Create SQL queries to calculate and store the required statistics in separate BigQuery tables that are referenced in the CREATE MODELstatement.

Answer: B

Explanation:
Using the TRANSFORM clause allows you to apply preprocessing steps directly within the model training process. This means that the required statistics for quantile bucketization and MinMax scaling can be calculated on-the-fly during model training and inference, minimizing storage overhead by not requiring separate tables to store these statistics. This approach also streamlines the workflow by integrating the preprocessing into the model training SQL query, thus reducing computational overhead and complexity in your pipeline.

NEW QUESTION # 246
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