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

Section	Weight	Objectives
Architect low-code AI solutions	12%	- Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Apply responsible AI principles to low-code designs - Identify use cases for low-code/no-code AI tools
Collaborate to manage data and models	16%	- Organize and prepare enterprise data 1. Work with structured, unstructured, and semi-structured data 2. Use Cloud Storage, BigQuery, Spanner, Cloud SQL, and data processing tools - Manage datasets and features in Vertex AI - Address data privacy, compliance, and governance
Train and deploy models	20%	- Implement generative AI deployment patterns - Configure training jobs and environments - Deploy models for online, batch, and streaming prediction - Use Vertex AI deployment features and infrastructure
Automate and orchestrate ML pipelines	18%	- Automate retraining and model updates - Design end-to-end ML workflows - Use Vertex AI Pipelines, TFX, and other orchestration tools - Implement CI/CD for ML systems
Scale prototypes into AI models	18%	- Work with foundation models and generative AI techniques - Design and run experiments - Optimize model performance and generalization - Select appropriate model architectures and frameworks
Monitor and optimize AI solutions	16%	- Monitor model performance, fairness, and drift - Troubleshoot and maintain production systems - Optimize cost, latency, and resource usage - Monitor data quality and pipeline health

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Google Professional Machine Learning Engineer Sample Questions (Q171-Q176):

NEW QUESTION # 171
You recently used BigQuery ML to train an AutoML regression model. You shared results with your team and received positive feedback. You need to deploy your model for online prediction as quickly as possible. What should you do?

A. Retrain the model by using BigQuery ML. and specify Vertex Al as the model registry Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.
B. Retrain the model by using Vertex Al Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.
C. Alter the model by using BigQuery ML and specify Vertex Al as the model registry Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.
D. Export the model from BigQuery ML to Cloud Storage Import the model into Vertex Al Model Registry Deploy the model to a Vertex Al endpoint.

Answer: D

NEW QUESTION # 172
You developed a custom model by using Vertex Al to predict your application's user churn rate You are using Vertex Al Model Monitoring for skew detection The training data stored in BigQuery contains two sets of features - demographic and behavioral You later discover that two separate models trained on each set perform better than the original model You need to configure a new model mentioning pipeline that splits traffic among the two models You want to use the same prediction-sampling-rate and monitoring-frequency for each model You also want to minimize management effort What should you do?

A. Keep the training dataset as is Deploy the models to two separate endpoints and submit two Vertex Al Model Monitoring jobs with appropriately selected feature-thresholds parameters
B. Separate the training dataset into two tables based on demographic and behavioral features Deploy the models to two separate endpoints, and submit two Vertex Al Model Monitoring jobs
C. Keep the training dataset as is Deploy both models to the same endpoint and submit a Vertex Al Model Monitoring job with a monitoring-config-from parameter that accounts for the model IDs and feature selections
D. Separate the training dataset into two tables based on demographic and behavioral features. Deploy both models to the same endpoint and submit a Vertex Al Model Monitoring job with a monitoring-config-from parameter that accounts for the model IDs and training datasets

Answer: C

NEW QUESTION # 173
You are developing a custom TensorFlow classification model based on tabular dat a. Your raw data is stored in BigQuery contains hundreds of millions of rows, and includes both categorical and numerical features. You need to use a MaxMin scaler on some numerical features, and apply a one-hot encoding to some categorical features such as SKU names. Your model will be trained over multiple epochs. You want to minimize the effort and cost of your solution. What should you do?

A. 1 Write a SQL query to create a separate lookup table to scale the numerical features.
2 Perform the one-hot text encoding in BigQuery.
3. Feed the resulting BigQuery view into Vertex Al Training.
B. 1 Use BigQuery to scale the numerical features.
2. Feed the features into Vertex Al Training.
3 Allow TensorFlow to perform the one-hot text encoding.
C. 1 Use TFX components with Dataflow to encode the text features and scale the numerical features.
2 Export results to Cloud Storage as TFRecords.
3 Feed the data into Vertex Al Training.
D. 1 Write a SQL query to create a separate lookup table to scale the numerical features.
2. Deploy a TensorFlow-based model from Hugging Face to BigQuery to encode the text features.
3. Feed the resulting BigQuery view into Vertex Al Training.

Answer: C

Explanation:
TFX (TensorFlow Extended) is a platform for end-to-end machine learning pipelines. It provides components for data ingestion, preprocessing, validation, model training, serving, and monitoring. Dataflow is a fully managed service for scalable data processing. By using TFX components with Dataflow, you can perform feature engineering on large-scale tabular data in a distributed and efficient way. You can use the Transform component to apply the MaxMin scaler and the one-hot encoding to the numerical and categorical features, respectively. You can also use the ExampleGen component to read data from BigQuery and the Trainer component to train your TensorFlow model. The output of the Transform component is a TFRecord file, which is a binary format for storing TensorFlow data. You can export the TFRecord file to Cloud Storage and feed it into Vertex AI Training, which is a managed service for training custom machine learning models on Google Cloud. Reference:
TFX | TensorFlow
Dataflow | Google Cloud
Vertex AI Training | Google Cloud

NEW QUESTION # 174
You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent. Which data transformation strategy would likely improve the performance of your classifier?

A. Log transform all numeric features.
B. Z-normalize all the numeric features.
C. Oversample the fraudulent transaction 10 times.
D. Modify the target variable using the Box-Cox transformation.

Answer: C

Explanation:
https://towardsdatascience.com/how-to-build-a-machine-learning-model-to-identify-credit-card-fraud-in-5-stepsa-hands-on-modeling-5140b3bd19f1

NEW QUESTION # 175
You work at a subscription-based company. You have trained an ensemble of trees and neural networks to predict customer churn, which is the likelihood that customers will not renew their yearly subscription. The average prediction is a 15% churn rate, but for a particular customer the model predicts that they are 70% likely to churn. The customer has a product usage history of
30%, is located in New York City, and became a customer in 1997. You need to explain the difference between the actual prediction, a 70% churn rate, and the average prediction. You want to use Vertex Explainable AI. What should you do?

A. Configure integrated gradients explanations on Vertex Explainable AI.
B. Train local surrogate models to explain individual predictions.
C. Configure sampled Shapley explanations on Vertex Explainable AI.
D. Measure the effect of each feature as the weight of the feature multiplied by the feature value.

Answer: C

Explanation:
Assigns credit for the outcome to each feature, and considers different permutations of the features. This method provides a sampling approximation of exact Shapley values.
shampled shapely recommended Model Type: Non-differentiable models, such as ensembles of trees and neural networks.
https://cloud.google.com/ai-platform/prediction/docs/ai-explanations/overview

NEW QUESTION # 176
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