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

SectionWeightObjectives
Topic 1: Architect low-code AI solutions12%- 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
Topic 2: Collaborate to manage data and models16%- Manage datasets and features in Vertex AI
- 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
      - Address data privacy, compliance, and governance
      Topic 3: Automate and orchestrate ML pipelines18%- Design end-to-end ML workflows
      - Implement CI/CD for ML systems
      - Automate retraining and model updates
      - Use Vertex AI Pipelines, TFX, and other orchestration tools
      Topic 4: Train and deploy models20%- Configure training jobs and environments
      - Implement generative AI deployment patterns
      - Use Vertex AI deployment features and infrastructure
      - Deploy models for online, batch, and streaming prediction
      Topic 5: Monitor and optimize AI solutions16%- Monitor data quality and pipeline health
      - Optimize cost, latency, and resource usage
      - Troubleshoot and maintain production systems
      - Monitor model performance, fairness, and drift
      Topic 6: Scale prototypes into AI models18%- Select appropriate model architectures and frameworks
      - Optimize model performance and generalization
      - Work with foundation models and generative AI techniques
      - Design and run experiments

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      Google Professional Machine Learning Engineer Sample Questions (Q32-Q37):

      NEW QUESTION # 32
      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?

      Answer: C


      NEW QUESTION # 33
      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?

      Answer: A


      NEW QUESTION # 34
      A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements:
      * Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.
      * Support event-driven ETL pipelines
      * Provide a quick and easy way to understand metadata
      Which approach meets these requirements?

      Answer: D


      NEW QUESTION # 35
      You are analyzing customer data for a healthcare organization that is stored in Cloud Storage.
      The data contains personally identifiable information (PII). You need to perform data exploration and preprocessing while ensuring the security and privacy of sensitive fields. What should you do?

      Answer: D


      NEW QUESTION # 36
      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?

      Answer: D

      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 # 37
      ......

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