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

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

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      Google Professional Machine Learning Engineer Sample Questions (Q410-Q415):

      NEW QUESTION # 410
      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: B


      NEW QUESTION # 411
      You work for a company that captures live video footage of checkout areas in their retail stores You need to use the live video footage to build a mode! to detect the number of customers waiting for service in near real time You want to implement a solution quickly and with minimal effort How should you build the model?

      Answer: C


      NEW QUESTION # 412
      You are tasked with building an MLOps pipeline to retrain tree-based models in production. The pipeline will include components related to data ingestion, data processing, model training, model evaluation, and model deployment. Your organization primarily uses PySpark-based workloads for data preprocessing. You want to minimize infrastructure management effort. How should you set up the pipeline?

      Answer: A


      NEW QUESTION # 413
      While running a model training pipeline on Vertex Al, you discover that the evaluation step is failing because of an out-of-memory error. You are currently using TensorFlow Model Analysis (TFMA) with a standard Evaluator TensorFlow Extended (TFX) pipeline component for the evaluation step. You want to stabilize the pipeline without downgrading the evaluation quality while minimizing infrastructure overhead. What should you do?

      Answer: B

      Explanation:
      The best option to stabilize the pipeline without downgrading the evaluation quality while minimizing infrastructure overhead is to use Dataflow as the runner for the evaluation step. Dataflow is a fully managed service for executing Apache Beam pipelines that can scale up and down according to the workload. Dataflow can handle large-scale, distributed data processing tasks such as model evaluation, and it can also integrate with Vertex AI Pipelines and TensorFlow Extended (TFX). By using the flag -runner=DataflowRunner in beam_pipeline_args, you can instruct the Evaluator component to run the evaluation step on Dataflow, instead of using the default DirectRunner, which runs locally and may cause out-of-memory errors. Option A is incorrect because adding tfma.MetricsSpec() to limit the number of metrics in the evaluation step may downgrade the evaluation quality, as some important metrics may be omitted.
      Moreover, reducing the number of metrics may not solve the out-of-memory error, as the evaluation step may still consume a lot of memory depending on the size and complexity of the data and the model. Option B is incorrect because migrating the pipeline to Kubeflow hosted on Google Kubernetes Engine (GKE) may increase the infrastructure overhead, as you need to provision, manage, and monitor the GKE cluster yourself.
      Moreover, you need to specify the appropriate node parameters for the evaluation step, which may require trial and error to find the optimal configuration. Option D is incorrect because moving the evaluation step out of the pipeline and running it on custom Compute Engine VMs may also increase the infrastructure overhead, as you need to create, configure, and delete the VMs yourself. Moreover, you need to ensure that the VMs have sufficientmemory for the evaluation step, which may require trial and error to find the optimal machine type. References:
      * Dataflow documentation
      * Using DataflowRunner
      * Evaluator component documentation
      * Configuring the Evaluator component


      NEW QUESTION # 414
      You are developing a recommendation engine for an online clothing store. The historical customer transaction data is stored in BigQuery and Cloud Storage. You need to perform exploratory data analysis (EDA), preprocessing and model training. You plan to rerun these EDA, preprocessing, and training steps as you experiment with different types of algorithms. You want to minimize the cost and development effort of running these steps as you experiment. How should you configure the environment?

      Answer: A


      NEW QUESTION # 415
      ......

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