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

SectionWeightObjectives
Topic 1: Monitor and optimize AI solutions16%- Optimize cost, latency, and resource usage
- Monitor data quality and pipeline health
- Troubleshoot and maintain production systems
- Monitor model performance, fairness, and drift
Topic 2: 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
      Topic 3: Automate and orchestrate ML pipelines18%- Implement CI/CD for ML systems
      - Automate retraining and model updates
      - Use Vertex AI Pipelines, TFX, and other orchestration tools
      - Design end-to-end ML workflows
      Topic 4: Train and deploy models20%- Configure training jobs and environments
      - Use Vertex AI deployment features and infrastructure
      - Implement generative AI deployment patterns
      - Deploy models for online, batch, and streaming prediction
      Topic 5: Architect low-code AI solutions12%- Design solutions using Vertex AI Studio, Model Garden, and Agent Builder
      - Identify use cases for low-code/no-code AI tools
      - Apply responsible AI principles to low-code designs
      Topic 6: Scale prototypes into AI models18%- Select appropriate model architectures and frameworks
      - Design and run experiments
      - Optimize model performance and generalization
      - Work with foundation models and generative AI techniques

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      Google Professional Machine Learning Engineer Sample Questions (Q238-Q243):

      NEW QUESTION # 238
      You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano. Scikit-team, and custom libraries. What should you do?

      Answer: D


      NEW QUESTION # 239
      You are implementing a batch inference ML pipeline in Google Cloud. The model was developed using TensorFlow and is stored in SavedModel format in Cloud Storage. You need to apply the model to a historical dataset containing 10 TB of data that is stored in a BigQuery table. How should you perform the inference?

      Answer: A


      NEW QUESTION # 240
      You are an ML engineer at a large grocery retailer with stores in multiple regions. You have been asked to create an inventory prediction model. Your models features include region, location, historical demand, and seasonal popularity. You want the algorithm to learn from new inventory data on a daily basis. Which algorithms should you use to build the model?

      Answer: B

      Explanation:
      "algorithm to learn from new inventory data on a daily basis" = time series model , best option to deal with time series is forsure RNN
      https://builtin.com/data-science/recurrent-neural-networks-and-lstm


      NEW QUESTION # 241
      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 # 242
      You are going to train a DNN regression model with Keras APIs using this code:

      How many trainable weights does your model have? (The arithmetic below is correct.)

      Answer: D

      Explanation:
      The number of trainable weights in a DNN regression model with Keras APIs can be calculated by multiplying the number of input units by the number of output units for each layer, and adding the number of bias units for each layer. The bias units are usually equal to the number of output units, except for the last layer, which does not have bias units if the activation function is softmax1. In this code, the model has three layers: a dense layer with 256 units and relu activation, a dropout layer with 0.25 rate, and a dense layer with 2 units and softmax activation. The input shape is 500. Therefore, the number of trainable weights is:
      * For the first layer: 500 input units * 256 output units + 256 bias units = 128256
      * For the second layer: The dropout layer does not have any trainable weights, as it only randomly sets some of the input units to zero to prevent overfitting2.
      * For the third layer: 256 input units * 2 output units + 0 bias units = 512 The total number of trainable weights is 128256 + 512 = 161024. Therefore, the correct answer is B.
      References:
      * How to calculate the number of parameters for a Convolutional Neural Network?
      * Dropout (keras.io)


      NEW QUESTION # 243
      ......

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