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

SectionWeightObjectives
Topic 1: 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 2: Train and deploy models20%- Deploy models for online, batch, and streaming prediction
      - Use Vertex AI deployment features and infrastructure
      - Implement generative AI deployment patterns
      - Configure training jobs and environments
      Topic 3: 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 4: Scale prototypes into AI models18%- Design and run experiments
      - Select appropriate model architectures and frameworks
      - Work with foundation models and generative AI techniques
      - Optimize model performance and generalization
      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: Automate and orchestrate ML pipelines18%- Implement CI/CD for ML systems
      - Use Vertex AI Pipelines, TFX, and other orchestration tools
      - Design end-to-end ML workflows
      - Automate retraining and model updates

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      Google Professional Machine Learning Engineer Sample Questions (Q373-Q378):

      NEW QUESTION # 373
      You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?

      Answer: A


      NEW QUESTION # 374
      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: B

      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.
      Reference:
      How to calculate the number of parameters for a Convolutional Neural Network?
      Dropout (keras.io)


      NEW QUESTION # 375
      You are building an internal assistant that answers employee questions about a 12,000-page HR policy corpus. The corpus is updated weekly, and answers must cite the specific policy section.
      Hallucinated answers have been reported during testing. You need to improve factual accuracy while keeping the content current. What should you do?

      Answer: A

      Explanation:
      Retrieval-augmented generation grounds the model in retrieved passages, which reduces hallucination and allows the response to cite the originating chunk. Re-embedding only changed documents keeps the index current at low cost, whereas weekly fine-tuning is expensive and does not reliably provide citations.


      NEW QUESTION # 376
      A Data Engineer needs to build a model using a dataset containing customer credit card information How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

      Answer: C

      Explanation:
      Explanation/Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/pca.html


      NEW QUESTION # 377
      You are an ML engineer at a retail company. You have built a model that predicts a coupon to offer an ecommerce customer at checkout based on the items in their cart. When a customer goes to checkout, your serving pipeline, which is hosted on Google Cloud, joins the customer's existing cart with a row in a BigQuery table that contains the customers' historic purchase behavior and uses that as the model's input. The web team is reporting that your model is returning predictions too slowly to load the coupon offer with the rest of the web page. How should you speed up your model's predictions?

      Answer: B


      NEW QUESTION # 378
      ......

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