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

SectionObjectives
Deployment and operations- Monitoring and maintenance
  • 1. Retraining and lifecycle management
    • 2. Monitor model drift and performance
      - Model deployment
      • 1. Batch and online prediction systems
        • 2. Deploy models using Vertex AI endpoints
          ML pipeline automation and orchestration- Pipeline design
          • 1. Build end-to-end ML pipelines
            • 2. Use Vertex AI Pipelines
              ML model development- Model training and tuning
              • 1. Hyperparameter tuning and optimization
                • 2. Train models using TensorFlow / Vertex AI
                  - Evaluation
                  • 1. Model validation strategies
                    • 2. Evaluate model performance metrics
                      Designing ML solutions- Framing ML problems
                      • 1. Translate business problems into ML tasks
                        • 2. Define success metrics and evaluation criteria
                          - ML architecture design
                          • 1. Design scalable ML systems on GCP
                            • 2. Select appropriate ML models and approaches
                              Data preparation and processing- Data ingestion and pipelines
                              • 1. Build data pipelines for training and serving
                                • 2. Use BigQuery and data processing services
                                  - Feature engineering
                                  • 1. Feature selection and representation techniques
                                    • 2. Transform and preprocess datasets

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                                      Google Professional Machine Learning Engineer Sample Questions (Q409-Q414):

                                      NEW QUESTION # 409
                                      You have recently used TensorFlow to train a classification model on tabular data. You have created a Dataflow pipeline that can transform several terabytes of data into training or prediction datasets consisting of TFRecords. You now need to productionize the model, and you want the predictions to be automatically uploaded to a BigQuery table on a weekly schedule. What should you do?

                                      Answer: B


                                      NEW QUESTION # 410
                                      You are training an LSTM-based model on Al Platform to summarize text using the following job submission script:

                                      You want to ensure that training time is minimized without significantly compromising the accuracy of your model. What should you do?

                                      Answer: C

                                      Explanation:
                                      The training time of a machine learning model depends on several factors, such as the complexity of the model, the size of the data, the hardware resources, and the hyperparameters. To minimize the training time without significantly compromising the accuracy of the model, one should optimize these factors as much as possible.
                                      One of the factors that can have a significant impact on the training time is the scale-tier parameter, which specifies the type and number of machines to use for the training job on AI Platform. The scale-tier parameter can be one of the predefined values, such as BASIC, STANDARD_1, PREMIUM_1, or BASIC_GPU, or a custom value that allows you to configure the machine type, the number of workers, and the number of parameter servers1 To speed up the training of an LSTM-based model on AI Platform, one should modify the scale-tier parameter to use a higher tier or a custom configuration that provides more computational resources, such as more CPUs, GPUs, or TPUs. This can reduce the training time by increasing the parallelism and throughput of the model training. However, one should also consider the trade-off between the training time and the cost, as higher tiers or custom configurations may incur higher charges2 The other options are not as effective or may have adverse effects on the model accuracy. Modifying the epochs parameter, which specifies the number of times the model sees the entire dataset, may reduce the training time, but also affect the model's convergence and performance. Modifying the batch size parameter, which specifies the number of examples per batch, may affect the model's stability and generalization ability, as well as the memory usage and the gradient update frequency. Modifying the learning rate parameter, which specifies the step size of the gradient descent optimization, may affect the model's convergence and performance, as well as the risk of overshooting or getting stuck in local minima3


                                      NEW QUESTION # 411
                                      You are building a TensorFlow text-to-image generative model by using a dataset that contains billions of images with their respective captions. You want to create a low maintenance, automated workflow that reads the data from a Cloud Storage bucket collects statistics, splits the dataset into training/validation/test datasets performs data transformations trains the model using the training/validation datasets, and validates the model by using the test dataset. What should you do?

                                      Answer: B


                                      NEW QUESTION # 412
                                      You need to design an architecture that serves asynchronous predictions to determine whether a particular mission-critical machine part will fail. Your system collects data from multiple sensors from the machine. You want to build a model that will predict a failure in the next N minutes, given the average of each sensor's data from the past 12 hours. How should you design the architecture?

                                      Answer: D

                                      Explanation:
                                      * Reasoning: The question asks for a design that serves asynchronous predictions to determine whether a machine part will fail. This means that the predictions do not need to be returned immediately to the sensors, but can be processed in batches and sent to a downstream system for monitoring. Option B is the only one that uses a streaming data pipeline with Pub/Sub and Dataflow, which can handle real-time data ingestion, processing, and prediction. Option B also invokes the model for prediction, which is required by the question. The other options either use synchronous predictions (option A), batch predictions (options C and D), or do not invoke the model for prediction (option D).
                                      * References: You can learn more about the differences between synchronous, asynchronous, and batch predictions in Vertex AI from this document. You can also find examples of how to use Pub/Sub and
                                      * Dataflow for streaming data pipelines from this tutorial and this codelab.


                                      NEW QUESTION # 413
                                      You received a training-serving skew alert from a Vertex AI Model Monitoring job running in production. You retrained the model with more recent training data, and deployed it back to the Vertex AI endpoint, but you are still receiving the same alert. What should you do?

                                      Answer: B


                                      NEW QUESTION # 414
                                      ......

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