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

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

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                                      Google Professional Machine Learning Engineer Sample Questions (Q285-Q290):

                                      NEW QUESTION # 285
                                      You are using Keras and TensorFlow to develop a fraud detection model Records of customer transactions are stored in a large table in BigQuery. You need to preprocess these records in a cost-effective and efficient way before you use them to train the model. The trained model will be used to perform batch inference in BigQuery. How should you implement the preprocessing workflow?

                                      Answer: D

                                      Explanation:
                                      * Option A is not the best answer because it requires using Apache Spark and Dataproc, which may incur additional cost and complexity for running and managing the cluster. It also requires saving the preprocessed data as CSV files in a Cloud Storage bucket, which may increase the storage cost and the data transfer latency.
                                      * Option B is not the best answer because it requires loading the data into a pandas DataFrame, which may not be scalable or efficient for large datasets. It also requires training the model directly on the DataFrame, which may not leverage the distributed computing capabilities of BigQuery.
                                      * Option C is the best answer because it allows performing preprocessing in BigQuery by using SQL, which is a cost-effective and efficient way to manipulate large datasets. It also allows using the BigQueryClient in TensorFlow to read the data directly from BigQuery, which is a convenient and fast way to access the data for training the model1.
                                      * Option D is not the best answer because it requires using Apache Beam and Dataflow, which may incur additional cost and complexity for running and managing the pipeline. It also requires saving the preprocessed data as CSV files in a Cloud Storage bucket, which may increase the storage cost and the data transfer latency.
                                      References:
                                      * 1: Read data from BigQuery | TensorFlow I/O


                                      NEW QUESTION # 286
                                      A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting.
                                      Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls.
                                      What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?

                                      Answer: A


                                      NEW QUESTION # 287
                                      You are a data scientist at an industrial equipment manufacturing company. You are developing a regression model to estimate the power consumption in the company's manufacturing plants based on sensor data collected from all of the plants. The sensors collect tens of millions of records every day. You need to schedule daily training runs for your model that use all the data collected up to the current date. You want your model to scale smoothly and require minimal development work. What should you do?

                                      Answer: C

                                      Explanation:
                                      BigQuery ML is a powerful tool that allows you to build and deploy machine learning models directly within BigQuery, Google's fully-managed, serverless data warehouse. It allows you to create regression models using SQL, which is a familiar and easy-to-use language for many data scientists. It also allows you to scale smoothly and require minimal development work since you don't have to worry about cluster management and it's fully-managed by Google.
                                      BigQuery ML also allows you to run your training on the same data where it's stored, this will minimize data movement, and thus minimize cost and time.
                                      Reference:
                                      BigQuery ML
                                      BigQuery ML for regression
                                      BigQuery ML for scalability


                                      NEW QUESTION # 288
                                      You need to develop an image classification model by using a large dataset that contains labeled images in a Cloud Storage bucket. What should you do?

                                      Answer: D


                                      NEW QUESTION # 289
                                      You are pre-training a large language model on Google Cloud. This model includes custom TensorFlow operations in the training loop Model training will use a large batch size, and you expect training to take several weeks You need to configure a training architecture that minimizes both training time and compute costs What should you do?

                                      Answer: D

                                      Explanation:
                                      According to the official exam guide1, one of the skills assessed in the exam is to "design, build, and productionalize ML models to solve business challenges using Google Cloud technologies". TPUs2 are Google's custom-developed application-specific integrated circuits (ASICs) used to accelerate machine learning workloads. TPUs are designed to handle large batch sizes, high dimensional data, and complex computations. TPUs can significantly reduce the training time and compute costs of large language models, especially when used with distributed training strategies, such as MultiWorkerMirroredStrategy3. Therefore, option D is the best way to configure a training architecture that minimizes both training time and compute costs for the given use case. The other options are not relevant or optimal for this scenario. References:
                                      * Professional ML Engineer Exam Guide
                                      * TPUs
                                      * MultiWorkerMirroredStrategy
                                      * Google Professional Machine Learning Certification Exam 2023
                                      * Latest Google Professional Machine Learning Engineer Actual Free Exam Questions


                                      NEW QUESTION # 290
                                      ......

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