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

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

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                                      Google Professional Machine Learning Engineer Sample Questions (Q27-Q32):

                                      NEW QUESTION # 27
                                      You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?

                                      Answer: B

                                      Explanation:
                                      This is an important step in ensuring that the model has been developed and trained properly before it is put into production.
                                      Model performance monitoring is also a crucial step to ensure that the model is working as expected after it is released, and to identify areas where further refinement may be necessary.
                                      This would help to ensure that the model is performing well in production, and would also help to identify any issues that may arise over time.
                                      Additionally, this would allow the team to better understand what changes need to be made in order to help the model perform optimally in production.


                                      NEW QUESTION # 28
                                      You recently deployed a scikit-learn model to a Vertex Al endpoint You are now testing the model on live production traffic While monitoring the endpoint. you discover twice as many requests per hour than expected throughout the day You want the endpoint to efficiently scale when the demand increases in the future to prevent users from experiencing high latency What should you do?

                                      Answer: B


                                      NEW QUESTION # 29
                                      You work for a hotel and have a dataset that contains customers' written comments scanned from paper-based customer feedback forms which are stored as PDF files Every form has the same layout. You need to quickly predict an overall satisfaction score from the customer comments on each form. How should you accomplish this task'?

                                      Answer: B

                                      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". Document AI2 is a document understanding platform that takes unstructured data from documents and transforms it into structured data, making it easier to understand, analyze, and consume. Document AI Workbench3 allows you to create custom extractors to parse the text in specific sections of your documents. Natural Language API4 is a service that provides natural language understanding technologies, such as sentiment analysis, entity analysis, and other text annotations. The analyzeSentiment feature5 inspects the given text and identifies the prevailing emotional opinion within the text, especially to determine a writer's attitude as positive, negative, or neutral. Therefore, option C is the best way to accomplish the task of predicting an overall satisfaction score from the customer comments on each form. The other options are not relevant or optimal for this scenario. References:
                                      * Professional ML Engineer Exam Guide
                                      * Document AI
                                      * Document AI Workbench
                                      * Natural Language API
                                      * Sentiment analysis
                                      * Google Professional Machine Learning Certification Exam 2023
                                      * Latest Google Professional Machine Learning Engineer Actual Free Exam Questions


                                      NEW QUESTION # 30
                                      Your work for a textile manufacturing company. Your company has hundreds of machines, and each machine has many sensors. Your team used the sensory data to build hundreds of ML models that detect machine anomalies. Models are retrained daily, and you need to deploy these models in a cost-effective way. The models must operate 24/7 without downtime and make sub millisecond predictions. What should you do?

                                      Answer: B


                                      NEW QUESTION # 31
                                      You are building a model to predict daily temperatures. You split the data randomly and then transformed the training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model performed with 97% accuracy; however, after deploying to production, the model's accuracy dropped to 66%. How can you make your production model more accurate?

                                      Answer: A

                                      Explanation:
                                      https://community.rapidminer.com/discussion/32592/normalising-data-before-data-split-or-after


                                      NEW QUESTION # 32
                                      ......

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