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

SectionObjectives
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- Model deployment
          • 1. Batch and online prediction systems
            • 2. Deploy models using Vertex AI endpoints
              - Monitoring and maintenance
              • 1. Retraining and lifecycle management
                • 2. Monitor model drift and performance
                  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
                          ML pipeline automation and orchestration- Pipeline design
                          • 1. Build end-to-end ML pipelines
                            • 2. Use Vertex AI Pipelines
                              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

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                                      Cert Professional-Machine-Learning-Engineer Guide - Professional-Machine-Learning-Engineer Exam Tests

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                                      Google Professional Machine Learning Engineer Sample Questions (Q350-Q355):

                                      NEW QUESTION # 350
                                      A Machine Learning Specialist is assigned a TensorFlow project using Amazon SageMaker for training, and needs to continue working for an extended period with no Wi-Fi access.
                                      Which approach should the Specialist use to continue working?

                                      Answer: D

                                      Explanation:
                                      Explanation


                                      NEW QUESTION # 351
                                      You work for a gaming company that manages a popular online multiplayer game where teams with 6 players play against each other in 5-minute battles. There are many new players every day. You need to build a model that automatically assigns available players to teams in real time. User research indicates that the game is more enjoyable when battles have players with similar skill levels. Which business metrics should you track to measure your model's performance?

                                      Answer: B


                                      NEW QUESTION # 352
                                      You work at a large organization that recently decided to move their ML and data workloads to Google Cloud. The data engineering team has exported the structured data to a Cloud Storage bucket in Avro format.
                                      You need to propose a workflow that performs analytics, creates features, and hosts the features that your ML models use for online prediction How should you configure the pipeline?

                                      Answer: C

                                      Explanation:
                                      BigQuery is a service that allows you to store and query large amounts of data in a scalable and cost-effective way. You can use BigQuery to ingest the Avro files from the Cloud Storage bucket and perform analytics on the structured data. Avro is a binary file format that can store complex data types and schemas. You can use the bq load command or the BigQuery API to load the Avro files into a BigQuery table. You can then use SQL queries to analyze the data and generate insights. Dataflow is a service that allows you to create and run scalable and portable data processing pipelines on Google Cloud. You can use Dataflow to create the features for your ML models, such as transforming, aggregating, and encoding the data. You can use the Apache Beam SDK to write your Dataflow pipeline code in Python or Java. You can also use the built-in transforms or custom transforms to apply the feature engineering logic to your data. Vertex AI Feature Store is a service that allows you to store and manage your ML features on Google Cloud. You can use Vertex AI Feature Store to host the features that your ML models use for online prediction. Online prediction is a type of prediction that provides low-latency responses to individual or small batches of input data. You can use the Vertex AI Feature Store API to write the features from your Dataflow pipeline to a feature store entity type. You can then use the Vertex AI Feature Store online serving API to read the features from the feature store and pass them to your ML models for online prediction. By using BigQuery, Dataflow, and Vertex AI Feature Store, you can configure a pipeline that performs analytics, creates features, and hosts the features that your ML models use for online prediction. References:
                                      * BigQuery documentation
                                      * Dataflow documentation
                                      * Vertex AI Feature Store documentation
                                      * Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate


                                      NEW QUESTION # 353
                                      You are developing a demand forecasting model for a large online retailer. The company has
                                      20,000 SKUs that are sold through a single distribution center. You have three years of historical data. You need to provide a daily forecast at the SKU level for the next two weeks. You need to develop the first version quickly and minimize the development effort while delivering high forecasting accuracy. What should you do?

                                      Answer: C

                                      Explanation:
                                      BigQuery ML can train and manage separate time-series forecasts for all 20,000 SKUs in a single SQL model by using the SKU identifier as TIME_SERIES_ID_COL. ARIMA_PLUS_XREG automates model selection, trend and seasonality handling, and supports additional explanatory variables, providing a scalable, accurate solution with minimal development effort.
                                      Reference:
                                      https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-multivariate-time-series
                                      https://docs.cloud.google.com/bigquery/docs/arima-plus-xreg-multiple-time-series-forecasting-tutorial


                                      NEW QUESTION # 354
                                      A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena. The dataset contains more than 800,000 records stored as plaintext CSV files. Each record contains
                                      200 columns and is approximately 1.5 MB in size. Most queries will span 5 to 10 columns only.
                                      How should the Machine Learning Specialist transform the dataset to minimize query runtime?

                                      Answer: A

                                      Explanation:
                                      Using compressions will reduce the amount of data scanned by Amazon Athena, and also reduce your S3 bucket storage. It's a Win-Win for your AWS bill. Supported formats: GZIP, LZO, SNAPPY (Parquet) and ZLIB.
                                      Reference: https://www.cloudforecast.io/blog/using-parquet-on-athena-to-save-money-on-aws/


                                      NEW QUESTION # 355
                                      ......

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