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

Section	Objectives
Topic 1: 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
Topic 2: Deployment and operations	- Monitoring and maintenance 1. Monitor model drift and performance 2. Retraining and lifecycle management - Model deployment 1. Deploy models using Vertex AI endpoints 2. Batch and online prediction systems
Topic 3: 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
Topic 4: Data preparation and processing	- Feature engineering 1. Transform and preprocess datasets 2. Feature selection and representation techniques - Data ingestion and pipelines 1. Use BigQuery and data processing services 2. Build data pipelines for training and serving
Topic 5: ML pipeline automation and orchestration	- Pipeline design 1. Build end-to-end ML pipelines 2. Use Vertex AI Pipelines

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Google Professional Machine Learning Engineer Sample Questions (Q303-Q308):

NEW QUESTION # 303
You need to use TensorFlow to train an image classification model. Your dataset is located in a Cloud Storage directory and contains millions of labeled images. Before training the model, you need to prepare the data. You want the data preprocessing and model training workflow to be as efficient, scalable, and low maintenance as possible. What should you do?

A. 1. Create a Dataflow job that moves the images into multiple Cloud Storage directories, where each directory is named according to the corresponding label
2. Reference tfds.folder_dataset:ImageFolder in the training script.
3. Train the model by using Vertex AI Training with a V100 GPU.
B. 1. Create a Dataflow job that creates sharded TFRecord files in a Cloud Storage directory.
2. Reference tf.data.TFRecordDataset in the training script.
3. Train the model by using Vertex AI Training with a V100 GPU.
C. 1. Create a Jupyter notebook that uses an n1-standard-64, V100 GPU Vertex AI Workbench instance.
2. Write a Python script that copies the images into multiple Cloud Storage directories, where each. directory is named according to the corresponding label.
3. Reference tfds.foladr_dataset.ImageFolder in the training script.
4. Train the model by using the Workbench instance.
D. 1. Create a Jupyter notebook that uses an nt-standard-64 V100 GPU Vertex AI Workbench instance.
2. Write a Python script that creates sharded TFRecord files in a directory inside the instance.
3. Reference tf.data.TFRecordDataset in the training script.
4. Train the model by using the Workbench instance.

Answer: B

NEW QUESTION # 304
You work on a team that builds state-of-the-art deep learning models by using the TensorFlow framework.
Your team runs multiple ML experiments each week which makes it difficult to track the experiment runs.
You want a simple approach to effectively track, visualize and debug ML experiment runs on Google Cloud while minimizing any overhead code. How should you proceed?

A. Set up a Cloud Function to write and save metrics files to a Cloud Storage Bucket Configure a Google Cloud VM to host TensorBoard locally for visualization.
B. Set up a Vertex Al Workbench notebook instance Use the instance to save metrics data in a Cloud Storage bucket and to host TensorBoard locally for visualization.
C. Set up Vertex Al Experiments to track metrics and parameters Configure Vertex Al TensorBoard for visualization.
D. Set up a Cloud Function to write and save metrics files to a BigQuery table. Configure a Google Cloud VM to host TensorBoard locally for visualization.

Answer: C

NEW QUESTION # 305
You developed a Vertex Al ML pipeline that consists of preprocessing and training steps and each set of steps runs on a separate custom Docker image Your organization uses GitHub and GitHub Actions as CI/CD to run unit and integration tests You need to automate the model retraining workflow so that it can be initiated both manually and when a new version of the code is merged in the main branch You want to minimize the steps required to build the workflow while also allowing for maximum flexibility How should you configure the CI/CD workflow?

A. Trigger GitHub Actions to run the tests launch a job on Cloud Run to build custom Docker images push the images to Artifact Registry and launch the pipeline in Vertex Al Pipelines.
B. Trigger GitHub Actions to run the tests build custom Docker images push the images to Artifact Registry, and launch the pipeline in Vertex Al Pipelines.
C. Trigger GitHub Actions to run the tests launch a Cloud Build workflow to build custom Dicker images, push the images to Artifact Registry, and launch the pipeline in Vertex Al Pipelines.
D. Trigger a Cloud Build workflow to run tests build custom Docker images, push the images to Artifact Registry and launch the pipeline in Vertex Al Pipelines.

Answer: B

NEW QUESTION # 306
You work on a team that builds state-of-the-art deep learning models by using the TensorFlow framework. Your team runs multiple ML experiments each week, which makes it difficult to track the experiment runs. You want a simple approach to effectively track, visualize, and debug ML experiment runs on Google Cloud while minimizing any overhead code. How should you proceed?

A. Set up a Vertex AI Workbench notebook instance. Use the instance to save metrics data in a Cloud Storage bucket and to host TensorBoard locally for visualization.
B. Set up a Cloud Function to write and save metrics files to a Cloud Storage bucket. Configure a Google Cloud VM to host TensorBoard locally for visualization.
C. Set up a Cloud Function to write and save metrics files to a BigQuery table. Configure a Google Cloud VM to host TensorBoard locally for visualization.
D. Set up Vertex AI Experiments to track metrics and parameters. Configure Vertex AI TensorBoard for visualization.

Answer: D

NEW QUESTION # 307
You work for a company that sells corporate electronic products to thousands of businesses worldwide. Your company stores historical customer data in BigQuery. You need to build a model that predicts customer lifetime value over the next three years. You want to use the simplest approach to build the model and you want to have access to visualization tools. What should you do?

A. Create a Vertex Al Workbench notebook to perform exploratory data analysis Use IPython magics to create a new BigQuery table with input features, create the model and validate the results by using the create model, ml. evaluates, and ml. predict statements.
B. Create a Vertex Al Workbench notebook to perform exploratory data analysis and create input features Save the features as a CSV file in Cloud Storage Import the CSV file as a new BigQuery table Use the BigQuery console to run the create model statement Validate the results by using the ml. evaluate and ml. predict statements.
C. Create a Vertex Al Workbench notebook to perform exploratory data analysis. Use IPython magics to create a new BigQuery table with input features Use the BigQuery console to run the create model statement Validate the results by using the ml. evaluate and ml. predict statements.
D. Run the create model statement from the BigQuery console to create an AutoML model Validate the results by using the ml. evaluate and ml. predict statements.

Answer: D

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 build a model that predicts customer lifetime value over the next three years, by using the create model statement. The create model statement is a SQL command that allows you to create and train an ML model using your data in BigQuery. You can use the create model statement to create an AutoML model, which is a type of model that automatically selects the best features and architecture for your data. By using an AutoML model, you can use the simplest approach to build the model, without writing any code or performing any feature engineering. You can also use the ml.evaluate and ml.predict statements to validate the results of your model. The ml.evaluate statement is a SQL command that allows you to evaluate the performance and quality of your model using various metrics. The ml.predict statement is a SQL command that allows you to make predictions using your model and new data. You can also use the BigQuery console to access visualization tools, such as charts and graphs, to explore and analyze your data and model results. By using the BigQuery console, the create model statement, and the ml.evaluate and ml.predict statements, you can build and validate a model that predicts customer lifetime value over the next three years, and have access to visualization tools. Reference:
BigQuery documentation
create model statement documentation
ml.evaluate statement documentation
ml.predict statement documentation
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

NEW QUESTION # 308
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