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

Section	Objectives
Collaborating within and across teams to manage data and models	- Version control and reproducibility (e.g., DVC, MLOps) - Collaboration between Data Scientists, Data Engineers, and ML Engineers - Data management and governance
Scaling prototypes into ML models	- Training at scale (Distributed training, TPUs) - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) - Hyperparameter tuning
Monitoring ML solutions	- Performance monitoring and drift detection - Logging and alerting (Cloud Monitoring) - Model retraining strategies
Architecting low-code ML solutions	- AutoML capabilities and implementation - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) - Implementing BigQuery ML for basic models
Automating and orchestrating ML pipelines	- Triggering and scheduling pipelines - Vertex AI Pipelines (Kubeflow Pipelines) - CI/CD for ML systems
Serving and scaling models	- Batch prediction - Model optimization (Quantization, Distillation) - Hardware accelerators (GPU/TPU) in serving - Online prediction (Vertex AI Prediction)

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Google Professional Machine Learning Engineer Sample Questions (Q168-Q173):

NEW QUESTION # 168
You are working on a Neural Network-based project. The dataset provided to you has columns with different ranges. While preparing the data for model training, you discover that gradient optimization is having difficulty moving weights to a good solution. What should you do?

A. Use the representation transformation (normalization) technique.
B. Improve the data cleaning step by removing features with missing values.
C. Change the partitioning step to reduce the dimension of the test set and have a larger training set.
D. Use feature construction to combine the strongest features.

Answer: B

NEW QUESTION # 169
A company is observing low accuracy while training on the default built-in image classification algorithm in Amazon SageMaker. The Data Science team wants to use an Inception neural network architecture instead of a ResNet architecture.
Which of the following will accomplish this? (Choose two.)

A. Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network, and use this for model training.
B. Create a support case with the SageMaker team to change the default image classification algorithm to Inception.
C. Download and apt-get installthe inception network code into an Amazon EC2 instance and use this instance as a Jupyter notebook in Amazon SageMaker.
D. Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training.
E. Customize the built-in image classification algorithm to use Inception and use this for model training.

Answer: A,E

NEW QUESTION # 170
You are developing a recommendation engine for an online clothing store. The historical customer transaction data is stored in BigQuery and Cloud Storage. You need to perform exploratory data analysis (EDA), preprocessing and model training. You plan to rerun these EDA, preprocessing, and training steps as you experiment with different types of algorithms. You want to minimize the cost and development effort of running these steps as you experiment. How should you configure the environment?

A. Create a Vertex AI Workbench user-managed notebook on a Dataproc Hub, and use the %%bigquery magic commands in Jupyter to query the tables.
B. Create a Vertex AI Workbench user-managed notebook using the default VM instance, and use the %%bigquerv magic commands in Jupyter to query the tables.
C. Create a Vertex AI Workbench managed notebook on a Dataproc cluster, and use the spark- bigquery-connector to access the tables.
D. Create a Vertex AI Workbench managed notebook to browse and query the tables directly from the JupyterLab interface.

Answer: D

NEW QUESTION # 171
Your company manages an ecommerce website. You developed an ML model that recommends additional products to users in near real time based on items currently in the user's cart. The workflow will include the following processes.
1 The website will send a Pub/Sub message with the relevant data and then receive a message with the prediction from Pub/Sub.
2 Predictions will be stored in BigQuery
3. The model will be stored in a Cloud Storage bucket and will be updated frequently You want to minimize prediction latency and the effort required to update the model How should you reconfigure the architecture?

A. Expose the model as a Vertex Al endpoint Write a custom DoFn in a Dataflow job that calls the endpoint for prediction.
B. Use the Runlnference API with watchFilePatterr. in a Dataflow job that wraps around the model and serves predictions.
C. Write a Cloud Function that loads the model into memory for prediction Configure the function to be triggered when messages are sent to Pub/Sub.
D. Create a pipeline in Vertex Al Pipelines that performs preprocessing, prediction and postprocessing Configure the pipeline to be triggered by a Cloud Function when messages are sent to Pub/Sub.

Answer: C

NEW QUESTION # 172
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 Use the BigQuery console to run the create model statement Validate the results by using the ml. evaluate and ml. predict statements.
B. 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.
C. 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.
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: B

NEW QUESTION # 173
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