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

Section	Weight	Objectives
Topic 1: Monitor and optimize AI solutions	16%	- Troubleshoot and maintain production systems - Optimize cost, latency, and resource usage - Monitor data quality and pipeline health - Monitor model performance, fairness, and drift
Topic 2: Automate and orchestrate ML pipelines	18%	- Design end-to-end ML workflows - Use Vertex AI Pipelines, TFX, and other orchestration tools - Implement CI/CD for ML systems - Automate retraining and model updates
Topic 3: Architect low-code AI solutions	12%	- Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Apply responsible AI principles to low-code designs - Identify use cases for low-code/no-code AI tools
Topic 4: Train and deploy models	20%	- Use Vertex AI deployment features and infrastructure - Configure training jobs and environments - Deploy models for online, batch, and streaming prediction - Implement generative AI deployment patterns
Topic 5: Scale prototypes into AI models	18%	- Optimize model performance and generalization - Work with foundation models and generative AI techniques - Design and run experiments - Select appropriate model architectures and frameworks
Topic 6: Collaborate to manage data and models	16%	- Address data privacy, compliance, and governance - Organize and prepare enterprise data 1. Use Cloud Storage, BigQuery, Spanner, Cloud SQL, and data processing tools 2. Work with structured, unstructured, and semi-structured data - Manage datasets and features in Vertex AI

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Google Professional Machine Learning Engineer Sample Questions (Q211-Q216):

NEW QUESTION # 211
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?

A. Ensure that model performance is monitored
B. Ensure that all hyperparameters are tuned
C. Ensure that feature expectations are captured in the schema
D. Ensure that training is reproducible

Answer: D

NEW QUESTION # 212
An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen.
Which combination of algorithms would provide the appropriate insights? (Select TWO.)

A. The Random Cut Forest (RCF) algorithm
B. The k-means algorithm
C. The factorization machines (FM) algorithm
D. The Latent Dirichlet Allocation (LDA) algorithm
E. The principal component analysis (PCA) algorithm

Answer: B,E

Explanation:
The PCA and K-means algorithms are useful in collection of data using census form.

NEW QUESTION # 213
One of your models is trained using data provided by a third-party data broker. The data broker does not reliably notify you of formatting changes in the data. You want to make your model training pipeline more robust to issues like this. What should you do?

A. Use TensorFlow Data Validation to detect and flag schema anomalies.
B. Use TensorFlow Transform to create a preprocessing component that will normalize data to the expected distribution, and replace values that don't match the schema with 0.
C. Use custom TensorFlow functions at the start of your model training to detect and flag known formatting errors.
D. Use tf.math to analyze the data, compute summary statistics, and flag statistical anomalies.

Answer: A

Explanation:
TensorFlow Data Validation (TFDV) is a library that helps you understand, validate, and monitor your data for machine learning. It can automatically detect and report schema anomalies, such as missing features, new features, or different data types, in your data. It can also generate descriptive statistics and data visualizations to help you explore and debug your data. TFDV can be integrated with your model training pipeline to ensure data quality and consistency throughout the machine learning lifecycle. References:
* TensorFlow Data Validation
* Data Validation | TensorFlow
* Data Validation | Machine Learning Crash Course | Google Developers

NEW QUESTION # 214
You recently developed a wide and deep model in TensorFlow. You generated training datasets using a SQL script that preprocessed raw data in BigQuery by performing instance-level transformations of the dat a. You need to create a training pipeline to retrain the model on a weekly basis. The trained model will be used to generate daily recommendations. You want to minimize model development and training time. How should you develop the training pipeline?

A. Use the TensorFlow Extended SDK to implement the pipeline Use the Examplegen component with the BigQuery executor to ingest the data the Transform component to preprocess the data, and the Trainer component to launch a Vertex Al training job.
B. Use the TensorFlow Extended SDK to implement the pipeline Implement the preprocessing steps as part of the input_fn of the model Use the ExampleGen component with the BigQuery executor to ingest the data and the Trainer component to launch a Vertex Al training job.
C. Use the Kubeflow Pipelines SDK to implement the pipeline Use the BigQueryJobop component to run the preprocessing script and the customTrainingJobop component to launch a Vertex Al training job.
D. Use the Kubeflow Pipelines SDK to implement the pipeline. Use the dataflowpythonjobopcomponent to preprocess the data and the customTraining JobOp component to launch a Vertex Al training job.

Answer: C

NEW QUESTION # 215
You work for an international manufacturing organization that ships scientific products all over the world. Instruction manuals for these products need to be translated to 15 different languages.
Your organization's leadership team wants to start using machine learning to reduce the cost of manual human translations and increase translation speed. You need to implement a scalable solution that maximizes accuracy and minimizes operational overhead. You also want to include a process to evaluate and fix incorrect translations. What should you do?

A. Use AutoML Translation to train a model. Configure a Translation Hub project, and use the trained model to translate the documents. Use human reviewers to evaluate the incorrect translations.
B. Create a Vertex AI pipeline that processes the documents launches, an AutoML Translation training job, evaluates the translations and deploys the model to a Vertex AI endpoint with autoscaling and model monitoring. When there is a predetermined skew between training and live data, re-trigger the pipeline with the latest data.
C. Use Vertex AI custom training jobs to fine-tune a state-of-the-art open source pretrained model with your data. Deploy the model to a Vertex AI endpoint with autoscaling and model monitoring.
When there is a predetermined skew between the training and live data, configure a trigger to run another training job with the latest data.
D. Create a workflow using Cloud Function triggers. Configure a Cloud Function that is triggered when documents are uploaded to an input Cloud Storage bucket. Configure another Cloud Function that translates the documents using the Cloud Translation API, and saves the translations to an output Cloud Storage bucket. Use human reviewers to evaluate the incorrect translations.

Answer: A

NEW QUESTION # 216
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