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

Section	Objectives
ML model development	- Model training and tuning 1. Train models using TensorFlow / Vertex AI 2. Hyperparameter tuning and optimization - Evaluation 1. Model validation strategies 2. Evaluate model performance metrics
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
Designing ML solutions	- ML architecture design 1. Select appropriate ML models and approaches 2. Design scalable ML systems on GCP - Framing ML problems 1. Translate business problems into ML tasks 2. Define success metrics and evaluation criteria
Deployment and operations	- Model deployment 1. Deploy models using Vertex AI endpoints 2. Batch and online prediction systems - Monitoring and maintenance 1. Monitor model drift and performance 2. Retraining and lifecycle management
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 (Q16-Q21):

NEW QUESTION # 16
You are developing an ML model intended to classify whether X-Ray images indicate bone fracture risk. You have trained on Api Resnet architecture on Vertex AI using a TPU as an accelerator, however you are unsatisfied with the trainning time and use memory usage. You want to quickly iterate your training code but make minimal changes to the code. You also want to minimize impact on the models accuracy. What should you do?

A. Configure your model to use bfloat16 instead float32
B. Reduce the number of layers in the model architecture
C. Reduce the global batch size from 1024 to 256
D. Reduce the dimensions of the images used un the model

Answer: A

Explanation:
Using bfloat16 instead of float32 can reduce the memory usage and training time of the model, while having minimal impact on the accuracy. Bfloat16 is a 16-bit floating-point format that preserves the range of 32-bit floating-point numbers, but reduces the precision from 24 bits to 8 bits. This means that bfloat16 can store the same magnitude of numbers as float32, but with less detail. Bfloat16 is supported by TPUs and some GPUs, and can be used as a drop-in replacement for float32 in most cases. Bfloat16 can also improve the numerical stability of the model, as it reduces the risk of overflow and underflow errors.
Reducing the global batch size, the number of layers, or the dimensions of the images can also reduce the memory usage and training time of the model, but they can also affect the model's accuracy and performance.
Reducing the global batch size can make the model less stable and converge slower, as it reduces the amount of information available for each gradient update. Reducing the number of layers can make the model less expressive and powerful, as it reduces the depth and complexity of the network. Reducing the dimensions of the images can make the model less accurate and robust, as it reduces the resolution and quality of the input data. References:
* Bfloat16: The secret to high performance on Cloud TPUs
* Bfloat16 floating-point format
* How does Batch Size impact your model learning

NEW QUESTION # 17
You are developing an ML model that predicts the cost of used automobiles based on data such as location, condition model type color, and engine-'battery efficiency. The data is updated every night Car dealerships will use the model to determine appropriate car prices. You created a Vertex Al pipeline that reads the data splits the data into training/evaluation/test sets performs feature engineering trains the model by using the training dataset and validates the model by using the evaluation dataset. You need to configure a retraining workflow that minimizes cost What should you do?

A. Compare the results to the evaluation results from a previous run If the performance improved deploy the model to a Vertex Al endpoint Configure a cron job to redeploy the pipeline every night.
B. Compare the results to the evaluation results from a previous run If the performance improved deploy the model to a Vertex Al endpoint with training/serving skew threshold model monitoring. When the model monitoring threshold is triggered, redeploy the pipeline.
C. Compare the training and evaluation losses of the current run If the losses are similar deploy the model to a Vertex Al endpoint with training/serving skew threshold model monitoring When the model monitoring threshold is tnggered redeploy the pipeline.
D. Compare the training and evaluation losses of the current run If the losses are similar, deploy the model to a Vertex AI endpoint Configure a cron job to redeploy the pipeline every night.

Answer: B

NEW QUESTION # 18
You are developing an ML model to identify your company s products in images. You have access to over one million images in a Cloud Storage bucket. You plan to experiment with different TensorFlow models by using Vertex Al Training You need to read images at scale during training while minimizing data I/O bottlenecks What should you do?

A. Load the images directly into the Vertex Al compute nodes by using Cloud Storage FUSE Read the images by using the tf .data.Dataset.from_tensor_slices function.
B. Create a Vertex Al managed dataset from your image data Access the aip_training_data_uri environment variable to read the images by using the tf. data. Dataset. Iist_flies function.
C. Store the URLs of the images in a CSV file Read the file by using the tf.data.experomental.CsvDataset function.
D. Convert the images to TFRecords and store them in a Cloud Storage bucket Read the TFRecords by using the tf. ciata.TFRecordDataset function.

Answer: A

NEW QUESTION # 19
As the lead ML Engineer for your company, you are responsible for building ML models to digitize scanned customer forms. You have developed a TensorFlow model that converts the scanned images into text and stores them in Cloud Storage. You need to use your ML model on the aggregated data collected at the end of each day with minimal manual intervention. What should you do?

A. Create a serving pipeline in Compute Engine for prediction.
B. Use the batch prediction functionality of AI Platform.
C. Deploy the model on AI Platform and create a version of it for online inference.
D. Use Cloud Functions for prediction each time a new data point is ingested.

Answer: B

Explanation:
You need to use your ML model on the aggregated data collected at the end of each day with minimal manual intervention.

NEW QUESTION # 20
You work as an analyst at a large banking firm. You are developing a robust, scalable ML pipeline to train several regression and classification models. Your primary focus for the pipeline is model interpretability. You want to productionize the pipeline as quickly as possible What should you do?

A. Use Cloud Composer to build the training pipelines for custom deep learning-based models.
B. Use Tabular Workflow forTabel through Vertex Al Pipelines to train attention-based models.
C. Use Tabular Workflow for Wide & Deep through Vertex Al Pipelines to jointly train wide linear models and deep neural networks.
D. Use Google Kubernetes Engine to build a custom training pipeline for XGBoost-based models.

Answer: A

Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to "automate and orchestrate ML pipelines using Cloud Composer". Cloud Composer2 is a fully managed workflow orchestration service that uses Apache Airflow to create, schedule, monitor, and manage workflows. Cloud Composer allows you to build custom training pipelines for deep learning-based models and integrate them with other Google Cloud services. You can also use Cloud Composer to implement model interpretability techniques, such as feature attributions, explainable AI, or model debugging3. The other options are not relevant or optimal for this scenario. Reference:
Professional ML Engineer Exam Guide
Cloud Composer
Model interpretability with Cloud Composer
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

NEW QUESTION # 21
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