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

Section	Objectives
Topic 1: Monitoring ML solutions	- Model retraining strategies - Performance monitoring and drift detection - Logging and alerting (Cloud Monitoring)
Topic 2: Automating and orchestrating ML pipelines	- Vertex AI Pipelines (Kubeflow Pipelines) - Triggering and scheduling pipelines - CI/CD for ML systems
Topic 3: 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
Topic 4: Scaling prototypes into ML models	- Hyperparameter tuning - Training at scale (Distributed training, TPUs) - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn)
Topic 5: Architecting low-code ML solutions	- Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) - AutoML capabilities and implementation - Implementing BigQuery ML for basic models
Topic 6: Serving and scaling models	- Model optimization (Quantization, Distillation) - Batch prediction - Hardware accelerators (GPU/TPU) in serving - Online prediction (Vertex AI Prediction)

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Google Professional Machine Learning Engineer Sample Questions (Q203-Q208):

NEW QUESTION # 203
You recently developed a deep learning model using Keras, and now you are experimenting with different training strategies. First, you trained the model using a single GPU, but the training process was too slow. Next, you distributed the training across 4 GPUs using tf.distribute.MirroredStrategy (with no other changes), but you did not observe a decrease in training time. What should you do?

A. Use a TPU with tf.distribute.TPUStrategy.
B. Distribute the dataset with tf.distribute.Strategy.experimental_distribute_dataset
C. Create a custom training loop.
D. Increase the batch size.

Answer: C

Explanation:
This would allow you to tailor the training process to your specific needs and requirements, and it would also allow for more flexible experimentation with different training strategies.
Additionally, creating a custom training loop could result in faster training times compared to using a single GPU or the distributed training strategies currently available in Keras.

NEW QUESTION # 204
Your company manages a video sharing website where users can watch and upload videos. You need to create an ML model to predict which newly uploaded videos will be the most popular so that those videos can be prioritized on your company's website. Which result should you use to determine whether the model is successful?

A. The model predicts 97.5% of the most popular clickbait videos measured by number of clicks.
B. The model predicts videos as popular if the user who uploads them has over 10,000 likes.
C. The model predicts 95% of the most popular videos measured by watch time within 30 days of being uploaded.
D. The Pearson correlation coefficient between the log-transformed number of views after 7 days and 30 days after publication is equal to 0.

Answer: C

Explanation:
https://developers.google.com/machine-learning/problem-framing/framing#quantify-it

NEW QUESTION # 205
You are an ML engineer at a manufacturing company. You need to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. You want your model to preprocess the images with lower computation to quickly extract features of defects in products. Which approach should you use to build the model?

A. Convolutional Neural Networks (CNN)
B. Recommender system
C. Reinforcement learning
D. Recurrent Neural Networks (RNN)

Answer: A

Explanation:
CNN is good for images processing.
https://developers.google.com/machine-learning/practica/image-classification/convolutional-neural-networks

NEW QUESTION # 206
You have successfully deployed to production a large and complex TensorFlow model trained on tabular data. You want to predict the lifetime value (LTV) field for each subscription stored in the BigQuery table named subscription. subscriptionPurchase in the project named my-fortune500- company-project.
You have organized all your training code, from preprocessing data from the BigQuery table up to deploying the validated model to the Vertex AI endpoint, into a TensorFlow Extended (TFX) pipeline. You want to prevent prediction drift, i.e., a situation when a feature data distribution in production changes significantly over time. What should you do?

A. Add a model monitoring job where 90% of incoming predictions are sampled 24 hours.
B. Implement continuous retraining of the model daily using Vertex AI Pipelines.
C. Add a model monitoring job where 10% of incoming predictions are sampled 24 hours.
D. Add a model monitoring job where 10% of incoming predictions are sampled every hour.

Answer: C

Explanation:
https://cloud.google.com/vertex-ai/docs/model-monitoring/overview
https://cloud.google.com/vertex-ai/docs/model-monitoring/using-model-monitoring#drift-detection

NEW QUESTION # 207
You work for an advertising company and want to understand the effectiveness of your company's latest advertising campaign. You have streamed 500 MB of campaign data into BigQuery. You want to query the table, and then manipulate the results of that query with a pandas dataframe in an Al Platform notebook. What should you do?

A. Download your table from BigQuery as a local CSV file, and upload it to your Al Platform notebook instance Use pandas. read_csv to ingest the file as a pandas dataframe
B. Export your table as a CSV file from BigQuery to Google Drive, and use the Google Drive API to ingest the file into your notebook instance
C. From a bash cell in your Al Platform notebook, use the bq extract command to export the table as a CSV file to Cloud Storage, and then use gsutii cp to copy the data into the notebook Use pandas. read_csv to ingest the file as a pandas dataframe
D. Use Al Platform Notebooks' BigQuery cell magic to query the data, and ingest the results as a pandas dataframe

Answer: B

NEW QUESTION # 208
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