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

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

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Google Professional Machine Learning Engineer Sample Questions (Q48-Q53):

NEW QUESTION # 48
You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?

A. Train a TensorFlow model on Vertex AI.
B. Train a classification Vertex AutoML model.
C. Run a logistic regression job on BigQuery ML.
D. Use scikit-learn in Vertex AI Workbench user-managed notebooks with pandas library.

Answer: B

NEW QUESTION # 49
You are developing a model to detect fraudulent credit card transactions. You need to prioritize detection, because missing even one fraudulent transaction could severely impact the credit card holder. You used AutoML to train a model on users' profile information and credit card transaction data. After training the initial model, you notice that the model is failing to detect many fraudulent transactions. How should you increase the number of fraudulent transactions that are detected?

A. Reduce the maximum number of node hours for training.
B. Decrease the probability threshold to classify a fraudulent transaction.
C. Increase the probability threshold to classify a fraudulent transaction.
D. Add more non-fraudulent examples to the training set.

Answer: B

Explanation:
Lowering the probability threshold makes the model more sensitive to identifying positive cases (fraudulent transactions), increasing the true positive rate. This approach helps prioritize detection, even if it may lead to more false positives - which is acceptable in high-risk use cases like fraud detection.

NEW QUESTION # 50
You need to deploy a scikit-leam classification model to production. The model must be able to serve requests 24/7, and you expect millions of requests per second to the production application from 8 am to 7 pm. You need to minimize the cost of deployment. What should you do?

A. Deploy an online Vertex AI prediction endpoint. Set the max replica count to 100
B. Deploy an online Vertex AI prediction endpoint with one GPU per replica. Set the max replica count to 100
C. Deploy an online Vertex AI prediction endpoint with one GPU per replica. Set the max replica count to 1
D. Deploy an online Vertex AI prediction endpoint. Set the max replica count to 1

Answer: A

NEW QUESTION # 51
You are an ML engineer at a regulated insurance company. You are asked to develop an insurance approval model that accepts or rejects insurance applications from potential customers. What factors should you consider before building the model?

A. Traceability, reproducibility, and explainability
B. Redaction, reproducibility, and explainability
C. Federated learning, reproducibility, and explainability
D. Differential privacy federated learning, and explainability

Answer: A

Explanation:
https://www.oecd.org/finance/Impact-Big-Data-AI-in-the-Insurance-Sector.pdf
https://medium.com/artefact-engineering-and-data-science/including-ethics-best-practices-in-your-data-science-project-from-day-one-c15b26c2bf99

NEW QUESTION # 52
A retail chain has been ingesting purchasing records from its network of 20,000 stores to Amazon S3 using Amazon Kinesis Data Firehose. To support training an improved machine learning model, training records will require new but simple transformations, and some attributes will be combined. The model needs to be retrained daily.
Given the large number of stores and the legacy data ingestion, which change will require the LEAST amount of development effort?

A. Require that the stores to switch to capturing their data locally on AWS Storage Gateway for loading into Amazon S3, then use AWS Glue to do the transformation.
B. Insert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehose stream that transforms raw record attributes into simple transformed values using SQL.
C. Deploy an Amazon EMR cluster running Apache Spark with the transformation logic, and have the cluster run each day on the accumulating records in Amazon S3, outputting new/transformed records to Amazon S3.
D. Spin up a fleet of Amazon EC2 instances with the transformation logic, have them transform the data records accumulating on Amazon S3, and output the transformed records to Amazon S3.

Answer: B

NEW QUESTION # 53
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