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Google Professional-Machine-Learning-Engineer Exam With Confidence Using Practice Dumps

Exam Code:
Professional-Machine-Learning-Engineer
Exam Name:
Google Professional Machine Learning Engineer
Certification:
Vendor:
Questions:
285
Last Updated:
Jun 15, 2025
Exam Status:
Stable
Google Professional-Machine-Learning-Engineer

Professional-Machine-Learning-Engineer: Machine Learning Engineer Exam 2025 Study Guide Pdf and Test Engine

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Google Professional Machine Learning Engineer Questions and Answers

Question 1

Your task is classify if a company logo is present on an image. You found out that 96% of a data does not include a logo. You are dealing with data imbalance problem. Which metric do you use to evaluate to model?

Options:

A.

F1 Score

B.

RMSE

C.

F Score with higher precision weighting than recall

D.

F Score with higher recall weighted than precision

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Question 2

You work with a learn of researchers lo develop state-of-the-art algorithms for financial analysis. Your team develops and debugs complex models in TensorFlow. You want to maintain the ease of debugging while also reducing the model training time. How should you set up your training environment?

Options:

A.

Configure a v3-8 TPU VM.

B.

Configure a v3-8 TPU node.

C.

Configure a c2-standard-60 VM without GPUs.

D, Configure a n1-standard-4 VM with 1 NVIDIA P100 GPU.

Question 3

You have trained a DNN regressor with TensorFlow to predict housing prices using a set of predictive features. Your default precision is tf.float64, and you use a standard TensorFlow estimator;

estimator = tf.estimator.DNNRegressor(

feature_columns=[YOUR_LIST_OF_FEATURES],

hidden_units-[1024, 512, 256],

dropout=None)

Your model performs well, but Just before deploying it to production, you discover that your current serving latency is 10ms @ 90 percentile and you currently serve on CPUs. Your production requirements expect a model latency of 8ms @ 90 percentile. You are willing to accept a small decrease in performance in order to reach the latency requirement Therefore your plan is to improve latency while evaluating how much the model's prediction decreases. What should you first try to quickly lower the serving latency?

Options:

A.

Increase the dropout rate to 0.8 in_PREDICT mode by adjusting the TensorFlow Serving parameters

B.

Increase the dropout rate to 0.8 and retrain your model.

C.

Switch from CPU to GPU serving

D.

Apply quantization to your SavedModel by reducing the floating point precision to tf.float16.