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Download DemoVendor: Google
Certifications: Google Certifications
Exam Code: PROFESSIONAL-MACHINE-LEARNING-ENGINEER
Exam Name: Professional Machine Learning Engineer
Updated: Mar 22, 2024
Q&As: 282
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VCE
You were asked to investigate failures of a production line component based on sensor readings. After receiving the dataset, you discover that less than 1% of the readings are positive examples representing failure incidents. You have tried to train several classification models, but none of them converge. How should you resolve the class imbalance problem?
A. Use the class distribution to generate 10% positive examples.
B. Use a convolutional neural network with max pooling and softmax activation.
C. Downsample the data with upweighting to create a sample with 10% positive examples.
D. Remove negative examples until the numbers of positive and negative examples are equal.
Correct Answer: C
https://developers.google.com/machine-learning/data-prep/construct/sampling-splitting/imbalanced-data#downsampling-and-upweighting
-less than 1% of the readings are positive
-none of them converge.
Downsampling (in this context) means training on a disproportionately low subset of the majority class examples.
You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using AI Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take? (Choose two.)
A. Decrease the number of parallel trials.
B. Decrease the range of floating-point values.
C. Set the early stopping parameter to TRUE.
D. Change the search algorithm from Bayesian search to random search.
E. Decrease the maximum number of trials during subsequent training phases.
Correct Answer: CE
https://cloud.google.com/ai-platform/training/docs/using-hyperparameter-tuning#early-stopping
You work for the AI team of an automobile company, and you are developing a visual defect detection model using TensorFlow and Keras. To improve your model performance, you want to incorporate some image augmentation functions such as translation, cropping, and contrast tweaking. You randomly apply these functions to each training batch. You want to optimize your data processing pipeline for run time and compute resources utilization. What should you do?
A. Embed the augmentation functions dynamically in the tf.Data pipeline.
B. Embed the augmentation functions dynamically as part of Keras generators.
C. Use Dataflow to create all possible augmentations, and store them as TFRecords.
D. Use Dataflow to create the augmentations dynamically per training run, and stage them as TFRecords.
Correct Answer: A
https://www.tensorflow.org/tutorials/load_data/images#using_tfdata_for_finer_control
Udom
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Ramon
GreeceThe answers are accurate. Well you should notice some of the questions are slightly changed. Be careful.
Hillary
HungaryI studied this material carefully and took every question seriously. At last, I passed the exam with high score. Prepare well and study much more.
zera
SwedenPassed today with 938. There are a lot of D&D but only 3-4 new. Thank you all!
ZoZo
AlgeriaThanks a lot and good lock for every body
Cadman
GreeceI'm feeling luck because there are no new questions! All the questions are form this dumps!!! Thanks for your help! Thank you all !!!
zuher
Indiathe content update quickly, there are many new questions in this dumps. thanks very much.
Mickey
South AfricaA very good study material, i just used one month and i passed the exam yesterday. So you can trust on it.
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United States
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