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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Certified - Professional Machine Learning Engineer |
| Exam Number: | Professional-Machine-Learning-Engineer |
| Available Languages: | English, Japanese |
| Certificate Validity Period: | 2 years |
| Exam Format: | Multiple choice, Multiple select |
| Exam Duration: | 120 minutes |
| Real Exam Qty: | 50-60 |
| Passing Score: | Not publicly disclosed (Pass/Fail) |
| Exam Price: | $200 USD |
| Related Certifications: | Google Cloud Certified - Professional Data Engineer |
| Sample Questions: | Google Professional-Machine-Learning-Engineer Sample Questions |
| Exam Way: | Online (proctored) or Test center (Kryterion) |
| Pre Condition: | Recommended 3+ years of industry experience with ML models and 1+ year of experience using Google Cloud. |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/machine-learning-engineer |
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Google Professional Machine Learning Engineer Certification Exam is a highly sought-after credential for individuals seeking to demonstrate their proficiency in designing and implementing machine learning models and systems on the Google Cloud Platform. Google Professional Machine Learning Engineer certification is designed for professionals with a solid foundation in machine learning concepts and practical experience in building and deploying machine learning models.
NEW QUESTION # 103
You work for a manufacturing company that owns a high-value machine which has several machine settings and multiple sensors. A history of the machine's hourly sensor readings and known failure event data are stored in BigQuery. You need to predict if the machine will fail within the next 3 days in order to schedule maintenance before the machine fails. Which data preparation and model training steps should you take?
Answer: A
Explanation:
A is not correct because a rolling average is a better feature engineering technique, as it will smooth out the noise and fluctuation in the data to demonstrate whether there is a trend. Using the max value could be an artifact of some noise and may not capture the trend accurately.
B is not correct because a rolling average is a better feature engineering technique, as it will smooth out the noise and fluctuation in the data to demonstrate whether there is a trend. Using the min value could be an artifact of some noise and may not capture the trend accurately.
C is not correct because the model training does not balance class labels for an imbalanced dataset.
D is correct because it uses the rolling average of the sensor data and balances the weights using the BQML auto class weight balance parameter.
https://cloud.google.com/dataprep/docs/html/ROLLINGAVERAGE-Function_57344753
https://cloud.google.com/dataprep/docs/html/AVERAGE-Function_57344661
https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create
https://en.wikipedia.org/wiki/Precision_and_recall
https://en.wikipedia.org/wiki/Sensitivity_and_specificity
https://en.wikipedia.org/wiki/Moving_average
NEW QUESTION # 104
You are training an ML model on a large dataset. You are using a TPU to accelerate the training process You notice that the training process is taking longer than expected. You discover that the TPU is not reaching its full capacity. What should you do?
Answer: B
Explanation:
The best option for training an ML model on a large dataset, using a TPU to accelerate the training process, and discovering that the TPU is not reaching its full capacity, is to increase the batch size. This option allows you to leverage the power and simplicity of TPUs to train your model faster and more efficiently. A TPU is a custom-developed application-specific integrated circuit (ASIC) that can accelerate machine learning workloads. A TPU can provide high performance and scalability for various types of models, such as linear regression, logistic regression, k-means clustering, matrix factorization, and deep neural networks. A TPU can also support various tools and frameworks, such as TensorFlow, PyTorch, and JAX. A batch size is a parameter that specifies the number of training examples in one forward/backward pass. A batch size can affect the speed and accuracy of the training process. A larger batch size can help you utilize the parallel processing power of the TPU, and reduce the communication overhead between the TPU and the host CPU. A larger batch size can also help you avoid overfitting, as it can reduce the variance of the gradient updates. By increasing the batch size, you can train your model on a large dataset faster and mo re efficiently, and make full use of the TPU capacity 1 .
The other options are not as good as option D, for the following reasons:
* Option A: Increasing the learning rate would not help you utilize the parallel processing power of the TPU, and could cause errors or poor performance. A learning rate is a parameter that controls how much the model is updated in each iteration. A learning rate can affect the speed and accuracy of the training process. A larger learning rate can help you converge faster, but it can also cause instability, divergence, or oscillation. By increasing the learning rate, you may not be able t o find the optimal solution, and your model may perform poorly on the validation or test data 2 .
* Option B: Increasing the number of epochs would not help you utilize the parallel processing power of the TPU, and could increase the complexity and cost of the training process. An epoch is a measure of the number of times all of the training examples are used once in the training process. An epoch can affect the speed and accuracy of the training process. A larger number of epochs can help you learn more from the data, but it can also cause overfitting, underfitting, or diminishing returns. By increasing the number of epochs, you may not be able to improve the model performance significantly, and your training process may take longer and consume more resources 3 .
* Option C: Decreasing the learning rate would not help you utilize the parallel processing power of the TPU, and could slow down the training process. A learning rate is a parameter that controls how much the model is updated in each iteration. A learning rate can affect the speed and accuracy of the training process. A smaller learning rate can help you find a more precise solution, but it can also cause slow convergence or local minima. By decreasing the learning rate, you may not be able to reach the optimal solution in a reasonable time, and your training process may take longer 2 .
References:
Prepar ing for Google Cloud Certification: Machine Learning Engineer , Course 2: ML Models and Architectures, Week 1: Introduction to ML Models and Architectures Google Cloud Professional Machine Learning Engineer Exam Guide , Section 2: Architecting ML solutions,
2.1 Designing ML models
Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 4: ML Models and Architectures, Section 4.1: Designing ML Models Use TPUs Triose phosphate utilization and beyond: from photosynthesis t o end ...
Cloud TPU performance guide
Google TPU: Architecture and Performance Best Practices - Run
NEW QUESTION # 105
A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat. The ML Specialist performs some tests and records the following results for a neural network-based image classifier:
Total number of images available = 1,000
Test set images = 100 (constant test set)
The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.
Which techniques can be used by the ML Specialist to improve this specific test error?
Answer: B
NEW QUESTION # 106
You are using Kubeflow Pipelines to develop an end-to-end PyTorch-based MLOps pipeline. The pipeline reads data from BigQuery, processes the data, conducts feature engineering, model training, model evaluation, and deploys the model as a binary file to Cloud Storage. You are writing code for several different versions of the feature engineering and model training steps, and running each new version in Vertex AI Pipelines. Each pipeline run is taking over an hour to complete. You want to speed up the pipeline execution to reduce your development time, and you want to avoid additional costs. What should you do?
Answer: B
Explanation:
1. Caching directly addresses the issue of redundant computations, especially for frequently used feature engineering versions
2. End-to-End" MLOps, Kubeflow Pipelines handle all stages, including feature engineering, maintaining your desired "end-to-end" workflow.
NEW QUESTION # 107
Your healthcare analytics team wants to use production support tickets that contain patient names, phone numbers, and medical record numbers to fine-tune a summarization model.
Company policy prohibits identifiable patient data from entering ML training datasets. You need to enable the project while complying with policy and minimizing manual review. What should you do?
Answer: C
Explanation:
Sensitive Data Protection detects infoTypes such as person names, phone numbers, and medical record numbers and applies transformations like masking or tokenization at scale. Encryption and perimeter controls protect data in place but do not remove identifiers from the dataset, so they do not satisfy a policy that prohibits identifiable data in training corpora.
NEW QUESTION # 108
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