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Google Professional-Machine-Learning-Engineer Exam Overview:

Certification Vendor:	Google Cloud
Exam Name:	Google Cloud Professional Machine Learning Engineer Certification Exam
Exam Number:	Professional-Machine-Learning-Engineer
Exam Price:	$200 USD (plus tax where applicable)
Available Languages:	English, Japanese
Certificate Validity Period:	2 years
Related Certifications:	Google Cloud Professional Data Engineer Google Cloud Professional Cloud Architect
Exam Format:	Multiple choice, Multiple select
Passing Score:	Not officially published, approximately 70%
Exam Duration:	120 minutes
Real Exam Qty:	50-60
Recommended Training:	Official Exam Guide Google Cloud Skills Boost - Professional Machine Learning Engineer Learning Path
Exam Registration:	Google Cloud Certification Registration
Sample Questions:	Google Professional-Machine-Learning-Engineer Sample Questions
Exam Way:	Online-proctored remote exam or onsite-proctored exam at authorized test centers
Pre Condition:	No mandatory prerequisites; recommended 3+ years industry experience including 1+ year designing/managing Google Cloud solutions
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 challenging and rewarding test that requires a deep understanding of machine learning principles and their practical application. Professional-Machine-Learning-Engineer Exam is designed to test the candidate's ability to design, implement, and deploy machine learning models using Google Cloud Platform. It covers a broad range of topics, including data preprocessing, model training, hyperparameter tuning, and model evaluation.

Individuals who pass the Google Professional Machine Learning Engineer Certification Exam will receive a certificate that validates their expertise in the field of machine learning. The certificate is recognized by Google Cloud Platform and is a valuable asset for individuals who are seeking career advancement opportunities in the field of machine learning. Google Professional Machine Learning Engineer certification exam is a challenging but rewarding experience that can help individuals take their career to the next level.

Google Professional Machine Learning Engineer Sample Questions (Q193-Q198):

NEW QUESTION # 193
You are pre-training a large language model on Google Cloud. This model includes custom TensorFlow operations in the training loop Model training will use a large batch size, and you expect training to take several weeks You need to configure a training architecture that minimizes both training time and compute costs What should you do?

Answer: C

NEW QUESTION # 194
A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily.
The model accuracy is acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes.
What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?

A. Do not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training.
B. Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.
C. Switch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals.
D. Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals.

Answer: D

NEW QUESTION # 195
You are building a predictive maintenance model to preemptively detect part defects in bridges. You plan to use high definition images of the bridges as model inputs. You need to explain the output of the model to the relevant stakeholders so they can take appropriate action. How should you build the model?

A. Use TensorFlow to create a deep learning-based model and use the sampled Shapley method to explain the model output.
B. Use scikit-lean to build a tree-based model, and use partial dependence plots (PDP) to explain the model output.
C. Use scikit-learn to build a tree-based model, and use SHAP values to explain the model output.
D. Use TensorFlow to create a deep learning-based model and use Integrated Gradients to explain the model output.

Answer: D

Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to "explain the predictions of a trained model". TensorFlow2 is an open source framework for developing and deploying machine learning and deep learning models. TensorFlow supports various model explainability methods, such as Integrated Gradients3, which is a technique that assigns an importance score to each input feature by approximating the integral of the gradients along the path from a baseline input to the actual input. Integrated Gradients can help explain the output of a deep learning-based model by highlighting the most influential features in the input images. Therefore, option C is the best way to build the model for the given use case. The other options are not relevant or optimal for this scenario. Reference:
Professional ML Engineer Exam Guide
TensorFlow
Integrated Gradients
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

NEW QUESTION # 196
Your organization wants to make its internal shuttle service route more efficient. The shuttles currently stop at all pick-up points across the city every 30 minutes between 7 am and 10 am. The development team has already built an application on Google Kubernetes Engine that requires users to confirm their presence and shuttle station one day in advance. What approach should you take?

A. 1. Build a tree-based regression model that predicts how many passengers will be picked up at each shuttle station.
2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the prediction.
B. 1. Build a tree-based classification model that predicts whether the shuttle should pick up passengers at each shuttle station.
2. Dispatch an available shuttle and provide the map with the required stops based on the prediction
C. 1. Build a reinforcement learning model with tree-based classification models that predict the presence of passengers at shuttle stops as agents and a reward function around a distance-based metric
2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the simulated outcome.
D. 1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints.
2 Dispatch an appropriately sized shuttle and indicate the required stops on the map

Answer: A

NEW QUESTION # 197
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. Use Dataflow to create all possible augmentations, and store them as TFRecords.
B. Embed the augmentation functions dynamically in the tf.Data pipeline.
C. Use Dataflow to create the augmentations dynamically per training run, and stage them as TFRecords.
D. Embed the augmentation functions dynamically as part of Keras generators.

Answer: B

Explanation:
The best option for optimizing the data processing pipeline for run time and compute resources utilization is to embed the augmentation functions dynamically in the tf.Data pipeline. This option has the following advantages:
It allows the data augmentation to be performed on the fly, without creating or storing additional copies of the data. This saves storage space and reduces the data transfer time.
It leverages the parallelism and performance of the tf.Data API, which can efficiently apply the augmentation functions to multiple batches of data in parallel, using multiple CPU cores or GPU devices. The tf.Data API also supports various optimization techniques, such as caching, prefetching, and autotuning, to improve the data processing speed and reduce the latency.
It integrates seamlessly with the TensorFlow and Keras models, which can consume the tf.Data datasets as inputs for training and evaluation. The tf.Data API also supports various data formats, such as images, text, audio, and video, and various data sources, such as files, databases, and web services.
The other options are less optimal for the following reasons:
Option B: Embedding the augmentation functions dynamically as part of Keras generators introduces some limitations and overhead. Keras generators are Python generators that yield batches of data for training or evaluation. However, Keras generators are not compatible with the tf.distribute API, which is used to distribute the training across multiple devices or machines. Moreover, Keras generators are not as efficient or scalable as the tf.Data API, as they run on a single Python thread and do not support parallelism or optimization techniques.
Option C: Using Dataflow to create all possible augmentations, and store them as TFRecords introduces additional complexity and cost. Dataflow is a fully managed service that runs Apache Beam pipelines for data processing and transformation. However, using Dataflow to create all possible augmentations requires generating and storing a large number of augmented images, which can consume a lot of storage space and incur storage and network costs. Moreover, using Dataflow to create the augmentations requires writing and deploying a separate Dataflow pipeline, which can be tedious and time-consuming.
Option D: Using Dataflow to create the augmentations dynamically per training run, and stage them as TFRecords introduces additional complexity and latency. Dataflow is a fully managed service that runs Apache Beam pipelines for data processing and transformation. However, using Dataflow to create the augmentations dynamically per training run requires running a Dataflow pipeline every time the model is trained, which can introduce latency and delay the training process. Moreover, using Dataflow to create the augmentations requires writing and deploying a separate Dataflow pipeline, which can be tedious and time-consuming.
Reference:
[tf.data: Build TensorFlow input pipelines]
[Image augmentation | TensorFlow Core]
[Dataflow documentation]

NEW QUESTION # 198
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