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Google Professional Machine Learning Engineer Certification Exam is an opportunity for individuals to validate their expertise in the field of machine learning. Google Professional Machine Learning Engineer certification exam is designed to test the individual's knowledge of machine learning concepts and their ability to apply these concepts in real-world scenarios. It is a rigorous exam that requires individuals to demonstrate their ability to design, build, and deploy scalable machine learning models using Google Cloud Platform.
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NEW QUESTION # 53
Your company stores a large number of audio files of phone calls made to your customer call center in an on-premises database. Each audio file is in wav format and is approximately 5 minutes long. You need to analyze these audio files for customer sentiment. You plan to use the Speech-to-Text API. You want to use the most efficient approach. What should you do?
Answer: C
Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to "design, build, and productionalize ML models to solve business challenges using Google Cloud technologies". The Speech-to-Text API2 allows you to convert audio to text by applying powerful neural network models. The Natural Language API3 enables you to analyze text and extract information about the sentiment, entities, and syntax. The Cloud Functions4 service lets you write and deploy code that runs in response to events, such as a Pub/Sub message or an HTTP request. Therefore, option B is the most efficient approach to analyze the audio files for customer sentiment, as it leverages the existing Google Cloud services and avoids unnecessary data processing and model training. The other options are not relevant or optimal for this scenario. Reference:
Professional ML Engineer Exam Guide
Speech-to-Text API
Natural Language API
Cloud Functions
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
NEW QUESTION # 54
You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high-throughput online prediction. Which architecture should you use?
Answer: C
Explanation:
https://cloud.google.com/architecture/data-preprocessing-for-ml-with-tf-transform-pt1#where_to_do_preprocessing
NEW QUESTION # 55
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?
Answer: B
Explanation:
The class imbalance problem is a common challenge in machine learning, especially in classification tasks. It occurs when the distribution of the target classes is highly skewed, such that one class (the majority class) has much more examples than the other class (the minority class). The minority class is often the more interesting or important class, such as failure incidents, fraud cases, or rare diseases. However, most machine learning algorithms are designed to optimize the overall accuracy, which can be biased towards the majority class and ignore the minority class. This can result in poor predictive performance, especially for the minority class.
There are different techniques to deal with the class imbalance problem, such as data-level methods, algorithm-level methods, and evaluation-level methods1. Data-level methods involve resampling the original dataset to create a more balanced class distribution. There are two main types of data-level methods:
oversampling and undersampling. Oversampling methods increase the number of examples in the minority class, either by duplicating existing examples or by generating synthetic examples. Undersampling methods reduce the number of examples in the majority class, either by randomly removing examples or by using clustering or other criteria to select representative examples. Both oversampling and undersampling methods can be combined with upweighting or downweighting, which assign different weights to the examples according to their class frequency, to further balance the dataset.
For the use case of investigating failures of a production line component based on sensor readings, the best option is to downsample the data with upweighting to create a sample with 10% positive examples. This option involves randomly removing some of the negative examples (the majority class) until the ratio of positive to negative examples is 1:9, and then assigning higher weights to the positive examples to compensate for their low frequency. This option can create a more balanced dataset that can improve the performance of the classification models, while preserving the diversity and representativeness of the original data. This option can also reduce the computation time and memory usage, as the size of the dataset is reduced. Therefore, downsampling the data with upweighting to create a sample with 10% positive examples is the best option for this use case.
References:
* A Systematic Study of the Class Imbalance Problem in Convolutional Neural Networks
NEW QUESTION # 56
You are an ML engineer at a manufacturing company. You need to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. You want your model to preprocess the images with lower computation to quickly extract features of defects in products. Which approach should you use to build the model?
Answer: A
Explanation:
Convolutional Neural Networks (CNNs) are well-suited to image classification tasks such as identifying defects in products based on images. CNNs use convolutional layers that effectively extract features from images and can be trained to identify patterns in the images. The architecture of CNNs is optimized for image processing tasks and can be more efficient for extracting features from images than other types of neural networks.
Reinforcement learning is a type of machine learning that is used for problems with a delayed reward, such as game playing or robotics. Recommender system is used for recommending products or content to users based on their preferences. Recurrent Neural Networks (RNNs) are used for sequential data such as time series or natural language processing.
NEW QUESTION # 57
A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.
The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:
* Real-time analytics
* Interactive analytics of historical data
* Clickstream analytics
* Product recommendations
Which services should the Specialist use?
Answer: D
Explanation:
Explanation
NEW QUESTION # 58
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