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To be eligible for the exam, candidates must have experience in developing and deploying machine learning models using Google Cloud Platform. They should also have experience with programming languages such as Python and SQL, and knowledge of machine learning concepts such as supervised and unsupervised learning, reinforcement learning, and deep learning.
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Earning the Google Professional Machine Learning Engineer Certification demonstrates to employers and clients that you have the skills and knowledge needed to design and implement effective machine learning solutions on the Google Cloud Platform. It is a valuable credential for data scientists, software engineers, and other professionals who are interested in developing their skills in machine learning and cloud computing.
Google Professional Machine Learning Engineer exam is a certification offered by Google Cloud that is designed to validate the skills and expertise of individuals in the field of machine learning. Google Professional Machine Learning Engineer certification is intended for professionals who have experience in developing and deploying machine learning models using Google Cloud technologies.
NEW QUESTION # 17
You are going to train a DNN regression model with Keras APIs using this code:
How many trainable weights does your model have? (The arithmetic below is correct.)
Answer: B
Explanation:
The number of trainable weights in a DNN regression model with Keras APIs can be calculated by multiplying the number of input units by the number of output units for each layer, and adding the number of bias units for each layer. The bias units are usually equal to the number of output units,except for the last layer, which does not have bias units if the activation function is softmax1. In this code, the model has three layers: a dense layer with 256 units and relu activation, a dropout layer with 0.25 rate, and a dense layer with 2 units and softmax activation. The input shape is 500. Therefore, the number of trainable weights is:
* For the first layer: 500 input units * 256 output units + 256 bias units = 128256
* For the second layer: The dropout layer does not have any trainable weights, as it only randomly sets some of the input units to zero to prevent overfitting2.
* For the third layer: 256 input units * 2 output units + 0 bias units = 512 The total number of trainable weights is 128256 + 512 = 161024. Therefore, the correct answer is B.
References:
* How to calculate the number of parameters for a Convolutional Neural Network?
* Dropout (keras.io)
NEW QUESTION # 18
You are building a linear model with over 100 input features, all with values between -1 and 1. You suspect that many features are non-informative. You want to remove the non-informative features from your model while keeping the informative ones in their original form. Which technique should you use?
Answer: A
Explanation:
L1 regularization, also known as Lasso regularization, adds the sum of the absolute values of the model's coefficients to the loss function1. It encourages sparsity in the model by shrinking some coefficients to precisely zero2. This way, L1 regularization can perform feature selection and remove the non-informative features from the model while keeping the informative ones in their original form. Therefore, using L1 regularization is the best technique for this use case.
Reference:
Regularization in Machine Learning - GeeksforGeeks
Regularization in Machine Learning (with Code Examples) - Dataquest
L1 And L2 Regularization Explained & Practical How To Examples
L1 and L2 as Regularization for a Linear Model
NEW QUESTION # 19
A company wants to predict the sale prices of houses based on available historical sales data. The target variable in the company's dataset is the sale price. The features include parameters such as the lot size, living area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built, and postal code. The company wants to use multi-variable linear regression to predict house sale prices.
Which step should a machine learning specialist take to remove features that are irrelevant for the analysis and reduce the model's complexity?
Answer: D
NEW QUESTION # 20
You work for a company that provides an anti-spam service that flags and hides spam posts on social media platforms. Your company currently uses a list of 200,000 keywords to identify suspected spam posts. If a post contains more than a few of these keywords, the post is identified as spam. You want to start using machine learning to flag spam posts for human review. What is the main advantage of implementing machine learning for this business case?
Answer: C
Explanation:
The main advantage of implementing machine learning for this business case is that new problematic phrases can be identified in spam posts. This is because machine learning can learn from the data and the feedback, and adapt to the changing patterns and trends of spam posts. Machine learning can also capture the semantic and contextual meaning of the posts, and not just rely on the presence or absence of keywords. By using machine learning, you can improve the accuracy and coverage of your anti-spam service, and detect new and emerging types of spam posts that may not be captured by the keyword list.
The other options are not advantages of implementing machine learning for this business case for the following reasons:
* A. Posts can be compared to the keyword list much more quickly is not an advantage, as it does not improve the quality or effectiveness of the anti-spam service. It only improves the efficiency of the service, which is not the primary objective. Moreover, machine learning may not necessarily be faster than the keyword list, depending on the complexity and size of the model and the data.
* C. A much longer keyword list can be used to flag spam posts is not an advantage, as it does not address the limitations or challenges of the keyword list approach. It only increases the size and complexity of the keyword list, which can make it harder to maintain and update. Moreover, a longer keyword list may not improve the accuracy or coverage of the anti-spam service, as it may introduce more false positives or false negatives, or miss new and emerging types of spam posts.
* D. Spam posts can be flagged using far fewer keywords is not an advantage, as it does not reflect the capabilities or benefits of machine learning. It only reduces the size and complexity of the keyword list, which can make it easier to maintain and update. However, using fewer keywords may not improve the accuracy or coverage of the anti-spam service, as it may lose some information or meaning of the posts, or miss some types of spam posts.
:
Professional ML Engineer Exam Guide
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate Google Cloud l aunches machine learning engineer certification Machine Learning for Spam Detection Spam Detection Using Machine Learning
NEW QUESTION # 21
You have built a custom model that performs several memory-intensive preprocessing tasks before it makes a prediction. You deployed the model to a Vertex Al endpoint. and validated that results were received in a reasonable amount of time After routing user traffic to the endpoint, you discover that the endpoint does not autoscale as expected when receiving multiple requests What should you do?
Answer: B
Explanation:
According to the web search results, Vertex AI is a unified platform for machine learning development and deployment. Vertex AI offers various services and tools for building, m anaging, and serving machine learning models 1 . Vertex AI allows you to deploy your models to endpoints for online prediction, and configure the compute resources and autoscaling options for your deployed models 2 . Autoscaling with Vertex AI endpoints is (by default) based on the CPU utilization across all cores of the machine type you have specified. The default threshold of 60% represents 60% on all cores. For example, for a 4 core machine, that means you need 240% utilization to trigger autoscaling 3 . Therefore, if you discover that the endpoint does not autoscale as expected when receiving multiple requests, you might need to decrease the CPU utilization target in the autoscaling configurations. This way, you can lower the threshold for triggering autoscaling and allocate more resources to handle the prediction requests. Therefore, option D is the best way to solve the problem for the given use case. The other options are not relevant or optimal for this scenario. References :
* Vertex AI
* Deploy a model to an endpoint
* Vertex AI endpoint doesn't scale up / down
* Google Professional Machine Learning Certification Exam 2023
* Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
NEW QUESTION # 22
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