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The Google Professional Machine Learning Engineer certification is developed to validate the ability of the specialists to design, build, and productionize the Machine Learning models to solve business challenges with the help of Google Cloud technologies as well as their knowledge of the proven Machine Learning models & techniques. Specifically, this certificate equips the candidates with an understanding of all the aspects related to data pipeline interaction, model architecture, as well as metrics interpretation. It also provides the target individuals with the comprehension of the basic concepts of application development, data engineering, infrastructure management, and data governance. To get certified, the individuals need to take one qualifying exam.
Google Professional Machine Learning Engineer certification exam is intended for professionals who have experience in the field of machine learning, including data scientists, machine learning engineers, and software developers. Google Professional Machine Learning Engineer certification exam is designed to test the candidate's knowledge of advanced machine learning concepts, including deep learning, natural language processing, and computer vision.
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Achieving the Google Professional Machine Learning Engineer certification demonstrates a candidate's ability to design and implement machine learning models using Google Cloud technologies, and can lead to career advancement opportunities and increased job prospects. It is a highly regarded certification in the field of machine learning and is recognized by industry professionals worldwide.
NEW QUESTION # 266
You work for a company that captures live video footage of checkout areas in their retail stores You need to use the live video footage to build a mode! to detect the number of customers waiting for service in near real time You want to implement a solution quickly and with minimal effort How should you build the model?
Answer: D
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 Vertex AI Vision Occupancy Analytics model2 is a specialized pre-built vision model that lets you count people or vehicles given specific inputs you add in video frames. It provides advanced features such as active zones counting, line crossing counting, and dwelling detection. This model is suitable for the use case of detecting the number of customers waiting for service in near real time. You can easily create and deploy an occupancy analytics application using Vertex AI Vision3. The other options are not relevant or optimal for this scenario. Reference:
Professional ML Engineer Exam Guide
Occupancy analytics guide
Create an occupancy analytics app with BigQuery forecasting
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
NEW QUESTION # 267
Your team trained and tested a DNN regression model with good results. Six months after deployment, the model is performing poorly due to a change in the distribution of the input data. How should you address the input differences in production?
Answer: C
Explanation:
The performance of a DNN regression model can degrade over time due to a change in the distribution of the input data. This phenomenon is known as data drift or concept drift, and it can affect the accuracy and reliability of the model predictions. Data drift can be caused by various factors, such as seasonal changes, population shifts, market trends, or external events 1 To address the input differences in production, one should create alerts to monitor for skew, and retrain the model. Skew is a measure of how much the input data in production differs from the input data used for training the model. Skew can be detected by comparing the statistics and distributions of the input features in the training and production data, such as mean, standard deviation, histogram, or quantiles. Alerts can be set up to notify the model developers or operators when the skew exceeds a certain threshold, indicating a significant change in th e input data 2 When an alert is triggered, the model should be retrained with the latest data that reflects the current distribution of the input features. Retraining the model can help the model adapt to the new data and improve its performance. Retraining the model can be done manually or automatically, depending on the frequency and severity of the data drift. Retraining the model can also involve updating the model architecture, hyperparameters, or optimization algorithm, if necessary 3 The other options are not as effective or feasible. Performing feature selection on the model and retraining the model with fewer features is not a good idea, as it may reduce the expressiveness and complexity of the model, and ignore some important features that may affect the output. Retraining the model and selecting an L2 regularization parameter with a hyperparameter tuning service is not relevant, as L2 regularization is a technique to prevent overfitting, not data drift. Retraining the model on a monthly basis with fewer features is not optimal, as it may not capture the timely changes in the input data, and may compromise the model performance.
References: 1 : Data drift detection for machine learning models 2 : Skew and drift detection 3
: Retraining machine learni ng models
NEW QUESTION # 268
You have created multiple versions of an ML model and have imported them to Vertex AI Model Registry.
You want to perform A/B testing to identify the best-performing model using the simplest approach. What should you do?
Answer: B
Explanation:
Vertex AI Model Registry supports traffic splitting and built-in monitoring, making A/B testing seamless.
This approach eliminates the need for additional monitoring tools and infrastructure overhead. Cloud Run and GKE solutions (Options A and C) add unnecessary complexity, while Looker Studio (Option B) requires additional configuration for monitoring.
NEW QUESTION # 269
You are an ML engineer at a large grocery retailer with stores in multiple regions. You have been asked to create an inventory prediction model. Your model's features include region, location, historical demand, and seasonal popularity. You want the algorithm to learn from new inventory data on a daily basis. Which algorithms should you use to build the model?
Answer: C
Explanation:
Recurrent Neural Networks (RNN) are a type of neural network that are used to process sequential data. They are well-suited for this task because the inventory data is sequential in nature. The RNN can learn from the historical demand data and the seasonal popularity data to predict the future inventory levels.
NEW QUESTION # 270
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 # 271
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