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Google Professional Machine Learning Engineer Certification Exam is a comprehensive assessment of a candidate's knowledge and skills in machine learning. It is a rigorous exam that requires a candidate to demonstrate their ability to design and implement machine learning solutions in real-world scenarios. Professional-Machine-Learning-Engineer exam covers a wide range of topics, from data preparation and feature engineering to model selection and deployment. Candidates who pass the exam are recognized as experts in the field of machine learning and are highly sought after by employers in the tech industry.

Google Professional Machine Learning Engineer Certification Exam is a test designed to validate advanced skills in machine learning. Professional-Machine-Learning-Engineer Exam is intended for professionals who want to demonstrate their ability to design and develop machine learning models on the Google Cloud platform. Google Professional Machine Learning Engineer certification exam is a great way for professionals to showcase their expertise in developing and deploying machine learning models.

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The Google Professional Machine Learning Engineer certification exam consists of multiple-choice questions that assess the candidate's knowledge of machine learning concepts, data preprocessing, model selection, hyperparameter tuning, model evaluation, and deployment. Professional-Machine-Learning-Engineer Exam is conducted online and is proctored by a third-party vendor. Candidates are required to pass the exam within two hours and thirty minutes and must score at least 80% to pass.

Google Professional Machine Learning Engineer Sample Questions (Q318-Q323):

NEW QUESTION # 318
You are designing an architecture with a serverless ML system to enrich customer support tickets with informative metadata before they are routed to a support agent. You need a set of models to predict ticket priority, predict ticket resolution time, and perform sentiment analysis to help agents make strategic decisions when they process support requests. Tickets are not expected to have any domain-specific terms or jargon.
The proposed architecture has the following flow:

Which endpoints should the Enrichment Cloud Functions call?

A. 1 = AI Platform, 2 = AI Platform, 3 = Cloud Natural Language API
B. 1 = AI Platform, 2 = AI Platform, 3 = AutoML Natural Language
C. 1 = AI Platform, 2 = AI Platform, 3 = AutoML Vision
D. 1 = Cloud Natural Language API, 2 = AI Platform, 3 = Cloud Vision API

Answer: A

Explanation:
The architecture has the following flow:
A user writes a ticket to Firebase, which triggers a Cloud Function.
- The Cloud Function calls 3 different endpoints to enrich the ticket:
- An AI Platform endpoint, where the function can predict the priority.
- An AI Platform endpoint, where the function can predict the resolution time.
- The Natural Language API to do sentiment analysis and word salience.
- For each reply, the Cloud Function updates the Firebase real-time database.
- The Cloud Function then creates a ticket into the helpdesk platform using the RESTful API.
https://cloud.google.com/architecture/architecture-of-a-serverless-ml-model#architecture

NEW QUESTION # 319
You work for an auto insurance company. You are preparing a proof-of-concept ML application that uses images of damaged vehicles to infer damaged parts Your team has assembled a set of annotated images from damage claim documents in the company's database The annotations associated with each image consist of a bounding box for each identified damaged part and the part name. You have been given a sufficient budget to tram models on Google Cloud You need to quickly create an initial model What should you do?

A. Download a pre-trained object detection mode! from TensorFlow Hub Fine-tune the model in Vertex Al Workbench by using the annotated image data.
B. Train an object detection model in AutoML by using the annotated image data.
C. Train an object detection model in Vertex Al custom training by using the annotated image data.
D. Create a pipeline in Vertex Al Pipelines and configure the AutoMLTrainingJobRunOp compon it to train a custom object detection model by using the annotated image data.

Answer: B

NEW QUESTION # 320
You work for a retail company. You have been asked to develop a model to predict whether a customer will purchase a product on a given day. Your team has processed the company's sales data, and created a table with the following rows:
* Customer_id
* Product_id
* Date
* Days_since_last_purchase (measured in days)
* Average_purchase_frequency (measured in 1/days)
* Purchase (binary class, if customer purchased product on the Date)
You need to interpret your models results for each individual prediction. What should you do?

A. Create a Vertex Al tabular dataset Train an AutoML model to predict customer purchases Deploy the model to a Vertex Al endpoint. At each prediction enable L1 regularization to detect non-informative features.
B. Create a BigQuery table Use BigQuery ML to build a boosted tree classifier Inspect the partition rules of the trees to understand how each prediction flows through the trees.
C. Create a Vertex Al tabular dataset Train an AutoML model to predict customer purchases Deploy the model to a Vertex Al endpoint and enable feature attributions Use the "explain" method to get feature attribution values for each individual prediction.
D. Create a BigQuery table Use BigQuery ML to build a logistic regression classification model Use the values of the coefficients of the model to interpret the feature importance with higher values corresponding to more importance.

Answer: C

Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to "explain the predictions of a trained model". Vertex AI provides feature attributions using Shapley Values, a cooperative game theory algorithm that assigns credit to each feature in a model for a particular outcome2. Feature attributions can help you understand how the model calculates the predictions and debug or optimize the model accordingly. You can use AutoML for Tabular Data to generate and query local feature attributions3. The other options are not relevant or optimal for this scenario. Reference:
Professional ML Engineer Exam Guide
Feature attributions for classification and regression
AutoML for Tabular Data
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

NEW QUESTION # 321
You are investigating the root cause of a misclassification error made by one of your models. You used Vertex AI Pipelines to train and deploy the model. The pipeline reads data from BigQuery.
creates a copy of the data in Cloud Storage in TFRecord format, trains the model in Vertex AI Training on that copy, and deploys the model to a Vertex AI endpoint. You have identified the specific version of that model that misclassified, and you need to recover the data this model was trained on. How should you find that copy of the data?

A. Find the job ID in Vertex AI Training corresponding to the training for the model. Search in the logs of that job for the data used for the training.
B. Use Vertex AI Feature Store. Modify the pipeline to use the feature store, and ensure that all training data is stored in it. Search the feature store for the data used for the training.
C. Use the logging features in the Vertex AI endpoint to determine the timestamp of the model's deployment. Find the pipeline run at that timestamp. Identify the step that creates the data copy, and search in the logs for its location.
D. Use the lineage feature of Vertex AI Metadata to find the model artifact. Determine the version of the model and identify the step that creates the data copy and search in the metadata for its location.

Answer: D

NEW QUESTION # 322
You recently built the first version of an image segmentation model for a self-driving car. After deploying the model, you observe a decrease in the area under the curve (AUC) metric. When analyzing the video recordings, you also discover that the model fails in highly congested traffic but works as expected when there is less traffic. What is the most likely reason for this result?

A. The model is overfitting in areas with less traffic and underfitting in areas with more traffic.
B. Too much data representing congested areas was used for model training.
C. Gradients become small and vanish while backpropagating from the output to input nodes.
D. AUC is not the correct metric to evaluate this classification model.

Answer: A

Explanation:
The most likely reason for the observed result is that the model is overfitting in areas with less traffic and underfitting in areas with more traffic. Overfitting means that the model learns the specific patterns and noise in the training data, but fails to generalize well to new and unseen data. Underfitting means that the model is not able to capture the complexity and variability of the data, and performs poorly on both training and test data. In this case, the model might have learned to segment the images well when there is less traffic, but it might not have enough data or features to handle the more challenging scenarios when there is more traffic.
This could lead to a decrease in the AUC metric, which measures the ability of the model to distinguish between different classes. AUC is a suitable metric for this classification model, as it is not affected by class imbalance or threshold selection. The other options are not likely to be the reason for the result, as they are not related to the traffic density. Too much data representing congested areas would not cause the model to fail in those areas, but rather help the model learn better. Gradients vanishing or exploding is a problem that occurs during the training process, not after the deployment, and it affects the whole model, not specific scenarios. References:
* Image Segmentation: U-Net For Self Driving Cars
* Intelligent Semantic Segmentation for Self-Driving Vehicles Using Deep Learning
* Sharing Pixelopolis, a self-driving car demo from Google I/O built with TensorFlow Lite
* Google Cloud launches machine learning engineer certification
* Google Professional Machine Learning Engineer Certification
* Professional ML Engineer Exam Guide
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

NEW QUESTION # 323
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