ExamGecko
Search Search Login
Home Home / Google / Professional Machine Learning Engineer
Question list
Search
Search

List of questions

Search
Open VPLUS File
Convert VPLUS to PDF

Related questions

You are implementing a batch inference ML pipeline in Google Cloud. The model was developed by using TensorFlow and is stored in SavedModel format in Cloud Storage. You need to apply the model to a historical dataset that is stored in a BigQuery table. You want to perform inference with minimal effort. What should you do?
You work for a social media company. You want to create a no-code image classification model for an iOS mobile application to identify fashion accessories You have a labeled dataset in Cloud Storage You need to configure a training workflow that minimizes cost and serves predictions with the lowest possible latency What should you do?
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 models 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?
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?
You have recently developed a new ML model in a Jupyter notebook. You want to establish a reliable and repeatable model training process that tracks the versions and lineage of your model artifacts. You plan to retrain your model weekly. How should you operationalize your training process?
You are an AI architect at a popular photo-sharing social media platform. Your organization's content moderation team currently scans images uploaded by users and removes explicit images manually. You want to implement an AI service to automatically prevent users from uploading explicit images. What should you do?
You work for an online retail company that is creating a visual search engine. You have set up an end-to-end ML pipeline on Google Cloud to classify whether an image contains your company's product. Expecting the release of new products in the near future, you configured a retraining functionality in the pipeline so that new data can be fed into your ML models. You also want to use Al Platform's continuous evaluation service to ensure that the models have high accuracy on your test data set. What should you do?
You are an AI engineer working for a popular video streaming platform. You built a classification model using PyTorch to predict customer churn. Each week, the customer retention team plans to contact customers identified as at-risk for churning with personalized offers. You want to deploy the model while minimizing maintenance effort. What should you do?
You are developing a model to help your company create more targeted online advertising campaigns. You need to create a dataset that you will use to train the model. You want to avoid creating or reinforcing unfair bias in the model. What should you do? Choose 2 answers
You have recently used TensorFlow to train a classification model on tabular data You have created a Dataflow pipeline that can transform several terabytes of data into training or prediction datasets consisting of TFRecords. You now need to productionize the model, and you want the predictions to be automatically uploaded to a BigQuery table on a weekly schedule. What should you do?

Question 265 - Professional Machine Learning Engineer discussion

Report
Export

You have trained an XGBoost model that you plan to deploy on Vertex Al for online prediction. You are now uploading your model to Vertex Al Model Registry, and you need to configure the explanation method that will serve online prediction requests to be returned with minimal latency. You also want to be alerted when feature attributions of the model meaningfully change over time. What should you do?

A.
1 Specify sampled Shapley as the explanation method with a path count of 5. 2 Deploy the model to Vertex Al Endpoints. 3. Create a Model Monitoring job that uses prediction drift as the monitoring objective.
Answers
A.
1 Specify sampled Shapley as the explanation method with a path count of 5. 2 Deploy the model to Vertex Al Endpoints. 3. Create a Model Monitoring job that uses prediction drift as the monitoring objective.
B.
1 Specify Integrated Gradients as the explanation method with a path count of 5. 2 Deploy the model to Vertex Al Endpoints. 3. Create a Model Monitoring job that uses prediction drift as the monitoring objective.
Answers
B.
1 Specify Integrated Gradients as the explanation method with a path count of 5. 2 Deploy the model to Vertex Al Endpoints. 3. Create a Model Monitoring job that uses prediction drift as the monitoring objective.
C.
1. Specify sampled Shapley as the explanation method with a path count of 50. 2. Deploy the model to Vertex Al Endpoints. 3. Create a Model Monitoring job that uses training-serving skew as the monitoring objective.
Answers
C.
1. Specify sampled Shapley as the explanation method with a path count of 50. 2. Deploy the model to Vertex Al Endpoints. 3. Create a Model Monitoring job that uses training-serving skew as the monitoring objective.
D.
1 Specify Integrated Gradients as the explanation method with a path count of 50. 2. Deploy the model to Vertex Al Endpoints. 3 Create a Model Monitoring job that uses training-serving skew as the monitoring objective.
Answers
D.
1 Specify Integrated Gradients as the explanation method with a path count of 50. 2. Deploy the model to Vertex Al Endpoints. 3 Create a Model Monitoring job that uses training-serving skew as the monitoring objective.
Suggested answer: A

Explanation:

Sampled Shapley is a fast and scalable approximation of the Shapley value, which is a game-theoretic concept that measures the contribution of each feature to the model prediction. Sampled Shapley is suitable for online prediction requests, as it can return feature attributions with minimal latency. The path count parameter controls the number of samples used to estimate the Shapley value, and a lower value means faster computation. Integrated Gradients is another explanation method that computes the average gradient along the path from a baseline input to the actual input. Integrated Gradients is more accurate than Sampled Shapley, but also more computationally intensive. Therefore, it is not recommended for online prediction requests, especially with a high path count. Prediction drift is the change in the distribution of feature values or labels over time. It can affect the performance and accuracy of the model, and may require retraining or redeploying the model. Vertex AI Model Monitoring allows you to monitor prediction drift on your deployed models and endpoints, and set up alerts and notifications when the drift exceeds a certain threshold. You can specify an email address to receive the notifications, and use the information to retrigger the training pipeline and deploy an updated version of your model. This is the most direct and convenient way to achieve your goal. Training-serving skew is the difference between the data used for training the model and the data used for serving the model. It can also affect the performance and accuracy of the model, and may indicate data quality issues or model staleness. Vertex AI Model Monitoring allows you to monitor training-serving skew on your deployed models and endpoints, and set up alerts and notifications when the skew exceeds a certain threshold. However, this is not relevant to the question, as the question is about the feature attributions of the model, not the data distribution.Reference:

Vertex AI: Explanation methods

Vertex AI: Configuring explanations

Vertex AI: Monitoring prediction drift

Vertex AI: Monitoring training-serving skew

Show Answer
asked 18/09/2024
Kevin Lizano
37 questions
PreviousPreviousNextNext
User
Your answer:
0 comments
Sorted by

Leave a comment first

ExamGecko

Copyright @2023 ExamGecko | All right reserved

Mail us: [email protected]
About us
Contact us
Twitter X
Facebook
Medium
Convert VPLUS to PDF
Open VPLUS files Easily and Quickly
Privacy Policy
Disclaimer
Terms