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 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?
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?
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 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 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 are using Kubeflow Pipelines to develop an end-to-end PyTorch-based MLOps pipeline. The pipeline reads data from BigQuery, processes the data, conducts feature engineering, model training, model evaluation, and deploys the model as a binary file to Cloud Storage. You are writing code for several different versions of the feature engineering and model training steps, and running each new version in Vertex Al Pipelines. Each pipeline run is taking over an hour to complete. You want to speed up the pipeline execution to reduce your development time, and you want to avoid additional costs. 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 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

Question 210 - Professional Machine Learning Engineer discussion

Report
Export

You have created a Vertex Al pipeline that automates custom model training You want to add a pipeline component that enables your team to most easily collaborate when running different executions and comparing metrics both visually and programmatically. What should you do?

A.
Add a component to the Vertex Al pipeline that logs metrics to a BigQuery table Query the table to compare different executions of the pipeline Connect BigQuery to Looker Studio to visualize metrics.
Answers
A.
Add a component to the Vertex Al pipeline that logs metrics to a BigQuery table Query the table to compare different executions of the pipeline Connect BigQuery to Looker Studio to visualize metrics.
B.
Add a component to the Vertex Al pipeline that logs metrics to a BigQuery table Load the table into a pandas DataFrame to compare different executions of the pipeline Use Matplotlib to visualize metrics.
Answers
B.
Add a component to the Vertex Al pipeline that logs metrics to a BigQuery table Load the table into a pandas DataFrame to compare different executions of the pipeline Use Matplotlib to visualize metrics.
C.
Add a component to the Vertex Al pipeline that logs metrics to Vertex ML Metadata Use Vertex Al Experiments to compare different executions of the pipeline Use Vertex Al TensorBoard to visualize metrics.
Answers
C.
Add a component to the Vertex Al pipeline that logs metrics to Vertex ML Metadata Use Vertex Al Experiments to compare different executions of the pipeline Use Vertex Al TensorBoard to visualize metrics.
D.
Add a component to the Vertex Al pipeline that logs metrics to Vertex ML Metadata Load the Vertex ML Metadata into a pandas DataFrame to compare different executions of the pipeline. Use Matplotlib to visualize metrics.
Answers
D.
Add a component to the Vertex Al pipeline that logs metrics to Vertex ML Metadata Load the Vertex ML Metadata into a pandas DataFrame to compare different executions of the pipeline. Use Matplotlib to visualize metrics.
Suggested answer: C

Explanation:

Vertex AI Experiments is a managed service that allows you to track, compare, and manage experiments with Vertex AI. You can use Vertex AI Experiments to record the parameters, metrics, and artifacts of each pipeline run, and compare them in a graphical interface. Vertex AI TensorBoard is a tool that lets you visualize the metrics of your models, such as accuracy, loss, and learning curves. By logging metrics to Vertex ML Metadata and using Vertex AI Experiments and TensorBoard, you can easily collaborate with your team and find the best model configuration for your problem.Reference:Vertex AI Pipelines: Metrics visualization and run comparison using the KFP SDK,Track, compare, manage experiments with Vertex AI Experiments,Vertex AI Pipelines

Show Answer
asked 18/09/2024
luis lozano
35 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