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 165 - Professional Machine Learning Engineer discussion

Report
Export

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 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.
Answers
A.
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.
B.
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.
Answers
B.
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.
C.
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.
Answers
C.
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.
D.
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.
Answers
D.
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.
Suggested answer: B

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

Show Answer
asked 18/09/2024
Yuwadee Srisathan
40 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