Free Google Professional-Machine-Learning-Engineer Exam Questions

Question 1

You work for a retail company that is using a regression model built with BigQuery ML to predict product sales. This model is being used to serve online predictions Recently you developed a new version of the model that uses a different architecture (custom model) Initial analysis revealed that both models are performing as expected You want to deploy the new version of the model to production and monitor the performance over the next two months You need to minimize the impact to the existing and future model users How should you deploy the model?

A : Import the new model to the same Vertex Al Model Registry as a different version of the existing model. Deploy the new model to the same Vertex Al endpoint as the existing model, and use traffic splitting to route 95% of production traffic to the BigQuery ML model and 5% of production traffic to the new model.

B : Import the new model to the same Vertex Al Model Registry as the existing model Deploy the models to one Vertex Al endpoint Route 95% of production traffic to the BigQuery ML model and 5% of production traffic to the new model

C : Import the new model to the same Vertex Al Model Registry as the existing model Deploy each model to a separate Vertex Al endpoint.

D : Deploy the new model to a separate Vertex Al endpoint Create a Cloud Run service that routes the prediction requests to the corresponding endpoints based on the input feature values.

Answer: A

Question 2

You want to migrate a scikrt-learn classifier model to TensorFlow. You plan to train the TensorFlow classifier model using the same training set that was used to train the scikit-learn model and then compare the performances using a common test set. You want to use the Vertex Al Python SDK to manually log the evaluation metrics of each model and compare them based on their F1 scores and confusion matrices. How should you log the metrics?

A :

B :

C :

D :

Answer: A

Question 3

Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers' account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?

A : 1. Create a Pub/Sub topic for each user. 2. Deploy a Cloud Function that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.

B : 1. Create a Pub/Sub topic for each user. 2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.

C : 1. Build a notification system on Firebase. 2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold.

D : 1. Build a notification system on Firebase. 2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.

Answer: A

Question 4

You have been given a dataset with sales predictions based on your company’s marketing activities. The data is structured and stored in BigQuery, and has been carefully managed by a team of data analysts. You need to prepare a report providing insights into the predictive capabilities of the data. You were asked to run several ML models with different levels of sophistication, including simple models and multilayered neural networks. You only have a few hours to gather the results of your experiments. Which Google Cloud tools should you use to complete this task in the most efficient and self-serviced way?

A : BigQuery ML to run several regression models, and analyze their performance.

B : ad the data from BigQuery using Dataproc, and run several models using SparkML

C : Vertex AI Workbench user-managed notebooks with scikit-learn code for a variety of ML algorithms and performance metrics.

D : ain a custom TensorFlow model with Vertex AI, reading the data from BigQuery featuring a variety of ML algorithms.

Answer: A

Question 5

You work for a semiconductor manufacturing company. You need to create a real-time application that automates the quality control process High-definition images of each semiconductor are taken at the end of the assembly line in real time. The photos are uploaded to a Cloud Storage bucket along with tabular data that includes each semiconductor's batch number serial number dimensions, and weight You need to configure model training and serving while maximizing model accuracy. What should you do?

A : Use Vertex Al Data Labeling Service to label the images and train an AutoML image classification model. Deploy the model and configure Pub/Sub to publish a message when an image is categorized into the failing class.

B : Use Vertex Al Data Labeling Service to label the images and train an AutoML image classification model. Schedule a daily batch prediction job that publishes a Pub/Sub message when the job completes.

C : Convert the images into an embedding representation Import this data into BigQuery, and train a BigQuery. ML K-means clustenng model with two clusters Deploy the model and configure Pub/Sub to publish a message when a semiconductor's data is categorized into the failing cluster.

D : Import the tabular data into BigQuery use Vertex Al Data Labeling Service to label the data and train an AutoML tabular classification model Deploy the model and configure Pub/Sub to publish a message when a semiconductor's data is categorized into the failing class.

Answer: D