Free Google Professional-Machine-Learning-Engineer Exam Questions

Question 1

You have developed a BigQuery ML model that predicts customer churn and deployed the model to Vertex Al Endpoints. You want to automate the retraining of your model by using minimal additional code when model feature values change. You also want to minimize the number of times that your model is retrained to reduce training costs. What should you do?

A : 1. Enable request-response logging on Vertex Al Endpoints.2 Schedule a TensorFlow Data Validation job to monitor prediction drift3. Execute model retraining if there is significant distance between the distributions

B : 1. Enable request-response logging on Vertex Al Endpoints2. Schedule a TensorFlow Data Validation job to monitor training/serving skew3. Execute model retraining if there is significant distance between the distributions

C : 1 Create a Vertex Al Model Monitoring job configured to monitor prediction drift.2. Configure alert monitoring to publish a message to a Pub/Sub queue when a monitonng alert isdetected.3. Use a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery

D : 1. Create a Vertex Al Model Monitoring job configured to monitor training/serving skew2. Configure alert monitoring to publish a message to a Pub/Sub queue when a monitoring alert isdetected3. Use a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery.

Answer: C

Question 2

You are an ML engineer at a global car manufacture. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?

A : ee individual features: binned latitude, binned longitude, and one-hot encoded car type.

B : feature obtained as an element-wise product between latitude, longitude, and car type.

C : feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type.

D : o feature crosses as an element-wise product: the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type.

Answer: C

Question 3

You work on a team that builds state-of-the-art deep learning models by using the TensorFlow framework. Your team runs multiple ML experiments each week which makes it difficult to track the experiment runs. You want a simple approach to effectively track, visualize and debug ML experiment runs on Google Cloud while minimizing any overhead code. How should you proceed?

A : Set up Vertex Al Experiments to track metrics and parameters Configure Vertex Al TensorBoard for visualization

B : Set up a Cloud Function to write and save metrics files to a Cloud Storage Bucket Configure a Google Cloud VM to host TensorBoard locally for visualization.

C : Set up a Vertex Al Workbench notebook instance Use the instance to save metrics data in a Cloud Storage bucket and to host TensorBoard locally for visualization.

D : Set up a Cloud Function to write and save metrics files to a BigQuery table. Configure a Google Cloud VM to host TensorBoard locally for visualization

Answer: A

Question 4

Your team has been tasked with creating an ML solution in Google Cloud to classify support requests for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the classifier so that you have full control of the model's code, serving, and deployment. You will use Kubeflow pipelines for the ML platform. To save time, you want to build on existing resources and use managed services instead of building a completely new model. How should you build the classifier?

A : the Natural Language API to classify support requests.

B : AutoML Natural Language to build the support requests classifier.

C : an established text classification model on AI Platform to perform transfer learning

D : an established text classification model on AI Platform as-is to classify support requests

Answer: D

Question 5

You are working with a dataset that contains customer transactions. You need to build an ML model to predict customer purchase behavior. You plan to develop the model in BigQuery ML, and export it to Cloud Storage for online prediction. You notice that the input data contains a few categorical features, including product category and payment method. You want to deploy the model as quickly as possible. What should you do?

A : Use the TRANSFORM clause with the ML.ONE_HOT_ENCODER function on the categorical features at model creation and select the categorical and non-categorical features.

B : Use the ML.ONE_HOT_ENCODER function on the categorical features and select the encoded categorical features and non-categorical features as inputs to create your model.

C : Use the CREATE MODEL statement and select the categorical and non-categorical features.

D : Use the ML.MULTI_HOT_ENCODER function on the categorical features, and select the encoded categorical features and non-categorical features as inputs to create your model.

Answer: D