Free Google Professional Machine Learning Engineer Practice Questions

A.  

Include a comprehensive set of demographic features.

B.  

include only the demographic groups that most frequently interact with advertisements.

C.  

Collect a random sample of production traffic to build the training dataset.

D.  

Collect a stratified sample of production traffic to build the training dataset.

E.  

Conduct fairness tests across sensitive categories and demographics on the trained model.

A.  

Create a workflow using Cloud Function Triggers Configure a Cloud Function that is triggered when documents are uploaded to an input Cloud Storage bucket Configure another Cloud Function that translates the documents using the Cloud Translation API and saves the translations to an output Cloud Storage bucket Use human reviewers to evaluate the incorrect translations.

B.  

Create a Vertex Al pipeline that processes the documents1 launches an AutoML Translation training job evaluates the translations, and deploys the model to a Vertex Al endpoint with autoscaling and model monitoring When there is a predetermined skew between training and live data re-trigger the pipeline with the latest data.

C.  

Use AutoML Translation to tram a model Configure a Translation Hub project and use the trained model to translate the documents Use human reviewers to evaluate the incorrect translations

D.  

Use Vertex Al custom training jobs to fine-tune a state-of-the-art open source pretrained model with your data Deploy the model to a Vertex Al endpoint with autoscaling and model monitoring When there is a predetermined skew between the training and live data, configure a trigger to run another training job with the latest data.

A.  

1, Maintain the same machine type on the endpoint.

2 Set up a monitoring job and an alert for CPU usage

3 If you receive an alert add a compute node to the endpoint

B.  

1 Change the machine type on the endpoint to have 32 vCPUs

2. Set up a monitoring job and an alert for CPU usage

3 If you receive an alert, scale the vCPUs further as needed

C.  

1 Maintain the same machine type on the endpoint Configure the endpoint to enable autoscalling based on vCPU usage.

2 Set up a monitoring job and an alert for CPU usage

3 If you receive an alert investigate the cause

D.  

1 Change the machine type on the endpoint to have a GPU_ Configure the endpoint to enable autoscaling based on the GPU usage.

2 Set up a monitoring job and an alert for GPU usage.

3 If you receive an alert investigate the cause.

A.  

Split incoming traffic among separate Cloud Run instances of deployed models. Monitor the performance of each version using Cloud Monitoring.

B.  

Split incoming traffic to distribute prediction requests among the versions. Monitor the performance of each version using Looker Studio dashboards that compare logged data for each version.

C.  

Split incoming traffic among Google Kubernetes Engine (GKE) clusters and use Traffic Director to distribute prediction requests to different versions. Monitor the performance of each version using Cloud Monitoring.

D.  

Split incoming traffic to distribute prediction requests among the versions. Monitor the performance of each version using Vertex AI’s built-in monitoring tools.

A.  

1 Upload your model to the Vertex Al Model Registry by using a prebuilt scikit-learn prediction container

2 Deploy your model to Vertex Al Endpoints, and create a Vertex Al batch prediction job that uses the instanceConfig.inscanceType setting to transform your input data

B.  

1 Wrap your model in a custom prediction routine (CPR). and build a container image from the CPR local model

2 Upload your sci-kit learn model container to Vertex Al Model Registry

3 Deploy your model to Vertex Al Endpoints, and create a Vertex Al batch prediction job

C.  

1. Create a custom container for your sci-kit learn model,

2 Define a custom serving function for your model

3 Upload your model and custom container to Vertex Al Model Registry

4 Deploy your model to Vertex Al Endpoints, and create a Vertex Al batch prediction job

D.  

1 Create a custom container for your sci-kit learn model.

2 Upload your model and custom container to Vertex Al Model Registry

3 Deploy your model to Vertex Al Endpoints, and create a Vertex Al batch prediction job that uses the instanceConfig. instanceType setting to transform your input data

A.  

Use a clustering algorithm to group popular items together. Give the list to the logistics team so they can increase inventory of the popular items.

B.  

Use a regression model to predict how much additional inventory should be purchased each month. Give the results to the logistics team at the beginning of the month so they can increase inventory by the amount predicted by the model.

C.  

Use a time series forecasting model to predict each item ' s monthly sales. Give the results to the logistics team so they can base inventory on the amount predicted by the model.

D.  

Use a classification model to classify inventory levels as UNDER_STOCKED, OVER_STOCKED, and CORRECTLY_STOCKE

D.  

A.  

Build a classification model

B.  

Build a knowledge-based filtering model

C.  

Build a collaborative-based filtering model

D.  

Build a regression model using the features as predictors

A.  

Build a random forest regression model in a Vertex Al Workbench notebook instance Configure the model to generate feature importance’s after the model is trained.

B.  

Build an AutoML tabular regression model Configure the model to generate explanations when it makes predictions.

C.  

Build a custom TensorFlow neural network by using Vertex Al custom training Configure the model to generate explanations when it makes predictions.

D.  

Build a random forest classification model in a Vertex Al Workbench notebook instance Configure the model to generate feature importance’s after the model is trained.