Professional-Machine-Learning-Engineer Practice Exam Questions and Answers

A.  

Store features in Bigtable as key/value data.

B.  

Store features in Vertex Al Feature Store.

C.  

Store features as a Vertex Al dataset and use those features to tram the models hosted in Vertex Al endpoints.

D.  

Store features in BigQuery timestamp partitioned tables, and use the BigQuery Storage Read API to serve the features.

A.  

Add a pipeline parameter and an additional pipeline step Depending on the parameter value the pipeline step conducts or skips data preprocessing and starts model training.

B.  

Create another pipeline without the preprocessing step, and hardcode the preprocessed Cloud Storage file location for model training.

C.  

Configure a machine with more CPU and RAM from the compute-optimized machine family for the data preprocessing step.

D.  

Enable caching for the pipeline job. and disable caching for the model training step.

A.  

Use the Healthcare Natural Language API to extract medical entities.

B.  

Use a BERT-based model to fine-tune a medical entity extraction model.

C.  

Use AutoML Entity Extraction to train a medical entity extraction model.

D.  

Use TensorFlow to build a custom medical entity extraction model.

A.  

Create a linear regression model in BigQuery ML and register the model in Vertex Al Model Registry Evaluate the model performance in Vertex Al.

B.  

Create a logistic regression model in BigQuery ML and register the model in Vertex Al Model Registry. Evaluate the model performance in Vertex Al.

C.  

Create a linear regression model in BigQuery ML Use the ml. evaluate function to evaluate the model performance.

D.  

Create a logistic regression model in BigQuery ML Use the ml.confusion_matrix function to evaluate the model performance.

A.  

Increase the size of the training batch

B.  

Decrease the size of the training batch

C.  

Increase the learning rate hyperparameter

D.  

Decrease the learning rate hyperparameter

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.  

Download the weather and flu data each week Configure Cloud Scheduler to execute a Vertex Al pipeline to retrain the model weekly.

B.  

Download the weather and flu data each month Configure Cloud Scheduler to execute a Vertex Al pipeline to retrain the model monthly.

C.  

Download the weather and flu data each week Configure Cloud Scheduler to execute a Vertex Al pipeline to retrain the model every month.

D.  

Download the weather data each week, and download the flu data each month Deploy the model to a Vertex Al endpoint with feature drift monitoring. and retrain the model if a monitoring alert is detected.

A.  

Create multiple models using AutoML Tables

B.  

Automate multiple training runs using Cloud Composer

C.  

Run multiple training jobs on Al Platform with similar job names

D.  

Create an experiment in Kubeflow Pipelines to organize multiple runs

A.  

Load the data into BigQuery and read the data from BigQuery.

B.  

Load the data into Cloud Bigtable, and read the data from Bigtable

C.  

Convert the CSV files into shards of TFRecords, and store the data in Cloud Storage

D.  

Convert the CSV files into shards of TFRecords, and store the data in the Hadoop Distributed File System (HDFS)

A.  

1. Enable request-response logging on Vertex Al Endpoints.

2 Schedule a TensorFlow Data Validation job to monitor prediction drift

3. Execute model retraining if there is significant distance between the distributions.

B.  

1. Enable request-response logging on Vertex Al Endpoints

2. Schedule a TensorFlow Data Validation job to monitor training/serving skew

3. 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 is detected.

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 skew

2. Configure alert monitoring to publish a message to a Pub/Sub queue when a monitoring alert is detected

3. Use a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery.