Question 1

You are an ML engineer on an agricultural research team working on a crop disease detection tool to detect leaf rust spots in images of crops to determine the presence of a disease. These spots, which can vary in shape and size, are correlated to the severity of the disease. You want to develop a solution that predicts the presence and severity of the disease with high accuracy. What should you do?

Options :

A : Create an object detection model that can localize the rust spots.

B : Develop an image segmentation ML model to locate the boundaries of the rust spots.

C : Develop a template matching algorithm using traditional computer vision libraries.

D : Develop an image classification ML model to predict the presence of the disease.

Answer: B

Question 2

You recently designed and built a custom neural network that uses critical dependencies specific to your organization’s framework. You need to train the model using a managed training service on Google Cloud. However, the ML framework and related dependencies are not supported by AI Platform Training. Also, both your model and your data are too large to fit in memory on a single machine. Your ML framework of choice uses the scheduler, workers, and servers distribution structure. What should you do?

Options :

A : Use a built-in model available on AI Platform Training.

B : Build your custom container to run jobs on AI Platform Training.

C : Build your custom containers to run distributed training jobs on AI Platform Training.

D : Reconfigure your code to a ML framework with dependencies that are supported by AI Platform Training.

Answer: D

Question 3

You work for a toy manufacturer that has been experiencing a large increase in demand. You need to build an ML model to reduce the amount of time spent by quality control inspectors checking for product defects. Faster defect detection is a priority. The factory does not have reliable Wi-Fi. Your company wants to implement the new ML model as soon as possible. Which model should you use?

Options :

A : AutoML Vision Edge mobile-high-accuracy-1 model

B : AutoML Vision Edge mobile-low-latency-1 model

C : AutoML Vision model

D : AutoML Vision Edge mobile-versatile-1 model

Answer: B

Question 4

You are training an object detection machine learning model on a dataset that consists of three million X-ray images, each roughly 2 GB in size. You are using Vertex AI Training to run a custom training application on a Compute Engine instance with 32-cores, 128 GB of RAM, and 1 NVIDIA P100 GPU. You notice that model training is taking a very long time. You want to decrease training time without sacrificing model performance. What should you do?

Options :

A : Increase the instance memory to 512 GB, and increase the batch size.

B : Replace the NVIDIA P100 GPU with a K80 GPU in the training job.

C : Enable early stopping in your Vertex AI Training job.

D : Use the tf.distribute.Strategy API and run a distributed training job.

Answer: D

Question 5

As the lead ML Engineer for your company, you are responsible for building ML models to digitize scanned customer forms. You have developed a TensorFlow model that converts the scanned images into text and stores them in Cloud Storage. You need to use your ML model on the aggregated data collected at the end of each day with minimal manual intervention. What should you do?

Options :

A : Use the batch prediction functionality of AI Platform.

B : Create a serving pipeline in Compute Engine for prediction.

C : Use Cloud Functions for prediction each time a new data point is ingested

D : Deploy the model on AI Platform and create a version of it for online inference.

Answer: A