Question 1

You work for a bank. You have created a custom model to predict whether a loan application should be flagged for human review. The input features are stored in a BigQuery table. The model is performing well, and you plan to deploy it to production. Due to compliance requirements the model must provide explanations for each prediction. You want to add this functionality to your model code with minimal effort and provide explanations that are as accurate as possible. What should you do?

Options :

A : Create an AutoML tabular model by using the BigQuery data with integrated Vertex Explainable AI.

B : Create a BigQuery ML deep neural network model and use the ML.EXPLAIN_PREDICT method with the num_integral_steps parameter.

C : Upload the custom model to Vertex AI Model Registry and configure feature-based attribution by using sampled Shapley with input baselines.

D : Update the custom serving container to include sampled Shapley-based explanations in the prediction outputs.

Answer: C

Question 2

You are developing ML models with AI Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?

Options :

A : Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job.

B : Use the gcloud command-line tool to submit training jobs on AI Platform when you update your code.

C : Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository

D : Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.

Answer: B

Question 3

You are an ML engineer at a manufacturing company. You need to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. You want your model to preprocess the images with lower computation to quickly extract features of defects in products. Which approach should you use to build the model?

Options :

A : Reinforcement learning

B : Recommender system

C : Recurrent Neural Networks (RNN)

D : Convolutional Neural Networks (CNN)

Answer: D

Question 4

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 5

You have been given a dataset with sales predictions based on your companys 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 dat a. 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?

Options :

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

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

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

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

Answer: A

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