Your organization wants to make its internal shuttle service route more efficient. The shuttles currently stop at all pick-up points across the city every 30 minutes between 7 am and 10 am. The development team has already built an application on Google Kubernetes Engine that requires users to confirm their presence and shuttle station one day in advance. What approach should you take?

1. A. 1. Build a tree-based regression model that predicts how many passengers will be picked up at each shuttle station.* 2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the prediction.
2. B. 1. Build a tree-based classification model that predicts whether the shuttle should pick up passengers at each shuttle station.* 2. Dispatch an available shuttle and provide the map with the required stops based on the prediction
3. C. 1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints.* 2 Dispatch an appropriately sized shuttle and indicate the required stops on the map
4. D. 1. Build a reinforcement learning model with tree-based classification models that predict the presenceof passengers at shuttle stops as agents and a reward function around a distance-based metric* 2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the simulated outcome.

Correct Answer: D

You need to build classification workflows over several structured datasets currently stored in BigQuery.
Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?

1. A. Configure AutoML Tables to perform the classification task
2. B. Run a BigQuery ML task to perform logistic regression for the classification
3. C. Use Al Platform Notebooks to run the classification model with pandas library
4. D. Use Al Platform to run the classification model job configured for hyperparameter tuning

Correct Answer: C

You are training an LSTM-based model on Al Platform to summarize text using the following job submission script:

You want to ensure that training time is minimized without significantly compromising the accuracy of your model. What should you do?

1. A. Modify the 'epochs' parameter
2. B. Modify the 'scale-tier' parameter
3. C. Modify the batch size' parameter
4. D. Modify the 'learning rate' parameter

Correct Answer: A

You want to rebuild your ML pipeline for structured data on Google Cloud. You are using PySpark to conduct data transformations at scale, but your pipelines are taking over 12 hours to run. To speed up development and pipeline run time, you want to use a serverless tool and SQL syntax. You have already moved your raw data into Cloud Storage. How should you build the pipeline on Google Cloud while meeting the speed and processing requirements?

1. A. Use Data Fusion's GUI to build the transformation pipelines, and then write the data into BigQuery
2. B. Convert your PySpark into SparkSQL queries to transform the data and then run your pipeline on Dataproc to write the data into BigQuery.
3. C. Ingest your data into Cloud SQL convert your PySpark commands into SQL queries to transform the data, and then use federated queries from BigQuery for machine learning
4. D. Ingest your data into BigQuery using BigQuery Load, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table

Correct Answer: B

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?

1. A. Use the batch prediction functionality of Al Platform
2. B. Create a serving pipeline in Compute Engine for prediction
3. C. Use Cloud Functions for prediction each time a new data point is ingested
4. D. Deploy the model on Al Platform and create a version of it for online inference.

Correct Answer: D