You are developing a Kubeflow pipeline on Google Kubernetes Engine. The first step in the pipeline is to issue a query against BigQuery. You plan to use the results of that query as the input to the next step in your pipeline. You want to achieve this in the easiest way possible. What should you do?

1. A. Use the BigQuery console to execute your query and then save the query results Into a new BigQuery table.
2. B. Write a Python script that uses the BigQuery API to execute queries against BigQuery Execute this script as the first step in your Kubeflow pipeline
3. C. Use the Kubeflow Pipelines domain-specific language to create a custom component that uses the Python BigQuery client library to execute queries
4. D. Locate the Kubeflow Pipelines repository on GitHub Find the BigQuery Query Component, copy that component's URL, and use it to load the component into your pipelin
5. E. Use the component to execute queries against BigQuery

Correct Answer: A

You need to design a customized deep neural network in Keras that will predict customer purchases based on their purchase history. You want to explore model performance using multiple model architectures, store training data, and be able to compare the evaluation metrics in the same dashboard. What should you do?

1. A. Create multiple models using AutoML Tables
2. B. Automate multiple training runs using Cloud Composer
3. C. Run multiple training jobs on Al Platform with similar job names
4. D. Create an experiment in Kubeflow Pipelines to organize multiple runs

Correct Answer: C

You work for an advertising company and want to understand the effectiveness of your company's latest advertising campaign. You have streamed 500 MB of campaign data into BigQuery. You want to query the table, and then manipulate the results of that query with a pandas dataframe in an Al Platform notebook. What should you do?

1. A. Use Al Platform Notebooks' BigQuery cell magic to query the data, and ingest the results as a pandas dataframe
2. B. Export your table as a CSV file from BigQuery to Google Drive, and use the Google Drive API to ingest the file into your notebook instance
3. C. Download your table from BigQuery as a local CSV file, and upload it to your Al Platform notebook instance Use panda
4. D. read_csv to ingest the file as a pandas dataframe
5. E. From a bash cell in your Al Platform notebook, use the bq extract command to export the table as a CSV file to Cloud Storage, and then use gsutii cp to copy the data into the notebook Use panda
6. F. read_csv to ingest the file as a pandas dataframe

Correct Answer: B

You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?

1. A. Ensure that training is reproducible
2. B. Ensure that all hyperparameters are tuned
3. C. Ensure that model performance is monitored
4. D. Ensure that feature expectations are captured in the schema

Correct Answer: B

You need to train a computer vision model that predicts the type of government ID present in a given image using a GPU-powered virtual machine on Compute Engine. You use the following parameters:
• Optimizer: SGD
• Image shape = 224x224
• Batch size = 64
• Epochs = 10
• Verbose = 2
During training you encounter the following error: ResourceExhaustedError: out of Memory (oom) when allocating tensor. What should you do?

1. A. Change the optimizer
2. B. Reduce the batch size
3. C. Change the learning rate
4. D. Reduce the image shape

Correct Answer: A