You are building a model to make clothing recommendations. You know a user’s fashion preference is likely to change over time, so you build a data pipeline to
stream new data back to the model as it becomes available. How should you use this data to train the model?
A. Continuously retrain the model on just the new data.
B. Continuously retrain the model on a combination of existing data and the new data.
C. Train on the existing data while using the new data as your test set.
D. Train on the new data while using the existing data as your test set.

Your company is streaming real-time sensor data from their factory floor into Bigtable and they have noticed extremely poor performance. How should the row key
be redesigned to improve Bigtable performance on queries that populate real-time dashboards?
A. Use a row key of the form <timestamp>.
B. Use a row key of the form <sensorid>.
C. Use a row key of the form <timestamp>#<sensorid>.
D. Use a row key of the form >#<sensorid>#<timestamp>.

Your company handles data processing for a number of different clients. Each client prefers to use their own suite of analytics tools, with some allowing direct query access via Google BigQuery. You need to secure the data so that clients cannot see each other’s data. You want to ensure appropriate access to the data.

Which three steps should you take? (Choose three.)

A. Load data into different partitions.
B. Load data into a different dataset for each client.
C. Put each client’s BigQuery dataset into a different table.
D. Restrict a client’s dataset to approved users.
E. Only allow a service account to access the datasets.
F. Use the appropriate identity and access management (IAM) roles for each client’s users.

Your company is in a highly regulated industry. One of your requirements is to ensure individual users have access only to the minimum amount of information
required to do their jobs. You want to enforce this requirement with Google BigQuery.

Which three approaches can you take? (Choose three.)

A. Disable writes to certain tables.
B. Restrict access to tables by role.
C. Ensure that the data is encrypted at all times.
D. Restrict BigQuery API access to approved users.
E. Segregate data across multiple tables or databases.
F. Use Google Stackdriver Audit Logging to determine policy violations.

Your startup has never implemented a formal security policy. Currently, everyone in the company has access to the datasets stored in Google BigQuery. Teams
have freedom to use the service as they see fit, and they have not documented their use cases. You have been asked to secure the data warehouse. You need to
discover what everyone is doing. What should you do first?
A. Use Google Stackdriver Audit Logs to review data access.
B. Get the identity and access management IIAM) policy of each table
C. Use Stackdriver Monitoring to see the usage of BigQuery query slots.
D. Use the Google Cloud Billing API to see what account the warehouse is being billed to.

You are building new real-time data warehouse for your company and will use Google BigQuery streaming inserts. There is no guarantee that data will only be sent in once but you do have a unique ID for each row of data and an event timestamp. You want to ensure that duplicates are not included while interactively
querying data.

Which query type should you use?

A. Include ORDER BY DESK on timestamp column and LIMIT to 1.
B. Use GROUP BY on the unique ID column and timestamp column and SUM on the values.
C. Use the LAG window function with PARTITION by unique ID along with WHERE LAG IS NOT NULL.
D. Use the ROW_NUMBER window function with PARTITION by unique ID along with WHERE row equals 1.

You have Google Cloud Dataflow streaming pipeline running with a Google Cloud Pub/Sub subscription as the source. You need to make an update to the code that will make the new Cloud Dataflow pipeline incompatible with the current version. You do not want to lose any data when making this update.

What should you do?

A. Update the current pipeline and use the drain flag.
B. Update the current pipeline and provide the transform mapping JSON object.
C. Create a new pipeline that has the same Cloud Pub/Sub subscription and cancel the old pipeline.
D. Create a new pipeline that has a new Cloud Pub/Sub subscription and cancel the old pipeline.

You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training
speed by removing some features while having a minimum effect on model accuracy. What can you do?
A. Eliminate features that are highly correlated to the output labels.
B. Combine highly co-dependent features into one representative feature.
C. Instead of feeding in each feature individually, average their values in batches of 3.
D. Remove the features that have null values for more than 50% of the training records.

You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you
do not want to manage infrastructure scaling.
Which Google database service should you use?
A. Cloud SQL
B. BigQuery
C. Cloud Bigtable
D. Cloud Datastore