- (Exam Topic 6)
Your company is selecting a system to centralize data ingestion and delivery. You are considering messaging and data integration systems to address the requirements. The key requirements are:
The ability to seek to a particular offset in a topic, possibly back to the start of all data ever captured
Support for publish/subscribe semantics on hundreds of topics
Retain per-key ordering
Which system should you choose?

1. A. Apache Kafka
2. B. Cloud Storage
3. C. Cloud Pub/Sub
4. D. Firebase Cloud Messaging

Correct Answer: A

- (Exam Topic 5)
What are two of the characteristics of using online prediction rather than batch prediction?

1. A. It is optimized to handle a high volume of data instances in a job and to run more complex models.
2. B. Predictions are returned in the response message.
3. C. Predictions are written to output files in a Cloud Storage location that you specify.
4. D. It is optimized to minimize the latency of serving predictions.

Correct Answer: BD
Online prediction
Optimized to minimize the latency of serving predictions.
Predictions returned in the response message. Batch prediction
Optimized to handle a high volume of instances in a job and to run more complex models. Predictions written to output files in a Cloud Storage location that you specify.
Reference:
https://cloud.google.com/ml-engine/docs/prediction-overview#online_prediction_versus_batch_prediction

- (Exam Topic 5)
Which of these statements about BigQuery caching is true?

1. A. By default, a query's results are not cached.
2. B. BigQuery caches query results for 48 hours.
3. C. Query results are cached even if you specify a destination table.
4. D. There is no charge for a query that retrieves its results from cache.

Correct Answer: D
When query results are retrieved from a cached results table, you are not charged for the query. BigQuery caches query results for 24 hours, not 48 hours.
Query results are not cached if you specify a destination table.
A query's results are always cached except under certain conditions, such as if you specify a destination table. Reference: https://cloud.google.com/bigquery/querying-data#query-caching

- (Exam Topic 6)
You are building a data pipeline on Google Cloud. You need to prepare data using a casual method for a machine-learning process. You want to support a logistic regression model. You also need to monitor and adjust for null values, which must remain real-valued and cannot be removed. What should you do?

1. A. Use Cloud Dataprep to find null values in sample source dat
2. B. Convert all nulls to ‘none’ using a Cloud Dataproc job.
3. C. Use Cloud Dataprep to find null values in sample source dat
4. D. Convert all nulls to 0 using a Cloud Dataprep job.
5. E. Use Cloud Dataflow to find null values in sample source dat
6. F. Convert all nulls to ‘none’ using a Cloud Dataprep job.
7. G. Use Cloud Dataflow to find null values in sample source dat
8. H. Convert all nulls to using a custom script.

Correct Answer: C

- (Exam Topic 6)
Government regulations in the banking industry mandate the protection of client’s personally identifiable information (PII). Your company requires PII to be access controlled encrypted and compliant with major data protection standards In addition to using Cloud Data Loss Prevention (Cloud DIP) you want to follow
Google-recommended practices and use service accounts to control access to PII. What should you do?

1. A. Assign the required identity and Access Management (IAM) roles to every employee, and create a single service account to access protect resources
2. B. Use one service account to access a Cloud SQL database and use separate service accounts for each human user
3. C. Use Cloud Storage to comply with major data protection standard
4. D. Use one service account shared by all users
5. E. Use Cloud Storage to comply with major data protection standard
6. F. Use multiple service accounts attached to IAM groups to grant the appropriate access to each group

Correct Answer: D