- (Exam Topic 1)
You work for a car manufacturer and have set up a data pipeline using Google Cloud Pub/Sub to capture anomalous sensor events. You are using a push subscription in Cloud Pub/Sub that calls a custom HTTPS endpoint that you have created to take action of these anomalous events as they occur. Your custom HTTPS endpoint keeps getting an inordinate amount of duplicate messages. What is the most likely cause of these duplicate messages?

1. A. The message body for the sensor event is too large.
2. B. Your custom endpoint has an out-of-date SSL certificate.
3. C. The Cloud Pub/Sub topic has too many messages published to it.
4. D. Your custom endpoint is not acknowledging messages within the acknowledgement deadline.

Correct Answer: B

- (Exam Topic 4)
You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of-Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about 100 attributes. The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID). However, high availability and low latency are required.
You need to analyze the data by querying against individual fields. Which three databases meet your requirements? (Choose three.)

1. A. Redis
2. B. HBase
3. C. MySQL
4. D. MongoDB
5. E. Cassandra
6. F. HDFS with Hive

Correct Answer: BDF

- (Exam Topic 5)
What are two methods that can be used to denormalize tables in BigQuery?

1. A. 1) Split table into multiple tables; 2) Use a partitioned table
2. B. 1) Join tables into one table; 2) Use nested repeated fields
3. C. 1) Use a partitioned table; 2) Join tables into one table
4. D. 1) Use nested repeated fields; 2) Use a partitioned table

Correct Answer: B
The conventional method of denormalizing data involves simply writing a fact, along with all its dimensions, into a flat table structure. For example, if you are dealing with sales transactions, you would write each individual fact to a record, along with the accompanying dimensions such as order and customer information.
The other method for denormalizing data takes advantage of BigQuery’s native support for nested and repeated structures in JSON or Avro input data. Expressing records using nested and repeated structures can provide a more natural representation of the underlying data. In the case of the sales order, the outer part of a JSON structure would contain the order and customer information, and the inner part of the structure would contain the individual line items of the order, which would be represented as nested, repeated elements.
Reference: https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data

- (Exam Topic 6)
You set up a streaming data insert into a Redis cluster via a Kafka cluster. Both clusters are running on Compute Engine instances. You need to encrypt data at rest with encryption keys that you can create, rotate, and destroy as needed. What should you do?

1. A. Create a dedicated service account, and use encryption at rest to reference your data stored in your Compute Engine cluster instances as part of your API service calls.
2. B. Create encryption keys in Cloud Key Management Servic
3. C. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
4. D. Create encryption keys locall
5. E. Upload your encryption keys to Cloud Key Management Servic
6. F. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
7. G. Create encryption keys in Cloud Key Management Servic
8. H. Reference those keys in your API service calls when accessing the data in your Compute Engine cluster instances.

Correct Answer: C

- (Exam Topic 6)
You work for an advertising company, and you’ve developed a Spark ML model to predict click-through rates at advertisement blocks. You’ve been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be migrated to BigQuery. You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?

1. A. Use Cloud ML Engine for training existing Spark ML models
2. B. Rewrite your models on TensorFlow, and start using Cloud ML Engine
3. C. Use Cloud Dataproc for training existing Spark ML models, but start reading data directly from BigQuery
4. D. Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery

Correct Answer: A