Your company needs to ingest and transform streaming data from IoT devices and store it for analysis. The data is sensitive and requires encryption with your own key in transit and at rest. The volume of data is expected to fluctuate significantly throughout the day. You need to identify a solution that is managed and elastic. What should you do?
Correct Answer: C
Comprehensive and Detailed Explanation: This question describes a classic, scalable streaming analytics architecture on Google Cloud. Option C is the correct answer as it combines the best-in-class managed services for each part of the pipeline. Pub/Sub is a fully managed, highly scalable messaging service perfect for ingesting fluctuating volumes of streaming IoT data. Dataflow is a fully managed, serverless service for stream and batch processing that automatically scales resources up and down to handle fluctuating data volumes. BigQuery is a serverless, highly scalable data warehouse optimized for analytics. Cloud KMS is the standard Google Cloud service for creating and managing your own cryptographic keys, which are then used to enable Customer-Managed Encryption Keys (CMEK) across services like Pub/Sub, Dataflow, and BigQuery, satisfying the security requirement. Option A is incorrect because it specifies using a Google-managed encryption key, which violates the requirement for encryption with "your own key." Option B is incorrect because Cloud SQL is a relational database (OLTP), not an analytical data warehouse (OLAP), making it unsuitable for storing and analyzing large volumes of streaming data. BigQuery is the appropriate choice. Option D is incorrect because Dataproc is a managed Hadoop/Spark service. While powerful, it is less "managed" and serverless than Dataflow, as you still need to provision and manage clusters. For a fully elastic and managed solution, Dataflow is the preferred choice. Reference (Google Cloud Documentation Concepts): This architecture is a canonical pattern for streaming analytics on Google Cloud. The "Streaming analytics" solution guide frequently highlights the Pub/Sub -> Dataflow -> BigQuery pattern. Each of these services supports Customer-Managed Encryption Keys (CMEK) using keys from Cloud KMS, ensuring data is protected at rest with customer-controlled keys, which aligns with the principle of customer control over data security.
Question 82
Your company's customer and order databases are often under heavy load. This makes performing analytics against them difficult without harming operations. The databases are in a MySQL cluster, with nightly backups taken using mysqldump. You want to perform analytics with minimal impact on operations. What should you do?
Correct Answer: D
Question 83
In order to securely transfer web traffic data from your computer's web browser to the Cloud Dataproc cluster you should use a(n) _____.
Correct Answer: C
Explanation To connect to the web interfaces, it is recommended to use an SSH tunnel to create a secure connection to the master node. Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#connecting_to_the_web_interfaces
Question 84
Which of the following is NOT one of the three main types of triggers that Dataflow supports?
Correct Answer: A
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers. You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way Reference: https://cloud.google.com/dataflow/model/triggers
Question 85
You have an Oracle database deployed in a VM as part of a Virtual Private Cloud (VPC) network. You want to replicate and continuously synchronize 50 tables to BigQuery. You want to minimize the need to manage infrastructure. What should you do?
Correct Answer: A
Datastream is a serverless, scalable, and reliable service that enables you to stream data changes from Oracle and MySQL databases to Google Cloud services such as BigQuery, Cloud SQL, Google Cloud Storage, and Cloud Pub/Sub. Datastream captures and streams database changes using change data capture (CDC) technology. Datastream supports private connectivity to the source and destination systems using VPC networks. Datastream also provides a connection profile to BigQuery, which simplifies the configuration and management of the data replication. Reference: Datastream overview Creating a Datastream stream Using Datastream with BigQuery
