About Building Resilient Streaming Analytics Systems on GCP Course
Processing streaming data is becoming increasingly popular as streaming enables businesses to get real-time metrics on business operations. Building Resilient Streaming Analytics Systems on GCP course covers how to build streaming data pipelines on Google Cloud Platform. Cloud Pub/Sub is described for handling incoming streaming data.
The course also covers how to apply aggregations and transformations to streaming data using Cloud Dataflow, and how to store processed records to BigQuery or Cloud Bigtable for analysis. Learners will get hands-on experience building streaming data pipeline components on Google Cloud Platform using QwikLabs.
Building Resilient Streaming Analytics Systems on GCP – All Quiz Answers
Introduction to Processing Streaming Data
Q1. Dataflow offers the following that makes it easy to create resilient streaming pipelines when working with unbounded data:
(Select all 2 correct responses)
- Ability to flexibly reason about time
- Controls to ensure correctness
- Global message bus to buffer messages
- SQL support to query in-process results
Q2. Match the GCP product with its role when designing streaming systems
Serverless Messaging with Cloud Pub/Sub
Q1. Which of the following about Cloud Pub/Sub is NOT true?
- Pub/Sub simplifies systems by removing the need for every component to speak to every component
- Pub/Sub connects applications and services through a messaging infrastructure
- Pub/Sub stores your messages indefinitely until you request it
Q2. Cloud Pub/Sub guarantees that messages delivered are in the order they were received
Q3. Which of the following about Cloud Pub/Sub topics and subscriptions are true? (Select all 2 correct responses)
- 1 or more publisher(s) can write to the same topic
- 1 or more subscriber(s) can request from the same subscription
- Each topic will deliver ALL messages for a topic for each subscriber
- Each topic MUST have at least 1 subscription
Q4. Which of the following delivery methods is ideal for subscribers needing close to real-time performance?
- Pull Delivery
- Push Delivery
Cloud Dataflow Streaming Features
Q1. The Dataflow models provide constructs that map to the four questions that are relevant in any out-of-order data processing pipeline:
Streaming Analytics and Dashboards
Q1. Which of the following is true for Data Studio?
- Data Studio can only ingest files stored in Cloud Storage buckets.
- Data Studio supports data ingest through multiple connectors.
- Data Studio is part of Dataflow and requires a streaming pipeline for data ingest.
- Data Studio is part of Google BigQuery and requires data to already exist in tables.
Q2. Data Studio can issue queries to BigQuery
High-Throughput Streaming with Cloud Bigtable
Q1. Which of the following are true about Cloud Bigtable? (Mark all 3 correct responses)
- Offers very low latency in the order of milliseconds
- Ideal for >1TB data
- Great for time-series data
- Support for SQL
Q2. Cloud Bigtable learns access patterns and attempts to distribute reads and storage across nodes evenly
Q3. Which of the following can help improve the performance of Bigtable? (Select all 3 correct responses)
- Change schema to minimize data skew
- Clients and Bigtable are in same zone
- Use HDD instead of SDD
- Add more nodes
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