Building Resilient Streaming Analytics Systems on GCP complete course is currently being offered by Google Cloud through Coursera platform.

About this Course:
Processing streaming data is becoming increasingly popular as streaming enables businesses to get real-time metrics on business operations. This course covers how to build streaming data pipelines on Google Cloud. Pub/Sub is described for handling incoming streaming data. The course also covers how to apply aggregations and transformations to streaming data using 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 using QwikLabs.
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
- B
- A
- D
- C
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
- False
- True
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:
- B
- A
- D
- C
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
- True
- False
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
- True
- False
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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