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LWT

S201: Data Modeling and Application Development Lightweight Transactions Lightweight Transactions Under the Hood LWT

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Question 1 of 5

1. Question

In ScyllaDB, when not using LWT, on average:

Writes are significantly faster than reads.

Reads are significantly faster than writes.

Reads and writes have similar performance.

Correct

Incorrect
Question 2 of 5

2. Question

Lightweight Transactions are also called:

Conditional updates

SQL inserts

CQL inserts

CQL updates

Correct

Incorrect
Question 3 of 5

3. Question

Which of the following are valid?

INSERT INTO bookings (id, item, client, quantity) VALUES (…) IF NOT EXISTS;

UPDATE inventory SET state = 'Used' WHERE itemid = ? IF state = 'Unused' AND check = 'Passed';

DELETE FROM tasks WHERE project_id = ? AND task_id = ? IF task['state'] IN ('Complete', 'Abandoned');

All answers are valid

Correct

Incorrect
Question 4 of 5

4. Question

In conditional batches, if any of the conditions are not true:

Nothing is done

Only the parts that are true are executed

All parts are executed

None of the answers are true

Correct

Incorrect
Question 5 of 5

5. Question

Using LWT compared to regular queries:

Improves consistency but impacts performance

Makes no difference in terms of performance

Makes no difference in terms of consistency

Improves performance but reduces consistency

Correct

Incorrect

Course Home

Basic Data Modeling

CQL and the CQL Shell – Part 1

CQL and the CQL Shell – Part 2

Overview and Basic Concepts – Part 1

Overview and Basic Concepts – Part 2

Primary Key, Partition Key, Clustering Key – Part 1

Primary Key, Partition Key, Clustering Key – Part 2

Primary Key, Partition Key, Clustering Key – Part 3

Importance of Primary Key Selection

Importance of the Clustering Key

Importance of Key Selection

Data Modeling Lab Walkthrough – Part 1 – Simple Primary Key

Data Modeling Lab Walkthrough – Part 2 – Composite Primary Key, Clustering Key

Data Modeling Lab Walkthrough – Part 3 – Key Selection, Sorting Order

Basic Data Modeling Lab

Advanced Data Modeling

Recap: ScyllaDB Vs Relational

Advanced Primary Key Selection

Application Workflow And Query Analysis

Common Data Types and Collections

Collections and Drivers

Expiring Data with TTL (Time to Live)

User Defined Types (UDT)

Denormalization

Materialized Views, Secondary Indexes, and Filtering

Materialized Views – Overview

Materialized Views – Under the Hood

Secondary Indexes

Materialized Views Hands-On Lab

Local Secondary Indexes

Secondary Indexes Hands-On Lab

Summary and Which One Should You Use

How to Write Better Apps

How to Write Better Apps – Overview

General Data Modeling Guidelines

Large Partitions and Collections

Hot Partitions, Cardinality and Tombstones

Diagnosis and Prevention

Overview, Monitoring Prepared Statements, and Token Aware

Filtering and Denormalizing Data

Scaling to Multiple Datacenters and Application Enhancements

Non-Paged and Reverse CQL Reads

Lightweight Transactions

Lightweight Transactions at a Glance

Lightweight Transactions Performance

Lightweight Transactions Under the Hood

Change Data Capture (CDC)

Change Data Capture (CDC) Overview

CDC Consuming the Data

CDC Under the Hood

Course Feedback

Return to S201: Data Modeling and Application Development

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