easyMultiple ChoiceObjective-mapped
Cloud Digital Leader Practice Question: A web application's homepage loads user-specific…
A web application's homepage loads user-specific data (shopping cart, recent orders) on every visit. The data changes frequently. An engineer suggests caching this data in a Redis cache between the web tier and the database. What is the primary benefit of this caching layer?
⚠ Common exam trap
Google Cloud often tests the misconception that caching provides permanent storage or replaces the database, leading candidates to incorrectly select Option C, but the trap here is that caching is a temporary, performance-enhancing layer, not a durable storage solution.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Caching reduces database load and improves response times by serving frequently accessed data from fast in-memory storage.
Caching user-specific data like shopping carts and recent orders in Redis reduces the load on the primary database by serving frequently accessed data from fast in-memory storage. This improves response times for the web application, as Redis can deliver data in microseconds compared to the millisecond latency of a typical relational database query. The caching layer acts as a temporary, high-speed buffer that offloads read-heavy traffic from the database, which is especially beneficial for data that changes frequently but is read often.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Caching encrypts data in transit between the web tier and database.
Why it's wrong here
Caching is an application-layer performance optimization that stores copies of computed or frequently accessed data in fast memory (e.g., Redis, Memorystore), not a security feature. Encryption in transit is provided by TLS/SSL between clients and the web tier, and the database connection typically uses its own TLS channel. A cache does not encrypt data, and misidentifying it as encryption conflates performance with confidentiality.
- ✓
Caching reduces database load and improves response times by serving frequently accessed data from fast in-memory storage.
Why this is correct
Caching improves performance by placing frequently accessed data (such as product details or user sessions) into an in-memory data store like Redis or Memorystore, which can serve cache hits in microseconds. This offloads repeated read queries from the database, reducing its CPU and I/O load, lowering query latency, and enabling the app to handle more concurrent users. The cache is a throughput multiplier because only cache misses fall through to the database, which is the durable source of truth.
- ✗
Caching permanently stores user data so the database can be deleted.
Why it's wrong here
A cache is an ephemeral store: entries are subject to eviction based on LRU policies, memory pressure, or Time-To-Live (TTL) expiration, and they are invalidated when the underlying data changes. The database remains the durable system of record, and deleting it would cause permanent data loss that cannot be recovered from the cache. The typical pattern is cache-aside or read-through, where the database is the authoritative store and the cache only accelerates reads.
- ✗
Caching automatically synchronizes data between multiple database replicas.
Why it's wrong here
Database replication is a distinct mechanism where primary and replica databases maintain consistent copies of the same dataset through log shipping or replication protocols. A cache does not synchronize data between replicas; it holds denormalized or aggregated results that may be stale and are populated from queries to the primary database. Treating a cache as a replication tool confuses read optimizations that tolerate eventual consistency with the strong replication guarantees needed to keep database nodes in sync.
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Related to this question
Learn chapter
Storage on Google Cloud
Key term
SQL
SQL is a standardized programming language used to manage and manipulate relational databases, enabling querying, updating, and data retrieval.
Key term
Relational database
A relational database organizes data into tables with rows and columns, where each table relates to others using unique keys, allowing efficient storage, retrieval, and manipulation of structured information.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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