Amazon AWS Certified Solutions Architect - Associate SAA-C03 Practice Test 54

Question 530

An RDS for PostgreSQL primary serves OLTP writes and expensive nightly SELECT reports; management will accept asynchronously lagging report data but not reduced write throughput. Which design isolates the reporting queries?

Use a synchronous Multi-AZ DB-instance standby as the report destination, with the primary handling writes.
Create a suitable RDS read replica and direct the reporting connection to the replica endpoint.
Schedule the heavy SELECT reports during peak OLTP hours on the primary and raise its automatic backup retention.
Create additional RDS read replicas but keep the reporting application configured with the primary endpoint.

Explanation

Correct Answer

Read replicas offload read-only requests using asynchronous replication while the source remains authoritative for writes.

Incorrect Answers

A traditional Multi-AZ DB-instance standby is maintained for failover and is not a connection endpoint for read-only SQL.

Backup retention has no effect on query isolation; retaining reports on the primary still competes with OLTP CPU and I/O.

Adding replicas does not redirect client connections; read traffic remains on the primary until the application uses a replica endpoint.

Question 531

An application writes to an RDS Multi-AZ DB instance deployment with a single standby. A business analyst asks to issue SQL queries against the standby to double read throughput. Which guidance is correct?

Keep the standby for automated failover and use a separately configured read replica or suitable alternative for read scaling.
Treat the existing synchronous standby in the Multi-AZ DB-instance deployment as a reader endpoint for reports.
Enable backup retention on the same Multi-AZ DB instance and direct the read workload to the backup storage.
Raise the Multi-AZ DB-instance standby capacity and assume primary SELECT sessions are balanced to it without changing endpoints.

Explanation

Correct Answer

A traditional RDS Multi-AZ DB instance standby is not a general read-query endpoint, unlike Multi-AZ DB clusters with readable standbys.

Incorrect Answers

The traditional single-standby configuration does not accept ordinary application SELECT connections on its standby.

Automated backups are recovery artifacts rather than queryable live database read replicas.

Increasing a standby class does not change the single-standby configuration into a readable Multi-AZ DB cluster or separate read replica.

Question 532

An online retail table receives unpredictable DynamoDB read/write spikes during flash promotions. Operators cannot reliably forecast capacity and want requests billed by throughput without selecting RCU/WCU units. Which capacity mode?

Use provisioned DynamoDB capacity with a permanently configured RCU/WCU ceiling based on the quietest month.
Add DynamoDB auto scaling to a minimum fixed provisioned target but expect every burst to pass without adjustment delay.
Use DynamoDB on-demand capacity and evaluate the documented peak and scaling considerations.
Run DynamoDB through a DAX cache and assume repeated writes are billed only as cache hits.

Explanation

Correct Answer

On-demand mode abstracts provisioned RCUs/WCUs and charges for consumed requests, with throughput scaling limits to assess.

Incorrect Answers

A static low throughput setting is vulnerable to flash-sale throttling, and its reserved capacity model does not match requested per-request billing.

Provisioned target tracking may react to utilization but is not instantaneous unlimited scaling; it also retains provisioned-capacity billing.

DAX accelerates supported cached reads; it does not turn write requests into a per-request on-demand capacity mode.

Question 533

A DynamoDB application runs at a predictable baseline all month with moderate daily peaks and wants controlled RCU/WCU provisioning with automatic adjustments. Which model fits?

Select provisioned capacity without any auto scaling policy, and provision only the overnight low point.
Choose on-demand request billing solely because the current baseline is constant and inexpensive.
Use provisioned capacity with DynamoDB auto scaling policies and monitored target utilization.
Increase DynamoDB table storage class to Standard-IA and assume storage class changes automatically change RCUs/WCUs.

Explanation

Correct Answer

Provisioned mode supports RCU/WCU controls; Application Auto Scaling can adapt targets to load while baseline is predictable.

Incorrect Answers

Fixed capacity sized for the minimum cannot automatically follow planned daily peaks, defeating the operational-adjustment requirement.

On-demand can work, but for sustained predictable load the business should assess properly utilized provisioned capacity and autoscaling economics.

Table class controls pricing for storage and throughput but does not provide a target-tracking provisioned-capacity scaling policy.

Question 534

A DynamoDB orders table has a partition key fixed to REGION#us-east-1 for most traffic. Its aggregate provisioned capacity is ample, yet requests for that key throttle. Which design correction is appropriate?

Raise only the table-level RCU budget without changing the low-cardinality partition-key design.
Create a local secondary index under the same REGION#us-east-1 partition key and expect it to distribute request load.
Enable strongly consistent reads for every request to make the hot partition able to serve twice as much traffic.
Distribute high-volume items across partition keys with sufficient cardinality, potentially by write sharding and an adjusted query design.

Explanation

Correct Answer

Hot logical keys can concentrate operations on partition throughput despite unused capacity elsewhere.

Incorrect Answers

Concentrating traffic on one hot key can exhaust per-partition serving limits even if aggregate table throughput rises.

An LSI preserves the base-table partition key, so it does not resolve the hot-key traffic concentration.

Strong consistency consumes more read capacity per item and does not increase a hot partition’s physical throughput ceiling.

Question 535

An orders DynamoDB table is keyed by orderId. A new endpoint needs efficient Query by customerId and createdAt without scanning all orders. What schema element is appropriate?

Add a global secondary index keyed by customerId with a suitable createdAt sort key and required projections.
Add a local secondary index keyed by customerId instead of orderId while keeping the existing base partition key unchanged.
Run Scan with a customerId FilterExpression on the entire orders table and assume the filter lowers the RCUs needed.
Add a DAX cache for orderId lookups and expect the cache to implement a new Query access pattern on customerId.

Explanation

Correct Answer

A GSI defines a different partition key for the access pattern, supporting efficient indexed queries without base-table scans.

Incorrect Answers

An LSI must reuse the table partition key orderId; it cannot provide a different customerId partition key access pattern.

A Scan can read most table items before applying its filter; it is not an efficient partition-key-based Query path.

DAX caches supported operations but does not create missing index keys for customerId/date queries.

Question 536

A DynamoDB table must support a second sort order under the existing partition key and developers want strongly consistent index reads. The table is being created now. Which index can fit?

Create a global secondary index with a different partition key and request ConsistentRead=true on that index.
Create a local secondary index at table creation with the same partition key and alternate sort key, after assessing the 10 GiB item-collection limit.
Wait until after the table has been populated to create an LSI with a new partition key.
Use an ordinary table Scan with an alternate sort key and claim that scanning provides indexed strongly consistent ordering.

Explanation

Correct Answer

An LSI retains the base partition key and can support strong consistency but carries creation-time and item-collection constraints.

Incorrect Answers

GSIs have their own partition keys but only eventually consistent reads; they cannot meet the specified strong-read requirement.

A local secondary index must share the base partition key and must be defined at table creation.

Strong reads on a table are possible, but a Scan does not create the requested indexed ordering or efficient access path.

Question 537

A DynamoDB-backed catalog needs microsecond latency for repeated product lookups and can tolerate eventually consistent values. Select TWO design checks that together justify using DynamoDB Accelerator (DAX) without breaking application expectations.

Use DAX for supported, repeatedly requested eventually consistent DynamoDB reads.
Require read-after-write strong consistency for every cached key and assume DAX will still return in-memory responses.
Cache only write operations in DAX and treat it as a write-back database without DynamoDB persistence.
Confirm that cached product lookups tolerate short-lived staleness, including when another client writes directly to DynamoDB rather than through DAX.
Use a GSI with ConsistentRead=true to make DAX cache entries strongly consistent across writes.

Explanation

Correct Answer

DAX is a managed in-memory cache designed to accelerate high-hit-rate eventually consistent reads.

A direct DynamoDB write that bypasses DAX does not instantly invalidate a DAX item or query-cache entry. Applications with a strict read-after-write requirement must use a strongly consistent DynamoDB path, so compatibility with cache staleness must be checked separately from having a high cache-hit workload.

Incorrect Answers

DAX cannot serve strongly consistent reads from cache; such requests must go to DynamoDB.

DAX is an in-memory cache for DynamoDB reads, not a standalone write-back persistence engine replacing DynamoDB.

DynamoDB GSIs do not support strong consistency, and enabling that parameter cannot confer strong read semantics on DAX.

Question 538

A shopping-cart service repeatedly reads the same relational product records; query capacity rather than write throughput is exhausted. It can tolerate short-lived cached values. Which architecture reduces database reads?

Use read replicas but leave the application on the RDS primary endpoint and assume traffic transparently shifts.
Add a Redis cluster but continue to query the relational database before every cache lookup.
Increase the relational database instance class and disable TTL/invalidation for all product records.
Use Amazon ElastiCache with cache-aside reads and defined TTL and invalidation on updates.

Explanation

Correct Answer

An application-managed cache offloads repeated hot-item reads, provided cache freshness and miss behavior are defined.

Incorrect Answers

A replica can offload reads only if the application directs the eligible queries to its endpoint.

The cache must be consulted first for reusable entries; otherwise the database still pays the full read cost.

A larger instance may defer load issues, but it does not exploit repeated reads, and unbounded cache freshness is unsafe for changing product data.

Question 539

An ElastiCache Redis deployment has a hard memory budget, rapidly changing keys and noncritical cached values that may be evicted. Which operational setting is central?

Keep the noeviction policy even when memory fills and assume Redis will discard expired least-recent keys anyway.
Choose an appropriate Redis maxmemory eviction policy and monitor evictions/hit ratio against application tolerance.
Increase Redis node CPU without evaluating cache-key memory usage or permissible eviction behavior.
Set a TTL on every cache key and assume expiry timestamps alone guarantee that Redis never rejects writes under a full maxmemory limit.

Explanation

Correct Answer

When memory fills, Redis eviction rules decide which cached entries may be discarded; TTL alone is not the universal capacity policy.

Incorrect Answers

A noeviction configuration rejects writes once at the memory limit rather than evicting arbitrary cache keys.

CPU scaling alone does not address the defined memory capacity and eviction policy at maxmemory.

TTL only removes a key when expiration processing occurs; a cache may fill before keys expire. The configured maxmemory-policy determines whether Redis evicts eligible entries or reports an out-of-memory write failure at its limit.

Question 540

Thousands of short-lived Lambda invocations each open a new TCP connection to an RDS MySQL database, exhausting backend connections. Which managed service can multiplex connections?

Increase the maximum allowable RDS database sessions but leave every Lambda invocation opening a new unpooled connection.
Add an RDS read replica and direct all write transactions to its read-only endpoint.
Enable Multi-AZ DB-instance failover and assume its synchronous standby multiplexes the primary’s client connections.
Place Amazon RDS Proxy between the Lambda clients and the database, then monitor connection pooling and avoid SQL session-pinning patterns that defeat reuse.

Explanation

Correct Answer

RDS Proxy can pool and multiplex backend MySQL database connections across many short-lived Lambda clients, reducing connection setup and memory pressure. Certain session state changes or long SQL statements can pin client sessions and reduce multiplexing, so monitor pinned sessions and keep appropriate authentication and pool settings.

Incorrect Answers

A larger limit may defer saturation but does not pool, reuse or multiplex the Lambda sessions that cause the connection explosion.

An RDS read replica does not accept normal write workload or pool thousands of short-lived MySQL client sessions.

The standby is for availability rather than client-side connection multiplexing; it does not solve primary-session churn.

Pass Your Amazon AWS Certified Solutions Architect - Associate SAA-C03 Exam Easy!

Question 530

Explanation

Correct Answer

Incorrect Answers

Question 531

Explanation

Correct Answer

Incorrect Answers

Question 532

Explanation

Correct Answer

Incorrect Answers

Question 533

Explanation

Correct Answer

Incorrect Answers

Question 534

Explanation

Correct Answer

Incorrect Answers

Question 535

Explanation

Correct Answer

Incorrect Answers

Question 536

Explanation

Correct Answer

Incorrect Answers

Question 537

Explanation

Correct Answer

Incorrect Answers

Question 538

Explanation

Correct Answer

Incorrect Answers

Question 539

Explanation

Correct Answer

Incorrect Answers

Question 540

Explanation

Correct Answer

Incorrect Answers

AWS Certified Solutions Architect - Associate SAA-C03 Practice Tests