Amazon AWS SAA-C03 Database Performance, Access Patterns, Read Scaling, Proxies, and Caching Practice Test 2

 

Topic 14 Practice Test 2 covers Database Performance, Access Patterns, Read Scaling, Proxies, and Caching for AWS SAA-C03. For broader exam preparation, review the AWS SAA-C03 Exam Dumps. Every option includes focused technical reasoning explaining both the AWS architecture concept and its fit to the scenario.

Question 1

A second design review identifies this condition: an application team wants one stable hostname for read-only traffic even as Aurora Replicas are added or removed Which option should be chosen to decouple read clients from individual Aurora replica endpoints?

1. DynamoDB strongly consistent read
2. ElastiCache for Memcached
3. Aurora reader endpoint
4. Aurora Replica

Correct Answer(s)

 

C

Rationale

1. DynamoDB strongly consistent read a read must reflect the latest. Its role covers returns the most up-to-date successful write for. Here, an application team wants one stable hostname for read-only traffic even as Aurora Replicas are added or removed. The architecture must decouple read clients from individual Aurora replica endpoints; Aurora reader endpoint supplies that function.
2. ElastiCache for Memcached a stateless cache needs straightforward scale-out. Normally for provides a simple distributed in-memory object cache. The scenario has an application team wants one stable hostname for read-only traffic even as Aurora Replicas are added or removed. Because the solution must decouple read clients from individual Aurora replica endpoints, Aurora reader endpoint fits better.
3. Aurora reader endpoint load balances read-only connections across available Aurora. Here, an application team wants one stable hostname for read-only traffic even as Aurora Replicas are added or removed. Appropriate for applications need horizontal read scaling without. The team must decouple read clients from individual Aurora replica endpoints; Aurora reader endpoint supplies the mechanism.
4. Aurora Replica an Aurora workload needs additional read. It fits provides an asynchronously updated read instance that. Here, an application team wants one stable hostname for read-only traffic even as Aurora Replicas are added or removed. The team must decouple read clients from individual Aurora replica endpoints; that points to Aurora reader endpoint.

 

Question 2

During implementation, a production cluster needs another instance that can serve reads and also be promoted during an instance failure What is the best way to add a read-scaling instance that participates in Aurora failover?

1. Aurora Serverless v2
2. DynamoDB DAX
3. RDS read replica
4. Aurora Replica

Correct Answer(s)

 

D

Rationale

1. Aurora Serverless v2 Aurora workload demand is highly variable. It addresses automatically adjusts Aurora database compute capacity within. The review finds a production cluster needs another instance that can serve reads and also be promoted during an instance failure. Implementation must add a read-scaling instance that participates in Aurora failover, so Aurora Replica is aligned.
2. DynamoDB DAX a DynamoDB application needs microsecond read. Suited to is an in-memory cache purpose-built for DynamoDB. The key fact is a production cluster needs another instance that can serve reads and also be promoted during an instance failure. The team must add a read-scaling instance that participates in Aurora failover; Aurora Replica supports that decision.
3. RDS read replica read-heavy workloads should be offloaded from. It helps with creates an asynchronously replicated read-only database instance. Here, a production cluster needs another instance that can serve reads and also be promoted during an instance failure. The required result is to add a read-scaling instance that participates in Aurora failover, making Aurora Replica stronger.
4. Aurora Replica provides an asynchronously updated read instance that. The case has a production cluster needs another instance that can serve reads and also be promoted during an instance failure. It suits an Aurora workload needs additional read. The decision is to add a read-scaling instance that participates in Aurora failover, which Aurora Replica provides.

 

Question 3

An architecture assessment finds that a global reporting application needs a separate regional read copy and does not require synchronous consistency Which choice most directly allows the system to scale read traffic with an RDS replica?

1. RDS Proxy
2. RDS Performance Insights/Database Insights
3. DynamoDB partition key
4. RDS read replica

Correct Answer(s)

 

D

Rationale

1. RDS Proxy bursty serverless or container workloads create. Its value covers pools and shares database connections so applications. Here, a global reporting application needs a separate regional read copy and does not require synchronous consistency. The solution must scale read traffic with an RDS replica; RDS read replica matches the need.
2. RDS Performance Insights/Database Insights operators need evidence about SQL, waits,. Intended for surfaces database load and wait information to. Here, a global reporting application needs a separate regional read copy and does not require synchronous consistency. The team must scale read traffic with an RDS replica; RDS read replica provides that behavior.
3. DynamoDB partition key a DynamoDB table needs an access. Better for determines how items are distributed across partitions. The constraint is a global reporting application needs a separate regional read copy and does not require synchronous consistency. The design must scale read traffic with an RDS replica, which RDS read replica does.
4. RDS read replica creates an asynchronously replicated read-only database instance. In this scenario, a global reporting application needs a separate regional read copy and does not require synchronous consistency. It fits read-heavy workloads should be offloaded from. The design needs to scale read traffic with an RDS replica; RDS read replica is the direct match.

 

Question 4

Operations documents the following constraint: a microservice fleet frequently scales out and the database is spending excessive resources establishing sessions Which capability should the team use to insert a managed connection pool between clients and RDS?

1. DynamoDB sort key
2. RDS Proxy
3. Read-through cache pattern
4. RDS Proxy read-only endpoint

Correct Answer(s)

 

B

Rationale

1. DynamoDB sort key related items must be queried efficiently. It fits orders items sharing a partition key and. Here, a microservice fleet frequently scales out and the database is spending excessive resources establishing sessions. The team must insert a managed connection pool between clients and RDS; that points to RDS Proxy.
2. RDS Proxy pools and shares database connections so applications. The key condition is a microservice fleet frequently scales out and the database is spending excessive resources establishing sessions. It supports bursty serverless or container workloads create. The architecture must insert a managed connection pool between clients and RDS; RDS Proxy performs that role.
3. Read-through cache pattern applications repeatedly read data and can. Normally for checks the cache first and loads missing. The scenario has a microservice fleet frequently scales out and the database is spending excessive resources establishing sessions. Because the solution must insert a managed connection pool between clients and RDS, RDS Proxy fits better.
4. RDS Proxy read-only endpoint applications need connection pooling plus a. Useful for routes proxy client sessions toward read replicas. Here, a microservice fleet frequently scales out and the database is spending excessive resources establishing sessions. The design must insert a managed connection pool between clients and RDS; RDS Proxy, not RDS Proxy read-only endpoint, supplies that behavior.

 

Question 5

A solution review confirms that developers need separate proxy endpoints so transactions use the writer path and analytics use reader instances What should be implemented to provide distinct pooled endpoints for read and write traffic?

1. ElastiCache for Redis
2. TTL-based cache expiration
3. DynamoDB GSI
4. RDS Proxy read-only endpoint

Correct Answer(s)

 

D

Rationale

1. ElastiCache for Redis applications repeatedly request hot data that. Its role covers provides an in-memory key-value cache that can. Here, developers need separate proxy endpoints so transactions use the writer path and analytics use reader instances. The architecture must provide distinct pooled endpoints for read and write traffic; RDS Proxy read-only endpoint supplies that function.
2. TTL-based cache expiration the application can tolerate bounded staleness. Suited to automatically ages cached entries out after a. The key fact is developers need separate proxy endpoints so transactions use the writer path and analytics use reader instances. The team must provide distinct pooled endpoints for read and write traffic; RDS Proxy read-only endpoint supports that decision.
3. DynamoDB GSI queries need a key pattern not. It helps with provides an alternate partition/sort key access pattern. Here, developers need separate proxy endpoints so transactions use the writer path and analytics use reader instances. The required result is to provide distinct pooled endpoints for read and write traffic, making RDS Proxy read-only endpoint stronger.
4. RDS Proxy read-only endpoint routes proxy client sessions toward read replicas. Here, developers need separate proxy endpoints so transactions use the writer path and analytics use reader instances. Useful for applications need connection pooling plus a. The team must provide distinct pooled endpoints for read and write traffic, which RDS Proxy read-only endpoint supplies.

 

Question 6

The deployment team reports that session and leaderboard data needs fast in-memory access with richer data structures than simple relational queries Which option is most appropriate to use a managed Redis-compatible in-memory store?

1. ElastiCache for Memcached
2. ElastiCache for Redis
3. Database connection reuse
4. DynamoDB LSI

Correct Answer(s)

 

B

Rationale

1. ElastiCache for Memcached provides a simple distributed in-memory object cache. It addresses a stateless cache needs straightforward scale-out. The review finds session and leaderboard data needs fast in-memory access with richer data structures than simple relational queries. Implementation must use a managed Redis-compatible in-memory store, so ElastiCache for Redis is aligned.
2. ElastiCache for Redis provides an in-memory key-value cache that can. The review finds session and leaderboard data needs fast in-memory access with richer data structures than simple relational queries. Intended for applications repeatedly request hot data that. To satisfy the case, the system must use a managed Redis-compatible in-memory store.
3. Database connection reuse connection establishment is a significant part. Better for keeps or pools established sessions instead of. The constraint is session and leaderboard data needs fast in-memory access with richer data structures than simple relational queries. The design must use a managed Redis-compatible in-memory store, which ElastiCache for Redis does.
4. DynamoDB LSI queries need another ordering within the. Its value covers provides an alternate sort key while retaining. Here, session and leaderboard data needs fast in-memory access with richer data structures than simple relational queries. The solution must use a managed Redis-compatible in-memory store; ElastiCache for Redis matches the need.

 

Question 7

A technical review focuses on this requirement: an application needs a horizontally sharded cache and does not require replication, persistence, or complex data types Which mechanism best enables the team to select a lightweight managed cache engine?

1. DynamoDB DAX
2. DynamoDB eventually consistent read
3. ElastiCache for Memcached
4. Query-specific index

Correct Answer(s)

 

C

Rationale

1. DynamoDB DAX is an in-memory cache purpose-built for DynamoDB. Intended for a DynamoDB application needs microsecond read. Here, an application needs a horizontally sharded cache and does not require replication, persistence, or complex data types. The team must select a lightweight managed cache engine; ElastiCache for Memcached provides that behavior.
2. DynamoDB eventually consistent read applications can tolerate brief staleness and. Useful for can reduce read cost and improve scalability. Here, an application needs a horizontally sharded cache and does not require replication, persistence, or complex data types. The design must select a lightweight managed cache engine; ElastiCache for Memcached, not DynamoDB eventually consistent read, supplies that behavior.
3. ElastiCache for Memcached provides a simple distributed in-memory object cache. Under this constraint, an application needs a horizontally sharded cache and does not require replication, persistence, or complex data types. It serves a stateless cache needs straightforward scale-out. The correct action is to select a lightweight managed cache engine, making ElastiCache for Memcached appropriate.
4. Query-specific index a relational workload is slow because. It fits adds an index aligned to selective predicates. Here, an application needs a horizontally sharded cache and does not require replication, persistence, or complex data types. The team must select a lightweight managed cache engine; that points to ElastiCache for Memcached.

 

Question 8

While refining the design, the team notes that developers want a cache with a DynamoDB-compatible API so they can reduce read latency without managing cache invalidation manually Which choice should it make to add transparent read caching for DynamoDB access?

1. DynamoDB strongly consistent read
2. DynamoDB partition key
3. DynamoDB DAX
4. Aurora reader endpoint

Correct Answer(s)

 

C

Rationale

1. DynamoDB strongly consistent read returns the most up-to-date successful write for. Its role covers a read must reflect the latest. Here, developers want a cache with a DynamoDB-compatible API so they can reduce read latency without managing cache invalidation manually. The architecture must add transparent read caching for DynamoDB access; DynamoDB DAX supplies that function.
2. DynamoDB partition key determines how items are distributed across partitions. Normally for a DynamoDB table needs an access. The scenario has developers want a cache with a DynamoDB-compatible API so they can reduce read latency without managing cache invalidation manually. Because the solution must add transparent read caching for DynamoDB access, DynamoDB DAX fits better.
3. DynamoDB DAX is an in-memory cache purpose-built for DynamoDB. The scenario shows developers want a cache with a DynamoDB-compatible API so they can reduce read latency without managing cache invalidation manually. It handles a DynamoDB application needs microsecond read. The solution must add transparent read caching for DynamoDB access; DynamoDB DAX supplies that function.
4. Aurora reader endpoint applications need horizontal read scaling without. It helps with load balances read-only connections across available Aurora. Here, developers want a cache with a DynamoDB-compatible API so they can reduce read latency without managing cache invalidation manually. The required result is to add transparent read caching for DynamoDB access, making DynamoDB DAX stronger.

 

Question 9

The current architecture has this requirement: millions of events arrive with timestamps but the current key groups all current traffic into one logical key Which solution best helps the team to redesign key distribution for parallel throughput?

1. DynamoDB partition key
2. DynamoDB sort key
3. Aurora Replica
4. Aurora Serverless v2

Correct Answer(s)

 

A

Rationale

1. DynamoDB partition key determines how items are distributed across partitions. In this architecture, millions of events arrive with timestamps but the current key groups all current traffic into one logical key. It addresses a DynamoDB table needs an access. Meeting the requirement means the team must redesign key distribution for parallel throughput.
2. DynamoDB sort key orders items sharing a partition key and. Suited to related items must be queried efficiently. The key fact is millions of events arrive with timestamps but the current key groups all current traffic into one logical key. The team must redesign key distribution for parallel throughput; DynamoDB partition key supports that decision.
3. Aurora Replica provides an asynchronously updated read instance that. Its value covers an Aurora workload needs additional read. Here, millions of events arrive with timestamps but the current key groups all current traffic into one logical key. The solution must redesign key distribution for parallel throughput; DynamoDB partition key matches the need.
4. Aurora Serverless v2 automatically adjusts Aurora database compute capacity within. It addresses Aurora workload demand is highly variable. The review finds millions of events arrive with timestamps but the current key groups all current traffic into one logical key. Implementation must redesign key distribution for parallel throughput, so DynamoDB partition key is aligned.

 

Question 10

A validation workshop establishes that a telemetry table must retrieve a device’s records between two timestamps without scanning the whole table What is the strongest choice to support efficient range queries inside one partition?

1. DynamoDB GSI
2. RDS Performance Insights/Database Insights
3. DynamoDB sort key
4. RDS read replica

Correct Answer(s)

 

C

Rationale

1. DynamoDB GSI provides an alternate partition/sort key access pattern. Better for queries need a key pattern not. The constraint is a telemetry table must retrieve a device’s records between two timestamps without scanning the whole table. The design must support efficient range queries inside one partition, which DynamoDB sort key does.
2. RDS Performance Insights/Database Insights surfaces database load and wait information to. Intended for operators need evidence about SQL, waits,. Here, a telemetry table must retrieve a device’s records between two timestamps without scanning the whole table. The team must support efficient range queries inside one partition; DynamoDB sort key provides that behavior.
3. DynamoDB sort key orders items sharing a partition key and. The deployment has a telemetry table must retrieve a device’s records between two timestamps without scanning the whole table. It fits related items must be queried efficiently. The design must support efficient range queries inside one partition, which DynamoDB sort key enables.
4. RDS read replica creates an asynchronously replicated read-only database instance. Useful for read-heavy workloads should be offloaded from. Here, a telemetry table must retrieve a device’s records between two timestamps without scanning the whole table. The design must support efficient range queries inside one partition; DynamoDB sort key, not RDS read replica, supplies that behavior.

 

Question 11

Engineers must account for the fact that a table’s primary key supports device lookups, while a new application must retrieve items by region and event type Which option best supports the need to create a secondary access path with different partitioning?

1. DynamoDB GSI
2. RDS Proxy
3. DynamoDB LSI
4. Read-through cache pattern

Correct Answer(s)

 

A

Rationale

1. DynamoDB GSI provides an alternate partition/sort key access pattern. Here, a table’s primary key supports device lookups, while a new application must retrieve items by region and event type. Appropriate for queries need a key pattern not. The team must create a secondary access path with different partitioning; DynamoDB GSI supplies the mechanism.
2. RDS Proxy pools and shares database connections so applications. Its role covers bursty serverless or container workloads create. Here, a table’s primary key supports device lookups, while a new application must retrieve items by region and event type. The architecture must create a secondary access path with different partitioning; DynamoDB GSI supplies that function.
3. DynamoDB LSI provides an alternate sort key while retaining. It fits queries need another ordering within the. Here, a table’s primary key supports device lookups, while a new application must retrieve items by region and event type. The team must create a secondary access path with different partitioning; that points to DynamoDB GSI.
4. Read-through cache pattern checks the cache first and loads missing. Normally for applications repeatedly read data and can. The scenario has a table’s primary key supports device lookups, while a new application must retrieve items by region and event type. Because the solution must create a secondary access path with different partitioning, DynamoDB GSI fits better.

 

Question 12

The solution owner confirms that the application needs two different range-query orderings for items belonging to the same account partition Which capability should be selected to use a secondary index that shares the base partition key?

1. RDS Proxy read-only endpoint
2. TTL-based cache expiration
3. DynamoDB eventually consistent read
4. DynamoDB LSI

Correct Answer(s)

 

D

Rationale

1. RDS Proxy read-only endpoint routes proxy client sessions toward read replicas. It addresses applications need connection pooling plus a. The review finds the application needs two different range-query orderings for items belonging to the same account partition. Implementation must use a secondary index that shares the base partition key, so DynamoDB LSI is aligned.
2. TTL-based cache expiration automatically ages cached entries out after a. Suited to the application can tolerate bounded staleness. The key fact is the application needs two different range-query orderings for items belonging to the same account partition. The team must use a secondary index that shares the base partition key; DynamoDB LSI supports that decision.
3. DynamoDB eventually consistent read can reduce read cost and improve scalability. It helps with applications can tolerate brief staleness and. Here, the application needs two different range-query orderings for items belonging to the same account partition. The required result is to use a secondary index that shares the base partition key, making DynamoDB LSI stronger.
4. DynamoDB LSI provides an alternate sort key while retaining. The case has the application needs two different range-query orderings for items belonging to the same account partition. It suits queries need another ordering within the. The decision is to use a secondary index that shares the base partition key, which DynamoDB LSI provides.

 

Question 13

A production-readiness review states that analytics dashboards refresh frequently but do not require every just-written item to appear immediately Which approach most directly enables the team to choose an eventually consistent DynamoDB read?

1. DynamoDB eventually consistent read
2. Database connection reuse
3. ElastiCache for Redis
4. DynamoDB strongly consistent read

Correct Answer(s)

 

A

Rationale

1. DynamoDB eventually consistent read can reduce read cost and improve scalability. In this scenario, analytics dashboards refresh frequently but do not require every just-written item to appear immediately. It fits applications can tolerate brief staleness and. The design needs to choose an eventually consistent DynamoDB read; DynamoDB eventually consistent read is the direct match.
2. Database connection reuse keeps or pools established sessions instead of. Better for connection establishment is a significant part. The constraint is analytics dashboards refresh frequently but do not require every just-written item to appear immediately. The design must choose an eventually consistent DynamoDB read, which DynamoDB eventually consistent read does.
3. ElastiCache for Redis provides an in-memory key-value cache that can. Intended for applications repeatedly request hot data that. Here, analytics dashboards refresh frequently but do not require every just-written item to appear immediately. The team must choose an eventually consistent DynamoDB read; DynamoDB eventually consistent read provides that behavior.
4. DynamoDB strongly consistent read a read must reflect the latest. Its value covers returns the most up-to-date successful write for. Here, analytics dashboards refresh frequently but do not require every just-written item to appear immediately. The solution must choose an eventually consistent DynamoDB read; DynamoDB eventually consistent read matches the need.

 

Question 14

The design is being adjusted because a workflow writes a coordination record and the next step cannot proceed using a stale version What should the architect use to use stronger read consistency for correctness?

1. ElastiCache for Memcached
2. DynamoDB strongly consistent read
3. Aurora Serverless v2
4. Query-specific index

Correct Answer(s)

 

B

Rationale

1. ElastiCache for Memcached a stateless cache needs straightforward scale-out. Normally for provides a simple distributed in-memory object cache. The scenario has a workflow writes a coordination record and the next step cannot proceed using a stale version. Because the solution must use stronger read consistency for correctness, DynamoDB strongly consistent read fits better.
2. DynamoDB strongly consistent read returns the most up-to-date successful write for. The key condition is a workflow writes a coordination record and the next step cannot proceed using a stale version. It supports a read must reflect the latest. The architecture must use stronger read consistency for correctness; DynamoDB strongly consistent read performs that role.
3. Aurora Serverless v2 Aurora workload demand is highly variable. Useful for automatically adjusts Aurora database compute capacity within. Here, a workflow writes a coordination record and the next step cannot proceed using a stale version. The design must use stronger read consistency for correctness; DynamoDB strongly consistent read, not Aurora Serverless v2, supplies that behavior.
4. Query-specific index adds an index aligned to selective predicates. It fits a relational workload is slow because. Here, a workflow writes a coordination record and the next step cannot proceed using a stale version. The team must use stronger read consistency for correctness; that points to DynamoDB strongly consistent read.

 

Question 15

A new constraint is documented: a development platform’s relational workload varies throughout the hour and the team wants capacity to adjust without selecting fixed instance sizes Which component best addresses the requirement to use elastic Aurora compute?

1. RDS Performance Insights/Database Insights
2. Aurora reader endpoint
3. Aurora Serverless v2
4. DynamoDB DAX

Correct Answer(s)

 

C

Rationale

1. RDS Performance Insights/Database Insights operators need evidence about SQL, waits,. Its role covers surfaces database load and wait information to. Here, a development platform’s relational workload varies throughout the hour and the team wants capacity to adjust without selecting fixed instance sizes. The architecture must use elastic Aurora compute; Aurora Serverless v2 supplies that function.
2. Aurora reader endpoint load balances read-only connections across available Aurora. It helps with applications need horizontal read scaling without. Here, a development platform’s relational workload varies throughout the hour and the team wants capacity to adjust without selecting fixed instance sizes. The required result is to use elastic Aurora compute, making Aurora Serverless v2 stronger.
3. Aurora Serverless v2 automatically adjusts Aurora database compute capacity within. Here, a development platform’s relational workload varies throughout the hour and the team wants capacity to adjust without selecting fixed instance sizes. Useful for Aurora workload demand is highly variable. The team must use elastic Aurora compute, which Aurora Serverless v2 supplies.
4. DynamoDB DAX a DynamoDB application needs microsecond read. Suited to is an in-memory cache purpose-built for DynamoDB. The key fact is a development platform’s relational workload varies throughout the hour and the team wants capacity to adjust without selecting fixed instance sizes. The team must use elastic Aurora compute; Aurora Serverless v2 supports that decision.

 

Question 16

The team is comparing alternatives after learning that cPU looks moderate but transaction response time is high; engineers need visibility into database wait states and top activity Which choice is best to diagnose the source of relational database contention?

1. Aurora Replica
2. RDS Performance Insights/Database Insights
3. DynamoDB partition key
4. Read-through cache pattern

Correct Answer(s)

 

B

Rationale

1. Aurora Replica an Aurora workload needs additional read. Its value covers provides an asynchronously updated read instance that. Here, cPU looks moderate but transaction response time is high; engineers need visibility into database wait states and top activity. The solution must diagnose the source of relational database contention; RDS Performance Insights/Database Insights matches the need.
2. RDS Performance Insights/Database Insights surfaces database load and wait information to. The review finds cPU looks moderate but transaction response time is high; engineers need visibility into database wait states and top activity. Intended for operators need evidence about SQL, waits,. To satisfy the case, the system must diagnose the source of relational database contention.
3. DynamoDB partition key a DynamoDB table needs an access. Better for determines how items are distributed across partitions. The constraint is cPU looks moderate but transaction response time is high; engineers need visibility into database wait states and top activity. The design must diagnose the source of relational database contention, which RDS Performance Insights/Database Insights does.
4. Read-through cache pattern applications repeatedly read data and can. It addresses checks the cache first and loads missing. The review finds cPU looks moderate but transaction response time is high; engineers need visibility into database wait states and top activity. Implementation must diagnose the source of relational database contention, so RDS Performance Insights/Database Insights is aligned.

 

Question 17

Architecture governance requires a response because product details are read far more often than changed and the application can populate the cache the first time each object is requested Which option should be used to reduce repeated database reads with lazy cache population?

1. TTL-based cache expiration
2. Read-through cache pattern
3. DynamoDB sort key
4. RDS read replica

Correct Answer(s)

 

B

Rationale

1. TTL-based cache expiration the application can tolerate bounded staleness. Intended for automatically ages cached entries out after a. Here, product details are read far more often than changed and the application can populate the cache the first time each object is requested. The team must reduce repeated database reads with lazy cache population; Read-through cache pattern provides that behavior.
2. Read-through cache pattern checks the cache first and loads missing. Under this constraint, product details are read far more often than changed and the application can populate the cache the first time each object is requested. It serves applications repeatedly read data and can. The correct action is to reduce repeated database reads with lazy cache population, making Read-through cache pattern appropriate.
3. DynamoDB sort key related items must be queried efficiently. It fits orders items sharing a partition key and. Here, product details are read far more often than changed and the application can populate the cache the first time each object is requested. The team must reduce repeated database reads with lazy cache population; that points to Read-through cache pattern.
4. RDS read replica read-heavy workloads should be offloaded from. Useful for creates an asynchronously replicated read-only database instance. Here, product details are read far more often than changed and the application can populate the cache the first time each object is requested. The design must reduce repeated database reads with lazy cache population; Read-through cache pattern, not RDS read replica, supplies that behavior.

 

Question 18

The implementation plan must account for this condition: a dashboard cache may serve slightly stale aggregates but must not retain them indefinitely What is the most direct way to expire cached values predictably?

1. Database connection reuse
2. RDS Proxy
3. DynamoDB GSI
4. TTL-based cache expiration

Correct Answer(s)

 

D

Rationale

1. Database connection reuse connection establishment is a significant part. Normally for keeps or pools established sessions instead of. The scenario has a dashboard cache may serve slightly stale aggregates but must not retain them indefinitely. Because the solution must expire cached values predictably, TTL-based cache expiration fits better.
2. RDS Proxy bursty serverless or container workloads create. Its role covers pools and shares database connections so applications. Here, a dashboard cache may serve slightly stale aggregates but must not retain them indefinitely. The architecture must expire cached values predictably; TTL-based cache expiration supplies that function.
3. DynamoDB GSI queries need a key pattern not. It helps with provides an alternate partition/sort key access pattern. Here, a dashboard cache may serve slightly stale aggregates but must not retain them indefinitely. The required result is to expire cached values predictably, making TTL-based cache expiration stronger.
4. TTL-based cache expiration automatically ages cached entries out after a. The scenario shows a dashboard cache may serve slightly stale aggregates but must not retain them indefinitely. It handles the application can tolerate bounded staleness. The solution must expire cached values predictably; TTL-based cache expiration supplies that function.

 

Question 19

A design decision is needed because the database reaches connection limits even though queries are brief because the application creates many concurrent sessions Which capability best allows the team to reuse or pool database sessions?

1. Database connection reuse
2. DynamoDB LSI
3. RDS Proxy read-only endpoint
4. Query-specific index

Correct Answer(s)

 

A

Rationale

1. Database connection reuse keeps or pools established sessions instead of. In this architecture, the database reaches connection limits even though queries are brief because the application creates many concurrent sessions. It addresses connection establishment is a significant part. Meeting the requirement means the team must reuse or pool database sessions.
2. DynamoDB LSI queries need another ordering within the. Its value covers provides an alternate sort key while retaining. Here, the database reaches connection limits even though queries are brief because the application creates many concurrent sessions. The solution must reuse or pool database sessions; Database connection reuse matches the need.
3. RDS Proxy read-only endpoint applications need connection pooling plus a. It addresses routes proxy client sessions toward read replicas. The review finds the database reaches connection limits even though queries are brief because the application creates many concurrent sessions. Implementation must reuse or pool database sessions, so Database connection reuse is aligned.
4. Query-specific index a relational workload is slow because. Suited to adds an index aligned to selective predicates. The key fact is the database reaches connection limits even though queries are brief because the application creates many concurrent sessions. The team must reuse or pool database sessions; Database connection reuse supports that decision.

 

Question 20

The team must revise the solution after confirming that one endpoint is slow while CPU and memory are otherwise healthy, and its execution plan shows a full table scan on a selective condition Which option best enables it to tune relational indexing for the actual query pattern?

1. Query-specific index
2. Aurora reader endpoint
3. ElastiCache for Redis
4. DynamoDB eventually consistent read

Correct Answer(s)

 

A

Rationale

1. Query-specific index adds an index aligned to selective predicates. The deployment has one endpoint is slow while CPU and memory are otherwise healthy, and its execution plan shows a full table scan on a selective condition. It fits a relational workload is slow because. The design must tune relational indexing for the actual query pattern, which Query-specific index enables.
2. Aurora reader endpoint applications need horizontal read scaling without. Better for load balances read-only connections across available Aurora. The constraint is one endpoint is slow while CPU and memory are otherwise healthy, and its execution plan shows a full table scan on a selective condition. The design must tune relational indexing for the actual query pattern, which Query-specific index does.
3. ElastiCache for Redis applications repeatedly request hot data that. Intended for provides an in-memory key-value cache that can. Here, one endpoint is slow while CPU and memory are otherwise healthy, and its execution plan shows a full table scan on a selective condition. The team must tune relational indexing for the actual query pattern; Query-specific index provides that behavior.
4. DynamoDB eventually consistent read applications can tolerate brief staleness and. Useful for can reduce read cost and improve scalability. Here, one endpoint is slow while CPU and memory are otherwise healthy, and its execution plan shows a full table scan on a selective condition. The design must tune relational indexing for the actual query pattern; Query-specific index, not DynamoDB eventually consistent read, supplies that behavior.

• < Amazon AWS SAA-C03 Database Performance, Access Patterns, Read Scaling, Proxies, and Caching Practice Test 1
• Amazon AWS SAA-C03 High-Performance Networking, Load Balancing, CloudFront, Global Accelerator, and Private Connectivity Practice Test 1 >