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Question 1Question →

Match each Google Cloud database or persistent storage option to the enterprise workload requirement that best aligns with its architectural characteristics and performance capabilities.

Click a left item, then click its matching right item

Items

Cloud Spanner
Cloud Bigtable
Local SSD
Cloud SQL

Matches

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Answer

Cloud Spanner matches globally distributed relational ACID workloads; Cloud Bigtable matches low-latency high-throughput NoSQL analytics; Local SSD matches host-attached ephemeral scratch storage; Cloud SQL matches managed regional relational DB engines.
Each GCP storage and database service addresses distinct architectural requirements across scalability, relational model support, access latency, and data persistence guarantees.

Step-by-Step Solution

1
Analyze global multi-region relational requirements.
Identify Cloud Spanner as the sole option supporting horizontal scaling with multi-region relational ACID transactions.
Standard regional engines cannot scale horizontally across regions while preserving synchronous relational transactions.
2
Evaluate high-throughput NoSQL time-series requirements.
Identify Cloud Bigtable for key-value wide-column workloads needing single-digit millisecond latency.
Cloud Bigtable provides seamless scaling for heavy streaming reads and writes.
3
Assess host-attached block storage performance and persistence.
Identify Local SSD as ephemeral block storage physically linked to the hypervisor host.
Local SSDs deliver highest IOPS and lowest latency but lose data if the instance stops.
4
Identify regional managed relational database solutions.
Match Cloud SQL to regional transactional workloads running MySQL, PostgreSQL, or SQL Server engines.
Cloud SQL simplifies operational overhead for standard relational database engines within a region.

Key Concept

Planning Database Options and Persistent Disk Types based on scale, transaction model, latency, and data persistence.
Question 2Question →

Match each Google Cloud storage option or persistent disk type to its primary architectural capability and ideal workload scenario.

Click a left item, then click its matching right item

Items

Cloud Spanner
Cloud Bigtable
Local SSD
Balanced Persistent Disk (pd-balanced)

Matches

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Answer

Cloud Spanner pairs with the global relational database capability; Cloud Bigtable pairs with the petabyte-scale NoSQL capability; Local SSD pairs with the high-performance host-attached ephemeral block storage; and Balanced Persistent Disk pairs with the cost-effective network block storage option.
Each service targets distinct operational constraints: Cloud Spanner supports globally scalable relational transactions; Cloud Bigtable handles massive NoSQL telemetry and time-series throughput; Local SSD provides maximum IOPS with host-bound ephemerality; and Balanced Persistent Disk offers durable network block storage optimized for cost-performance balance.

Step-by-Step Solution

1
Identify relational database requirements requiring global consistency
Matched Cloud Spanner to global relational database with strong ACID consistency.
Cloud Spanner is uniquely engineered to scale relationally across regions without sacrificing ACID consistency.
2
Identify massive-scale NoSQL key-value and time-series requirements
Matched Cloud Bigtable to petabyte-scale NoSQL low-latency workloads.
Cloud Bigtable handles high-throughput key-value write/read operations required by time-series data.
3
Differentiate persistent network block storage from ephemeral host-attached block storage
Matched Local SSD to non-persistent host-attached storage and pd-balanced to persistent network storage.
Local SSD is physically bound to the host hypervisor and loses data on VM stop, whereas persistent disks are network-attached and retain data.

Key Concept

GCP Database & Block Storage Selection Criteria
Estimated Time:1m 30s
Question 3Question →

Match each Google Cloud storage or database requirement to its appropriate Google Cloud service or disk type.

Click a left item, then click its matching right item

Items

High-performance, ephemeral block storage physically attached to the VM host server for microsecond latency requirements
Fully managed relational database providing global scale, multi-region consistency, and ACID transactions
High-throughput NoSQL wide-column database optimized for large-scale analytical and real-time key-value data
Standard durable network-attached block storage suitable for low-cost sequential workload data volumes

Matches

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Answer

Local SSD matches high-performance ephemeral block storage; Cloud Spanner matches global multi-region relational ACID requirements; Cloud Bigtable matches high-throughput NoSQL key-value data; Standard Persistent Disk (pd-standard) matches low-cost sequential block storage.
Each GCP database service and block storage option caters to distinct latency, persistence, scalability, and consistency models: Local SSD provides maximum IOPS with ephemeral host-attached disk; Cloud Spanner delivers globally scalable relational transactions; Cloud Bigtable supports ultra-high-throughput NoSQL operations; and Standard Persistent Disk provides low-cost network-attached HDD block storage.

Step-by-Step Solution

1
Analyze storage performance and lifespan requirements
Identify that physically attached disk storage with microsecond latency corresponds to Local SSD.
Local SSD provides the highest IOPS and lowest latency by residing on the physical host, though data does not persist past instance termination.
2
Evaluate relational vs NoSQL database architecture limits
Map global multi-region relational ACID needs to Cloud Spanner, and high-throughput key-value/wide-column data to Cloud Bigtable.
Cloud Spanner is designed for enterprise global relational workloads, while Cloud Bigtable handles non-relational heavy write/read throughput.
3
Identify block storage persistent disk performance tier
Map cost-effective HDD sequential access requirements to Standard Persistent Disk (pd-standard).
Standard Persistent Disk relies on HDDs providing cost-effective network storage for sequential access scenarios.

Key Concept

Selecting GCP Database and Storage Products Based on Workload Requirements
Question 4Question →

For a multi-tier enterprise architecture migration to Google Cloud, evaluate the data access patterns and performance requirements of each workload component below. Match each requirement to the most appropriate and cost-effective Google Cloud database service or block storage configuration.

Click a left item, then click its matching right item

Items

A mobile client backend needing scalable hierarchical document storage, real-time live synchronization, offline data handling, and ACID transaction support across multiple collections.
An IoT streaming engine requiring single-digit millisecond latency for massive throughput writes and key-range scans across petabytes of time-series metric data.
A batch log aggregation VM performing high-volume sequential disk write operations where cost per gigabyte and sustained sequential throughput are prioritized over high random IOPS.
A cluster of web server Compute Engine VMs requiring high-performance shared block storage mounted simultaneously to all instances in read-only mode to access static application binary data.

Matches

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Answer

The correct pairings match mobile document sync requirements to Firestore in Native mode, high-throughput IoT time-series data to Cloud Bigtable, sequential batch logging to Standard Persistent Disk (pd-standard), and multi-instance shared read-only block storage to SSD Persistent Disk (pd-ssd) in ReadOnly attachment mode.
Firestore in Native mode provides flexible, hierarchical document storage with built-in client SDK offline sync and multi-document transactions. Cloud Bigtable delivers ultra-low latency write throughput for petabyte-scale time-series and IoT metrics. Standard Persistent Disk (pd-standard) minimizes costs for sequential disk operations like log aggregation. SSD Persistent Disk (pd-ssd) supports concurrent multi-VM attachment when accessed strictly in ReadOnly mode.

Step-by-Step Solution

1
Analyze document model, mobile client sync, offline support, and multi-collection ACID requirement.
Identifies Firestore in Native mode as the tailored database option for mobile backends requiring document synchronization.
Cloud SQL and Cloud Spanner are relational DBs, while Bigtable lacks mobile client synchronization SDKs and offline capabilities.
2
Analyze high-throughput, low-latency, petabyte-scale write and key-scan pattern for IoT metrics.
Identifies Cloud Bigtable as the optimal wide-column NoSQL service.
Cloud Bigtable scales seamlessly to petabytes and is specifically optimized for high-write time-series and key-range queries.
3
Analyze sequential write workload prioritized by cost per GB over random IOPS.
Identifies Standard Persistent Disk (pd-standard) as the correct block storage type.
HDD-backed standard disks offer the lowest cost per GB for sequential access workloads such as batch log ingestion.
4
Analyze requirement for high-performance block storage attached to multiple VMs simultaneously.
Identifies SSD Persistent Disk (pd-ssd) configured in ReadOnly mode.
Persistent Disks can be attached to multiple Compute Engine instances simultaneously only when attached in ReadOnly mode.

Key Concept

Selecting GCP Database and Persistent Disk types based on access patterns, scaling needs, latency SLA, and multi-attach capabilities.
Estimated Time:2m 0s
Question 5Question →

Match each Google Cloud enterprise workload requirement on the left with the database or persistent block storage service on the right that best satisfies the technical constraints with minimal operational overhead.

Click a left item, then click its matching right item

Items

A high-throughput, low-latency NoSQL data store requiring single-digit millisecond latency for real-time time-series IoT ingestion at petabyte scale.
A globally distributed transactional relational database requiring full ACID compliance, horizontal scaling across multi-region deployments, and up to 99.999% availability.
An ephemeral, ultra-high IOPS storage volume providing sub-millisecond latency attached physically to the hypervisor host for temporary processing and scratch space.
A fully managed regional relational database engine (MySQL, PostgreSQL, or SQL Server) supporting standard OLTP applications with automated backups and cross-zone High Availability (HA).

Matches

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Answer

High-throughput petabyte IoT time-series maps to Cloud Bigtable. Globally distributed ACID relational database maps to Cloud Spanner. Ephemeral ultra-high IOPS sub-millisecond scratch space maps to Local SSD. Managed regional MySQL/PostgreSQL with HA maps to Cloud SQL.
Each requirement directly aligns with GCP's database and storage classification guidelines: Cloud Bigtable for high-throughput NoSQL time-series; Cloud Spanner for globally scalable ACID relational data; Local SSD for non-persistent, physically attached high-IOPS scratch disks; and Cloud SQL for standard managed regional relational databases.

Step-by-Step Solution

1
Analyze the scale, latency, and data structure requirements for the IoT time-series workload.
Identified wide-column NoSQL at petabyte scale with single-digit millisecond latency requirement.
Cloud Bigtable is the optimal GCP native service designed specifically for high-volume NoSQL and time-series streaming workloads.
2
Evaluate the database needs for global multi-region transactional consistency.
Identified global ACID compliance and multi-region horizontal scaling.
Cloud Spanner uniquely combines relational database ACID semantics with horizontal global scaling and 99.999% availability.
3
Differentiate ephemeral scratch storage performance requirements from persistent disks.
Identified host-attached temporary storage with sub-millisecond latency.
Local SSD provides the required IOPS and sub-millisecond latency for ephemeral scratch storage.
4
Determine the service for regional standard relational database engines.
Identified managed regional MySQL/PostgreSQL/SQL Server with automatic failover.
Cloud SQL manages standard relational database engines regionally with built-in automated backups and HA.

Key Concept

Matching workload storage and relational requirements to appropriate Google Cloud database engines and persistent disk options.
Estimated Time:2m 0s
Question 6Question →

Match each Google Cloud database or persistent block storage service on the left with the workload architecture requirement on the right that it best satisfies.

Click a left item, then click its matching right item

Items

Cloud Spanner
Cloud Bigtable
Cloud SQL
Extreme Persistent Disk (pd-extreme)

Matches

Show answer & explanation

Answer

Cloud Spanner pairs with global multi-region relational ACID requirements; Cloud Bigtable pairs with high-throughput NoSQL time-series data; Cloud SQL pairs with managed regional relational SQL engines; Extreme Persistent Disk pairs with provisioned high-IOPS block storage for Compute Engine.
Each Google Cloud storage and database technology fulfills specific operational and architectural constraints: Cloud Spanner supports global multi-region relational consistency with horizontal scaling; Cloud Bigtable provides high-throughput NoSQL capabilities for heavy time-series analytics; Cloud SQL provides standard managed relational databases with regional high availability; and Extreme Persistent Disk (pd-extreme) supplies high-provisioned IOPS block storage directly to Compute Engine VMs.

Step-by-Step Solution

1
Evaluate multi-region relational transactional requirements
Match Cloud Spanner with global relational ACID consistency and horizontal scaling.
Cloud Spanner is the only fully managed GCP database that provides horizontal scaling combined with global multi-region ACID transactions.
2
Identify high-throughput analytics and ingestion requirements
Match Cloud Bigtable with high-throughput NoSQL time-series data.
Cloud Bigtable is optimized for heavy write throughput and real-time analytical workloads on NoSQL data structures.
3
Assess standard relational database engine needs within a single region
Match Cloud SQL with managed MySQL/PostgreSQL supporting regional failover.
Cloud SQL simplifies standard database management with turnkey regional high availability.
4
Determine high-performance block storage needs for VM-hosted databases
Match Extreme Persistent Disk (pd-extreme) with high provisioned IOPS requirements.
Extreme Persistent Disk allows provisioned IOPS up to top-tier thresholds specifically for heavy I/O workloads running directly on Compute Engine instances.

Key Concept

Planning GCP Database and Persistent Disk Types for Architectural Requirements
Question 7Question →

An enterprise media streaming platform is architecting its storage and database tier on Google Cloud. Match each technical workload requirement to the most appropriate Google Cloud database service or block storage option.

Click a left item, then click its matching right item

Items

Ephemeral, sub-millisecond latency block storage for high-performance video transcoding scratch files on Compute Engine
Fully managed relational database supporting standard SQL and ACID compliance with regional failover for subscription billing
Serverless, globally available NoSQL document database for managing dynamic user profile attributes and active session states
Petabyte-scale, high-throughput NoSQL database optimized for real-time video playback telemetry and time-series analytical ingestion

Matches

Show answer & explanation

Answer

The requirement for ephemeral video transcoding scratch space pairs with Local SSD; the relational subscription billing workload pairs with Cloud SQL (Regional High Availability configuration); the serverless user profile document store pairs with Firestore in Native mode; and the petabyte-scale real-time telemetry workload pairs with Cloud Bigtable.
Each requirement is mapped directly to its optimal GCP engine based on architectural strengths: Local SSD for maximum throughput ephemeral block storage, Cloud SQL for relational transactional consistency, Firestore for flexible document management, and Cloud Bigtable for heavy time-series telemetry ingestion.

Step-by-Step Solution

1
Evaluate the requirement for high-performance temporary storage.
Video transcoding requires maximum throughput and minimal latency for scratch files, but does not require data persistence across instance restarts.
Local SSD is physically bound to the host server, providing ultra-high performance suitable for temporary scratch disk needs.
2
Evaluate the requirement for transactional relational data.
Subscription billing relies on standard SQL transactions and strict ACID guarantees paired with regional redundancy.
Cloud SQL provides managed relational engine capabilities complete with regional automated failover setups.
3
Evaluate the requirement for mobile/web user session and profile data.
Document-oriented session states benefit from a serverless, auto-scaling database with multi-region availability.
Firestore in Native mode simplifies serverless application architecture while scaling seamlessly for user state.
4
Evaluate the requirement for real-time analytics and time-series telemetry.
High-velocity streaming playback events require continuous high-throughput writes at scale.
Cloud Bigtable is designed specifically for time-series ingestion and massive analytical write workloads.

Key Concept

Selecting appropriate GCP storage types based on relational schema requirements, IOPS/latency needs, data longevity, and access patterns.
Estimated Time:1m 30s
Question 8Question →

A cloud architecture team is designing the infrastructure for a newly onboarded suite of enterprise applications. Match each technical workload requirement to the most appropriate Google Cloud storage or database option based on Google-recommended practices.

Click a left item, then click its matching right item

Items

A mobile application backend requiring real-time document synchronization across offline devices, client SDK integration, and seamless serverless scaling.
A mission-critical relational database requiring global multi-region ACID compliance, horizontal scaling without sharding, and up to 99.999% availability.
A high-performance database running on Compute Engine virtual machines requiring provisioned performance up to 120,000 IOPS independent of storage volume capacity.
A large-scale IoT sensor data processing pipeline requiring low single-digit millisecond latency for massive throughput of unstructured time-series records.

Matches

Show answer & explanation

Answer

Matching pairs: Mobile app offline sync matches Firestore; Global multi-region relational ACID matches Cloud Spanner; Compute Engine VM requiring independent high IOPS provisioning matches Extreme Persistent Disk (pd-extreme); Large-scale IoT time-series processing matches Cloud Bigtable.
Matching each workload to its ideal GCP storage option respects core database engine capabilities and persistent disk performance characteristics: Firestore handles mobile document state sync; Cloud Spanner handles global relational scale; Extreme Persistent Disk allows independent IOPS provisioning for VM workloads; Cloud Bigtable handles massive IoT time-series data ingestion.

Step-by-Step Solution

1
Analyze mobile app document sync requirement
Firestore is selected because of native offline synchronization support and serverless document storage capabilities.
Neither relational databases nor block storage provide native mobile client state synchronization.
2
Analyze global multi-region relational database requirement
Cloud Spanner is selected because it delivers global consistency, horizontal relational scaling, and ACID compliance.
Cloud SQL does not scale horizontally across global regions with multi-region write consistency.
3
Analyze Compute Engine high IOPS persistent block storage requirement
Extreme Persistent Disk (pd-extreme) is selected as it allows IOPS to be provisioned independently of capacity.
Standard SSD (pd-ssd) and Balanced (pd-balanced) Persistent Disks scale IOPS strictly based on allocated disk size.
4
Analyze high-throughput IoT time-series requirement
Cloud Bigtable is selected because it handles massive throughput key-value and time-series data with single-digit millisecond latency.
Bigtable is designed specifically for high-volume unstructured streaming ingest and analytical workloads.

Key Concept

Selecting GCP Database and Block Storage Solutions Based on Workload Characteristics
Question 9Question →

Match each Google Cloud database or persistent disk option on the left with its target technical workload requirement on the right.

Click a left item, then click its matching right item

Items

Cloud Spanner
Cloud Bigtable
Extreme Persistent Disk (pd-extreme)
Firestore

Matches

Show answer & explanation

Answer

Cloud Spanner matches fully managed relational database with strong global ACID consistency; Cloud Bigtable matches high-throughput NoSQL key-value store for time-series; Extreme Persistent Disk matches block storage providing up to 120,000 IOPS; Firestore matches serverless NoSQL document storage with real-time synchronization.
Each Google Cloud storage service is matched to its core architectural use case: Cloud Spanner delivers globally scalable relational transactions; Cloud Bigtable provides sub-10ms latency for massive NoSQL time-series data; Extreme Persistent Disk (pd-extreme) provides maximum block-level IOPS for VM-hosted databases; and Firestore provides serverless NoSQL document synchronization for web/mobile apps.

Step-by-Step Solution

1
Analyze the data structure, transaction model, and scalability requirements for each workload scenario.
Differentiate between relational global transactions, NoSQL key-value throughput, NoSQL document sync, and high-performance block storage.
Selecting the proper GCP database or block storage option depends on consistency guarantees, query capabilities, and IOPS requirements.
2
Map managed database options based on database paradigm.
Pair Cloud Spanner with global relational workloads needing ACID compliance, and pair Firestore with serverless document sync for mobile/web clients.
Cloud Spanner offers horizontally scalable SQL, while Firestore focuses on document hierarchies with client SDK sync.
3
Map high-throughput NoSQL storage and specialized Compute Engine block storage.
Pair Cloud Bigtable with high-throughput low-latency NoSQL data streams, and pair pd-extreme with high-IOPS block storage for self-managed VM databases.
Cloud Bigtable handles massive analytical streams, while pd-extreme provides the highest IOPS for block devices attached to Compute Engine VMs.

Key Concept

Selecting GCP Database and Storage Solutions Based on Workload Requirements
Question 10Question →

An enterprise cloud engineering team is evaluating Google Cloud database solutions and persistent block storage types for an infrastructure modernization project. Match each storage or database service on the left to the corresponding workload architecture requirement on the right.

Click a left item, then click its matching right item

Items

Cloud Spanner
Cloud Bigtable
Persistent Disk Balanced (pd-balanced)
Local SSD

Matches

Show answer & explanation

Answer

Cloud Spanner matches globally distributed relational ACID requirements; Cloud Bigtable matches high-throughput NoSQL time-series workloads; Persistent Disk Balanced matches cost-effective SSD general-purpose block storage; Local SSD matches ephemeral physically attached scratch storage.
Each service aligns directly with its fundamental design characteristics: Cloud Spanner handles global relational transactions; Cloud Bigtable provides scalable NoSQL high-throughput storage; pd-balanced delivers cost-effective durable block storage; Local SSD supplies ultra-fast ephemeral host storage.

Step-by-Step Solution

1
Identify the database requirements involving multi-region relational SQL and strict ACID transactions.
Map Cloud Spanner to the requirement for globally distributed ACID relational data.
Cloud Spanner is designed specifically for global scale while maintaining full relational capabilities and ACID transactions.
2
Identify high-throughput NoSQL ingestion and time-series analytical needs.
Map Cloud Bigtable to the low-latency high-throughput NoSQL requirement.
Bigtable handles massive throughput and large-scale NoSQL data with low latency.
3
Differentiate between durable network-attached block storage and ephemeral host-attached storage.
Map pd-balanced to general-purpose enterprise virtual machine storage and Local SSD to high-performance temporary scratch storage.
pd-balanced offers durable network block storage with balanced IOPS/cost, whereas Local SSD provides host-bound ephemeral performance.

Key Concept

Selecting GCP Managed Databases and Block Storage Options
All practice questions — Google Cloud Associate Cloud Engineer | Examkin