Cloud Digital Leader Google Cloud Products and Services Practice Test 9 — 15 Questions

Question 1

A company runs a high-performance computing (HPC) workload on Compute Engine that requires low-latency, high-throughput scratch storage. The workload is checkpointed every hour. Which TWO storage options should the engineer consider for the scratch storage? (Choose 2)

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Persistent Disk (SSD). Option B (Persistent Disk SSD) is correct because SSD-backed Persistent Disks deliver the high IOPS and throughput needed for low-latency scratch storage while remaining durable and independent of the VM lifecycle, which suits an HPC workload that checkpoints hourly. Option C (Local SSD) is correct because Local SSDs are physically attached NVMe devices offering the lowest latency and highest throughput per instance, making them ideal for scratch data that is regenerated from checkpoints. Option A (Persistent Disk HDD) is not appropriate because HDD-backed disks have much lower IOPS and higher latency than SSDs. Option D (Filestore) is a managed NFS file service optimized for shared file access rather than the highest-throughput local scratch storage. Option E (Cloud Storage) is an object store accessed over the network, so it cannot provide the low-latency, high-throughput block-level scratch performance required.

Answer

Persistent Disk (HDD)

Answer

Filestore

Answer

Cloud Storage

Question 2

A company is migrating a legacy monolithic application to Google Cloud. They want to reduce operational overhead by eliminating server management while keeping the ability to run containers. The application has unpredictable traffic patterns and needs to scale to zero when idle. Which compute option is the best fit?

Accepted Answer

Cloud Run. Cloud Run is a serverless container platform that automatically scales to zero when idle, eliminating server management and cost during idle periods. GKE requires cluster management, Compute Engine is not serverless, and App Engine Flex requires VMs always running.

Answer

Google Kubernetes Engine (GKE)

Answer

App Engine Flexible Environment

Answer

Compute Engine with managed instance groups

Question 3

What is the primary benefit of using preemptible VMs on Compute Engine?

Accepted Answer

Cost savings for fault-tolerant workloads. Preemptible VMs on Compute Engine are deeply discounted (up to 60-91% off on-demand) but can be terminated by Google at any time with a 30-second warning, making them ideal for fault-tolerant, stateless, or batch workloads that can tolerate interruption. The primary benefit is therefore cost savings for workloads designed to handle preemption. They are not for guaranteed-availability or high-performance use cases.

Answer

Support for live migration

Answer

Higher performance than standard VMs

Answer

Guaranteed availability of resources

Question 4

An online retailer stores product images in a Cloud Storage bucket. Current access patterns: images uploaded once and read frequently for 30 days, then accessed rarely after 90 days, and must be retained for 7 years for compliance. Which storage class transition strategy minimizes cost while meeting requirements?

Accepted Answer

Upload to Standard, lifecycle rule to Nearline at 30 days, then to Archive at 90 days. The optimal strategy is to upload to Standard (for frequent access in the first 30 days), then transition to Nearline at 30 days (for infrequent access but still low latency), and finally to Archive at 90 days (for long-term retention at lowest cost). This matches the access pattern: frequent reads for 30 days, rare access after 90 days, and 7-year retention. Archive is the cheapest storage class for long-term retention, and transitioning at 90 days aligns with the change in access frequency.

Answer

Upload to Nearline, lifecycle rule to Archive at 30 days

Answer

Upload to Standard, lifecycle rule to Nearline at 30 days, then to Coldline at 90 days

Answer

Upload to Standard, lifecycle rule to Coldline at 30 days, then to Archive at 90 days

Question 5

A development team runs a web application on Cloud Run. They need to store session state across requests. Which Google Cloud service should they use?

Accepted Answer

Memorystore for Redis. Cloud Run instances are stateless; for session state, use an external caching layer like Memorystore (Redis). Pub/Sub is for messaging, Cloud SQL for relational data, and Cloud Storage for objects.

Answer

Cloud Storage

Answer

Cloud SQL

Answer

Cloud Pub/Sub

Question 6

A financial services company needs a relational database with global strong consistency, horizontal scaling, and 99.999% availability SLA. Which database should they choose?

Accepted Answer

Cloud Spanner. Cloud Spanner is Google's globally distributed, horizontally scalable relational database that offers external consistency (strong consistency) and a 99.999% availability SLA for multi-region configurations. It is the only GCP database that combines relational semantics, horizontal scale, and global strong consistency at that SLA level.

Answer

Cloud Bigtable

Answer

Cloud SQL

Answer

Firestore

Question 7

Which Google Cloud service is a fully managed, serverless data warehouse for running SQL queries on petabyte-scale datasets?

Accepted Answer

BigQuery. BigQuery is the serverless data warehouse for analytics SQL queries. Dataflow is for stream processing, Dataproc for Hadoop/Spark, and Cloud SQL for OLTP.

Answer

Cloud SQL

Answer

Cloud Dataflow

Answer

Cloud Dataproc

Question 8

A company uses Cloud Functions to process image uploads. Each image triggers a function that uses Vision API to extract text and stores results in Firestore. The function sometimes fails due to timeout when images are large. How should they redesign for reliability and scale?

Accepted Answer

Use Cloud Tasks with Cloud Run to process images asynchronously. Redesigning to use Cloud Tasks with Cloud Run allows asynchronous processing of image uploads. Cloud Tasks can queue the work and trigger Cloud Run services, which can scale independently and have longer timeouts (up to 60 minutes). This decouples the upload from processing, improving reliability and scalability, and avoids Cloud Functions' timeout limits.

Answer

Use Cloud Scheduler to trigger the function every minute

Answer

Increase the function timeout to 60 minutes

Answer

Use Compute Engine VMs with startup scripts

Question 9

A data engineer needs to process streaming clickstream data in real-time, apply transformations, and write the output to BigQuery. Which Google Cloud service is built for this use case?

Accepted Answer

Cloud Dataflow. Cloud Dataflow is Google Cloud's fully managed, serverless service for both batch and stream processing, built on Apache Beam. It natively supports reading from Pub/Sub, applying transformations, and writing to BigQuery, making it the correct choice for real-time clickstream processing. Its autoscaling and exactly-once processing capabilities are designed for streaming pipelines like this.

Answer

Cloud Dataproc

Answer

Cloud Pub/Sub

Answer

Cloud Functions

Question 10

An organization runs a multi-region web application behind a global external HTTP(S) load balancer. They want to protect against DDoS attacks and filter traffic based on IP reputation and request headers. Which service should they integrate with the load balancer?

Accepted Answer

Cloud Armor. Cloud Armor is Google Cloud's edge security service that attaches to global external HTTP(S) load balancers and provides WAF rules, IP reputation/denylists, geo-blocking, and header-based filtering. It's the native integration point for DDoS and Layer 7 filtering at the load balancer.

Answer

Cloud CDN

Answer

VPC firewall rules

Answer

Cloud NAT

Question 11

Which Google Cloud service provides a unified platform for building, training, and deploying machine learning models at scale?

Accepted Answer

Vertex AI. Vertex AI is the unified ML platform covering all stages of ML workflow. AutoML is a component, Dataflow is for data processing, and BigQuery ML runs ML models in SQL.

Answer

BigQuery ML

Answer

AutoML

Answer

Cloud Dataflow

Question 12

An analytics team needs to create dashboards and visualizations from data stored in BigQuery. They want a free solution that integrates natively. Which tool should they use?

Accepted Answer

Looker Studio. Looker Studio (formerly Google Data Studio) is a free, fully managed business intelligence and data visualization tool that natively connects to BigQuery. It allows users to create interactive dashboards and reports without any cost, making it the ideal choice for an analytics team seeking a free, natively integrated solution. Its direct BigQuery connector enables real-time querying and visualization of large datasets.

Answer

Cloud Dataflow

Answer

Looker

Answer

Google Sheets

Question 13

A company is building a real-time leaderboard for an online game using Google Cloud. They need a database that can handle millions of updates per second with low latency and serve the current top scores. Which TWO services should they use together? (Choose 2)

Accepted Answer

Cloud Bigtable. Cloud Bigtable (C) is correct because it is a fully managed, horizontally scalable NoSQL database designed for high-throughput, low-latency workloads, making it suitable for ingesting millions of score updates per second and durably storing the leaderboard data. Memorystore for Redis (D) is correct because it provides an in-memory data store with sub-millisecond latency, ideal for serving the current top scores in real time and handling rapid ranking operations. Together, Bigtable handles massive write throughput while Redis serves the hot leaderboard reads. Cloud SQL (A) is not appropriate because it is a relational database that cannot scale to millions of updates per second with low latency. Firestore (B) is not ideal because, while it is a NoSQL document database, it is optimized for mobile/web app synchronization rather than extreme write throughput. BigQuery (E) is not suitable because it is an analytics data warehouse designed for large-scale queries, not real-time transactional updates.

Answer

Cloud SQL

Answer

Firestore

Answer

BigQuery

Question 14

A developer wants to deploy a containerized web application that can scale to zero when not in use, and only pay for actual request processing time. Which Google Cloud compute service should the developer use?

Accepted Answer

Cloud Run. Cloud Run is a serverless container runtime that scales to zero and charges per request, ideal for containerized apps with variable traffic. Cloud Functions is for smaller code snippets, not containers. Compute Engine runs VMs continuously, and GKE requires at least one node.

Answer

Cloud Functions

Answer

Compute Engine

Answer

Google Kubernetes Engine (GKE)

Question 15

A data engineering team needs to process streaming data from Cloud Pub/Sub, perform transformations, and write the results to BigQuery. The team requires exactly-once processing semantics and automatic scaling. Which service should they use?

Accepted Answer

Cloud Dataflow. Dataflow (Apache Beam) provides exactly-once processing, autoscaling, and native integration with Pub/Sub and BigQuery for streaming pipelines. Cloud Dataproc is for batch Spark/Hadoop, not streaming. Cloud Functions processes events one at a time without exactly-once guarantees across a pipeline. BigQuery itself does not transform streaming data.

Answer

Cloud Functions

Answer

Cloud Dataproc

Answer

BigQuery