Questions d'entraînement

Question 1

A media-streaming company is migrating a 12-node analytics backend to a fully managed relational database service on Google Cloud that prohibits custom agents and targets a 99.95% monthly SLA; to reduce operational toil without writing maintenance scripts, which routine system-maintenance task will the managed platform perform automatically across all instances?

Question 2

A logistics company operates 24 Kubernetes clusters (12 on-premises, 8 on Google Cloud, and 4 on AWS) to support microservices across 60 warehouses and needs a single, centralized platform to consistently manage policies, configurations, service mesh, and observability across all environments without relocating workloads; which Google Cloud service should they choose?

Question 3

Your 12-person logistics startup must add image label detection for 50,000 product photos per month and sentiment analysis on 5,000 support emails per week within 30 days and without hiring any new staff; how do Google Cloud's out-of-the-box AI APIs make AI/ML adoption feasible for your team?

Analyse de la question

Core Concept: This question tests Google Cloud’s pre-trained, out-of-the-box AI services (often called “AI APIs”), such as Cloud Vision API for image label detection and Natural Language API (or Vertex AI language capabilities) for sentiment analysis. These are managed services that expose REST/gRPC endpoints and client libraries, letting teams add AI features without building or training models. Why the Answer is Correct: A small startup must deliver two AI capabilities quickly (within 30 days) and without hiring ML specialists. Google Cloud’s pre-trained APIs are designed for exactly this scenario: you send data (images or text) to an API and receive predictions (labels or sentiment) immediately. No custom model development lifecycle is required (data labeling, feature engineering, training, hyperparameter tuning, evaluation, deployment, monitoring). This dramatically reduces time-to-value and operational burden, aligning with the Google Cloud Architecture Framework principle of using managed services to reduce undifferentiated heavy lifting. Key Features: 1) Pre-trained models: Vision label detection and sentiment analysis work out-of-the-box. 2) Simple integration: client libraries, REST calls, and IAM-controlled service accounts. 3) Scalability: the APIs scale to handle monthly/weekly batch workloads; you can run batch jobs via Cloud Run/Functions + Cloud Scheduler, or Dataflow for larger pipelines. 4) Governance and security: IAM permissions (least privilege), audit logs, and optional VPC Service Controls for data exfiltration protection. 5) Cost model: pay-per-use pricing per request/unit processed, which is attractive for variable workloads like 50,000 images/month and 5,000 emails/week. Common Misconceptions: Some assume “AI” always requires custom training (D) or that managed AI removes the need for data preparation (B). In reality, you must provide valid inputs (correct image formats, text encoding, language considerations) and handle quality checks, but you don’t need to build models. Others think AI APIs reduce security requirements (C); instead, they still require appropriate IAM and data handling controls. Exam Tips: For Digital Leader questions, map business constraints (small team, fast timeline, no hiring) to managed services and pre-trained APIs. When you see tasks like OCR, labeling, translation, sentiment, entity extraction, or speech-to-text, the best answer is typically Google’s out-of-the-box AI APIs or Vertex AI pre-built offerings—especially when custom ML development is unrealistic.

Question 4

A national bike-sharing consortium needs to publish real-time docking station availability (updated every 2 seconds) from 300 municipal operators and simultaneously receive rider rental/return events in real time to forward to each operator’s system. They require a standardized, secure, versioned interface over HTTPS that supports authentication and partner-specific rate limits; what should they implement?

Question 5

Your team operates a ride-sharing analytics service on Google Cloud across us-central1 and europe-west1, aiming for 99.9% monthly availability and 95th-percentile request latency under 300 ms during peak 18:00–22:00 traffic; within the SRE framework, which concept represents the metric that actually quantifies how well the service is performing?

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Question 6

Your global e-commerce platform ingests clickstream and ad-impression events from 180 million monthly active users across 5 regions (NA, EU, APAC, LATAM, MEA). At peak, the system must handle at least 1.2 million writes per second with sub-10 ms write latency, store petabyte-scale wide-column time-series data, and scale horizontally without complex schema migrations. Which Google Cloud product should you choose?

Question 7

Your agriculture analytics company has 200,000 labeled PNG images of crop leaves in a Cloud Storage bucket and needs to train a custom model to classify each image into 8 disease categories within two weeks using a fully managed service without writing model code. Which Google Cloud product or service should you use?

Question 8

An urban bike-sharing startup managing 2,500 bikes across 120 stations wants to improve rider satisfaction over the next quarter. Over the past 6 months, they collected 500 user submissions via an in-app 'Report an issue' form, IoT telemetry from bike locks every 5 seconds, and daily station fill-level summaries. To decide where to prioritize improvements, which of the following represents unstructured data they can analyze?

Question 9

A 120-employee nonprofit is conducting a quarterly security readiness review and notes that during the last 30 days, 3 simulated lures were sent and 26% of staff clicked the links; the CISO wants to prioritize controls for social-engineering risks specifically. Which of the following is the most plausible method an attacker would use to carry out a social-engineering attack in this context?

Question 10

A metropolitan transit agency stores real-time vehicle locations and historical ridership data in a relational database (e.g., Cloud SQL). They want to create a new revenue stream by allowing third-party mobile app developers to use this data in their apps. They expect around 120,000 requests per day at peak and require usage-based billing without granting direct database access. Which cloud-first approach should the agency choose?

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