Preview a few questions below — answers are revealed when you take the
exam.
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A company is migrating its data analytics workload to Google Cloud. The team needs to ensure that the data processing pipeline can handle variable workloads efficiently. What approach should they take to optimize costs and performance?
- Implement a static cluster of Compute Engine instances to handle the data processing workload, ensuring that the instances are always running regardless of the workload.
- Utilize Google Cloud Dataflow with autoscaling enabled to dynamically adjust the number of worker instances based on the incoming data volume, optimizing both cost and performance.
- Migrate the data analytics workload to Google Kubernetes Engine (GKE) and manually adjust the number of pods based on the workload, ensuring that the cluster remains cost-effective.
- Use Google Cloud Storage for data processing and rely on Cloud Functions to trigger data processing jobs, ensuring that the processing is event-driven and cost-efficient.
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Identify the Google Cloud service that provides a fully managed, serverless platform for building and deploying containerized applications.
- Google Cloud Functions
- Google Cloud Run
- Google Cloud Storage
- Google Kubernetes Engine
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Consider the situation where a company wants to implement a disaster recovery plan using Google Cloud. Which method solves it best by ensuring minimal downtime and data loss?
- Set up a secondary data center on-premises and use VPN to replicate data between the primary and secondary locations.
- Utilize Google Cloud's regional persistent disks with synchronous replication to ensure data consistency across zones within a region.
- Implement Google Cloud's global load balancing with Cloud CDN to distribute traffic and reduce latency for end-users.
- Use Google Cloud's Cloud SQL with automated backups and point-in-time recovery to protect database instances.
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Which option best evaluates the effectiveness of using Google Cloud's BigQuery for large-scale data analytics in terms of performance and cost?
- BigQuery's serverless architecture allows for automatic scaling and cost optimization, making it ideal for handling petabyte-scale data analytics with pay-as-you-go pricing.
- BigQuery's reliance on Google's infrastructure results in higher costs compared to traditional on-premises solutions, despite its performance advantages.
- BigQuery's integration with other Google Cloud services leads to increased complexity and higher operational costs, negating its performance benefits.
- BigQuery's query performance is significantly impacted by the volume of data, leading to increased costs and reduced efficiency for large-scale analytics.
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How should a professional assess the security posture of a Google Cloud environment to ensure compliance with industry standards and regulations?
- Rely solely on Google Cloud's default security configurations and assume that they meet all industry standards and regulations.
- Conduct regular security audits, utilize Google Cloud's Security Command Center, and implement custom security policies to ensure compliance with industry standards and regulations.
- Disable all Google Cloud services that are not explicitly required for the workload to minimize the attack surface and ensure compliance.
- Use third-party security tools exclusively to assess the security posture of the Google Cloud environment, disregarding Google's native security features.