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GCDL Practice Question: A machine learning team wants to train, evaluate,…
This GCDL practice question tests your understanding of a machine learning team wants to train, evaluate,…. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A machine learning team wants to train, evaluate, deploy, and monitor ML models in a unified platform without managing infrastructure, and with built-in support for experiment tracking, model versioning, and A/B testing between model versions. Which Google Cloud product provides this end-to-end managed ML platform?
Answer choices
Why each option matters
Good practice is not just finding the correct option. The wrong answers often show the exact trap the exam wants you to fall into.
A
Best answer
Vertex AI, Google Cloud's unified ML platform covering training, experiment tracking, model registry, deployment, and monitoring in a single managed service
Vertex AI is the complete answer. It provides: managed training (custom containers or AutoML), Vertex AI Experiments (experiment tracking and comparison), Vertex AI Model Registry (version management), Vertex AI Endpoints (serving with traffic splitting for A/B testing), and Model Monitoring (data drift and skew detection). This is Google Cloud's end-to-end ML platform.
B
Distractor review
Cloud Functions, for deploying ML inference code as serverless functions
Cloud Functions can serve ML predictions via HTTP, but it provides only the deployment/serving step. It has no training, experiment tracking, model registry, or monitoring capabilities.
C
Distractor review
BigQuery ML, for training and deploying ML models using SQL within BigQuery
BigQuery ML is excellent for training models directly in BigQuery using SQL, but it is limited to BigQuery data and specific algorithm types. It doesn't provide the full experiment tracking, custom training, model monitoring, and multi-version deployment the question describes.
D
Distractor review
Cloud Dataproc, for running distributed Spark ML jobs on managed Hadoop clusters
Cloud Dataproc runs Spark and Hadoop workloads. While Spark has ML libraries (MLlib), Dataproc doesn't provide the integrated experiment tracking, model registry, monitoring, or unified deployment capabilities described.
Common exam trap
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Technical deep dive
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Related practice questions
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More questions from this exam
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Question 1
A traditional retailer currently maintains its own data centers, purchasing servers every 3–5 years and paying for facilities, power, and staff regardless of demand. When it migrates its workloads to the public cloud, which change in cost model does it experience?
Question 2
An e-commerce company plans its infrastructure for peak shopping events (e.g., Black Friday) which drive 50× normal traffic. On-premises, they must maintain 50× capacity year-round. In the cloud, they provision 50× capacity only during peak periods. Which cloud characteristic enables this cost optimization?
Question 3
Which term describes the process by which organizations integrate digital technology into all areas of their business, fundamentally changing how they operate and deliver value to customers?
Question 4
When a company moves from maintaining its own data center to using Google Cloud, which operational responsibility does Google assume that the company previously managed?
Question 5
A hospital runs a patient records system that must remain on-premises due to strict regulatory data residency requirements. However, they also want to use cloud-based AI for diagnostic imaging analysis. Which cloud deployment model best describes their architecture?
Question 6
What is virtualization in the context of cloud computing, and why is it fundamental to how cloud providers deliver services?
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FAQ
Questions learners often ask
What does this GCDL question test?
Static NAT maps one inside address to one outside address.
What is the correct answer to this question?
The correct answer is: Vertex AI, Google Cloud's unified ML platform covering training, experiment tracking, model registry, deployment, and monitoring in a single managed service — Vertex AI is Google Cloud's unified ML platform that covers the entire ML lifecycle: managed training (AutoML and custom training), Vertex AI Experiments (tracking), Vertex AI Model Registry (versioning), Vertex AI Endpoints (deployment), and Vertex AI Model Monitoring (drift detection and performance monitoring). It eliminates the need to stitch together separate tools for each lifecycle stage.
What should I do if I get this GCDL question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related GCDL NAT questions on configuration and troubleshooting.
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