- A
CLUSTER_SPEC
Why wrong: Not used; TF_CONFIG contains cluster spec.
- B
TF_CPP_MIN_LOG_LEVEL
Why wrong: Controls logging verbosity, not communication.
- C
TF_CONFIG_JSON
Why wrong: Not a standard env var; TF_CONFIG is a JSON string.
- D
TF_DISTRIBUTED_STRATEGY
Why wrong: Not a standard env var; strategy is set in code.
- E
TF_CONFIG
Required to specify cluster spec and task identity.
Question 258 of 1,000
PMLE Scaling Prototypes into ML Models Practice Question
This PMLE practice question tests your understanding of scaling prototypes into ml models. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
An engineer is designing a distributed training job on Vertex AI for a TensorFlow model that uses the MultiWorkerMirroredStrategy. They need to ensure proper communication between workers. Which two environment variables must be set correctly for each worker? (Choose TWO.)
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
TF_CONFIG
In TensorFlow distributed training with MultiWorkerMirroredStrategy, two environment variables are essential. First, `TF_CONFIG` (option E) provides the cluster topology and task information, enabling gRPC communication between workers. Second, `TF_DISTRIBUTED_STRATEGY` (option D) can be set to `'multi_worker_mirrored'` to automatically configure the strategy without modifying code. Both must be correctly set for each worker to ensure proper communication and strategy initialization.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
CLUSTER_SPEC
Why it's wrong here
Not used; TF_CONFIG contains cluster spec.
- ✗
TF_CPP_MIN_LOG_LEVEL
Why it's wrong here
Controls logging verbosity, not communication.
- ✗
TF_CONFIG_JSON
Why it's wrong here
Not a standard env var; TF_CONFIG is a JSON string.
- ✗
TF_DISTRIBUTED_STRATEGY
Why it's wrong here
Not a standard env var; strategy is set in code.
- ✓
TF_CONFIG
Why this is correct
Required to specify cluster spec and task identity.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The exam often tests the distinction between the actual environment variable name (`TF_CONFIG`) and plausible-sounding alternatives like `TF_CONFIG_JSON` or `CLUSTER_SPEC`, leading candidates to pick a non-existent variable.
Detailed technical explanation
How to think about this question
Under the hood, `TF_CONFIG` is parsed by TensorFlow's `ClusterCoordinator` and `MultiWorkerMirroredStrategy` to build a `tf.distribute.ClusterSpec`, which defines the job names (e.g., `'worker'`) and task addresses (IP:port). Each worker uses this to establish gRPC connections for collective all-reduce operations. A subtle behavior: if `TF_CONFIG` is missing or malformed, workers will fall back to local execution or fail with a `RuntimeError` about an incomplete cluster specification.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PMLE question test?
Scaling Prototypes into ML Models — This question tests Scaling Prototypes into ML Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: TF_CONFIG — In TensorFlow distributed training with MultiWorkerMirroredStrategy, two environment variables are essential. First, `TF_CONFIG` (option E) provides the cluster topology and task information, enabling gRPC communication between workers. Second, `TF_DISTRIBUTED_STRATEGY` (option D) can be set to `'multi_worker_mirrored'` to automatically configure the strategy without modifying code. Both must be correctly set for each worker to ensure proper communication and strategy initialization.
What should I do if I get this PMLE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.
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