- A
Convert the model to TensorFlow Lite and use a smaller model.
Why wrong: This would reduce latency but typically reduces accuracy as well.
- B
Increase the number of prediction nodes in the AI Platform Prediction cluster.
Why wrong: This adds horizontal scaling but does not reduce per-request latency.
- C
Enable XLA (Accelerated Linear Algebra) compilation on model loading.
XLA compiles and optimizes the TensorFlow graph, often improving latency without affecting accuracy.
- D
Apply quantization to the model weights to reduce size.
Why wrong: Quantization reduces model size and latency but often reduces accuracy slightly.
Question 585 of 1,000
Reducing Prediction Latency with XLA Compilation — Google Professional Data Engineer Explained
This PDE practice question tests your understanding of operationalizing machine learning models. 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 company has deployed a machine learning model to AI Platform Prediction. The model uses a custom container with a TensorFlow SavedModel. After deployment, the prediction latency is higher than expected. Which action is most likely to reduce latency without significantly impacting model accuracy?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Quick Answer
The answer is to enable XLA (Accelerated Linear Algebra) compilation on model loading, as this directly reduces prediction latency without sacrificing accuracy. XLA compiles the TensorFlow computational graph into a fused, device-specific kernel, eliminating runtime overhead from graph execution and optimizing memory bandwidth usage. On the Google Professional Data Engineer exam, this question tests your understanding of performance optimization for AI Platform Prediction, specifically how to accelerate inference in custom containers without altering the model’s weights or architecture. A common trap is confusing XLA with quantization or pruning, which can degrade accuracy, or assuming that increasing hardware resources is the only path to lower latency. Remember the mnemonic: “XLA fuses, no accuracy loses”—it fuses operations for speed while preserving the original model’s precision.
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
✓
Enable XLA (Accelerated Linear Algebra) compilation on model loading.
Option C is correct because enabling XLA (Accelerated Linear Algebra) compilation on model loading optimizes the TensorFlow computation graph by fusing operations and reducing runtime overhead, which directly lowers prediction latency without altering model weights or architecture. XLA works by compiling the graph into efficient machine code at load time, improving execution speed while preserving the original model accuracy.
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.
- ✗
Convert the model to TensorFlow Lite and use a smaller model.
Why it's wrong here
This would reduce latency but typically reduces accuracy as well.
- ✗
Increase the number of prediction nodes in the AI Platform Prediction cluster.
Why it's wrong here
This adds horizontal scaling but does not reduce per-request latency.
- ✓
Enable XLA (Accelerated Linear Algebra) compilation on model loading.
Why this is correct
XLA compiles and optimizes the TensorFlow graph, often improving latency without affecting accuracy.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Apply quantization to the model weights to reduce size.
Why it's wrong here
Quantization reduces model size and latency but often reduces accuracy slightly.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common pitfall in Google Cloud exams is confusing latency reduction with throughput scaling; candidates often choose to increase prediction nodes (Option B) thinking it reduces latency, when it actually only improves concurrent request handling.
Detailed technical explanation
How to think about this question
XLA (Accelerated Linear Algebra) works by JIT-compiling the TensorFlow graph into a fused kernel that minimizes memory bandwidth and kernel launch overhead, often yielding 2-3x speedups for inference on CPU/GPU. A subtle behavior is that XLA can sometimes increase latency for very small models due to compilation overhead, but for typical production models with complex graphs, the benefit outweighs the cost. In real-world scenarios, enabling XLA on AI Platform Prediction is a configuration flag (e.g., `--enable-xla`) that requires no code changes, making it a low-risk optimization.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this PDE question test?
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable XLA (Accelerated Linear Algebra) compilation on model loading. — Option C is correct because enabling XLA (Accelerated Linear Algebra) compilation on model loading optimizes the TensorFlow computation graph by fusing operations and reducing runtime overhead, which directly lowers prediction latency without altering model weights or architecture. XLA works by compiling the graph into efficient machine code at load time, improving execution speed while preserving the original model accuracy.
What should I do if I get this PDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
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