The answer is to use ML.ADJUST_THRESHOLD to lower the classification threshold. This directly addresses low recall in BigQuery ML logistic regression by reducing the decision boundary—for example, from 0.5 to 0.3—so that more customers are classified as positive for churn, increasing true positives at the cost of more false positives. On the Google Professional Machine Learning Engineer exam, this tests your understanding of the precision-recall tradeoff and the practical use of BigQuery ML’s built-in threshold adjustment, a common alternative to retraining or reweighting data. A frequent trap is assuming you must change the model architecture or add class weights, but the fastest fix is simply lowering the threshold. Memory tip: “Lower the bar to catch more recall”—think of a high jump bar: lowering it lets more athletes clear it, just as lowering the threshold catches more true positives.
PMLE Architecting low-code ML solutions Practice Question
This PMLE practice question tests your understanding of architecting low-code ml solutions. 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.
Exhibit
Refer to the exhibit.
```
# BigQuery ML model creation
CREATE OR REPLACE MODEL `mydataset.churn_model`
OPTIONS
( model_type='LOGISTIC_REG',
auto_class_weights=TRUE,
input_label_cols=['churned'] )
AS
SELECT
* EXCEPT(customer_id, churn_date)
FROM `mydataset.training_data`
WHERE churn_date IS NOT NULL;
# Evaluation query
SELECT * FROM ML.EVALUATE(MODEL `mydataset.churn_model`);
# Prediction query
SELECT * FROM ML.PREDICT(MODEL `mydataset.churn_model`,
TABLE `mydataset.new_customers`);
```
Refer to the exhibit. A data analyst creates a BigQuery ML logistic regression model for churn prediction. The model evaluation shows high precision but low recall. Which change to the model creation would most likely improve recall?
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.
Refer to the exhibit.
```
# BigQuery ML model creation
CREATE OR REPLACE MODEL `mydataset.churn_model`
OPTIONS
( model_type='LOGISTIC_REG',
auto_class_weights=TRUE,
input_label_cols=['churned'] )
AS
SELECT
* EXCEPT(customer_id, churn_date)
FROM `mydataset.training_data`
WHERE churn_date IS NOT NULL;
# Evaluation query
SELECT * FROM ML.EVALUATE(MODEL `mydataset.churn_model`);
# Prediction query
SELECT * FROM ML.PREDICT(MODEL `mydataset.churn_model`,
TABLE `mydataset.new_customers`);
```
A
Drop more columns to reduce overfitting.
Why wrong: Why D is wrong: Overfitting may not be the issue, and dropping columns could worsen recall.
B
Increase the training data by including customers without churn dates.
Why wrong: Why B is wrong: Including non-churned customers may not improve recall.
C
Use ML.ADJUST_THRESHOLD to lower the classification threshold.
Why C is correct: Lowering threshold increases sensitivity, improving recall.
D
Change model_type to 'BOOSTED_TREE_CLASSIFIER'.
Why wrong: Why A is wrong: Model type change may not directly improve recall.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use ML.ADJUST_THRESHOLD to lower the classification threshold.
Option C is correct because lowering the classification threshold (e.g., from 0.5 to 0.3) will classify more customers as positive (churn), increasing recall (true positives / (true positives + false negatives)). In BigQuery ML, ML.ADJUST_THRESHOLD directly modifies the decision boundary, trading off precision for recall. This is the most direct way to address low recall without altering the model architecture or training data.
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.
✗
Drop more columns to reduce overfitting.
Why it's wrong here
Why D is wrong: Overfitting may not be the issue, and dropping columns could worsen recall.
✗
Increase the training data by including customers without churn dates.
Why it's wrong here
Why B is wrong: Including non-churned customers may not improve recall.
✓
Use ML.ADJUST_THRESHOLD to lower the classification threshold.
Why this is correct
Why C is correct: Lowering threshold increases sensitivity, improving recall.
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.
✗
Change model_type to 'BOOSTED_TREE_CLASSIFIER'.
Why it's wrong here
Why A is wrong: Model type change may not directly improve recall.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that changing the model type (e.g., to boosted trees) is the default solution for any performance metric issue, when in fact the threshold adjustment is the simplest and most direct way to trade off precision and recall in a logistic regression model.
Detailed technical explanation
How to think about this question
In logistic regression, the default classification threshold is 0.5, meaning predicted probabilities >= 0.5 are classified as positive. Lowering this threshold increases the number of positive predictions, which raises recall (more true positives) but may also increase false positives (lowering precision). In BigQuery ML, ML.ADJUST_THRESHOLD is a post-training transformation that adjusts the threshold without retraining, making it a lightweight and targeted fix for recall issues. This is especially useful in churn prediction where false negatives (missing churners) are often more costly than false positives.
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.
Architecting low-code ML solutions — This question tests Architecting low-code ML solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use ML.ADJUST_THRESHOLD to lower the classification threshold. — Option C is correct because lowering the classification threshold (e.g., from 0.5 to 0.3) will classify more customers as positive (churn), increasing recall (true positives / (true positives + false negatives)). In BigQuery ML, ML.ADJUST_THRESHOLD directly modifies the decision boundary, trading off precision for recall. This is the most direct way to address low recall without altering the model architecture or training data.
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.
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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