Google GCP-PMLE Certification Exam Sample Questions

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Google GCP-PMLE Sample Questions:

01. A fraud model scores 0.91 AUC on the held-out test set but only 0.68 in its first month of production. The training table was built by joining each labeled transaction to the customer aggregate features held in the feature store, reading whichever feature values were current when the training table was assembled. Labels are confirmed by an investigations team roughly 30 days after the transaction.
What most likely explains the gap between offline and production performance?
a) Training rows carried feature values computed after the label event, so the model learned from information unavailable at scoring time
b) The online and offline stores compute the aggregates with different code, so serving values differ from training values
c) The held-out test set was drawn at random rather than split by transaction date, so it shares customers with the training set
d) The investigations team confirms only a subset of transactions, so the label distribution in training differs from the true rate
 
02. Your TFX pipeline computes vocabularies, bucket boundaries and normalization constants from the training split, then trains a model on the transformed examples. The serving application currently reimplements those calculations in its own request handler, and the team wants to remove that duplicate code permanently.
Which component arrangement removes the duplication?
a) Have the request handler call a preprocessing microservice that the training job also calls before it writes examples
b) Use the Transform component and have Trainer attach the emitted transform graph to the exported SavedModel
c) Compute the constants in a Dataflow job, write them to a configuration file, and read that file from both the trainer and the handler
d) Add a StatisticsGen and SchemaGen pair so the schema is applied identically in training and in serving
 
03. Last night's retraining pipeline reported success and registered a new model version. The training loss curve shows the run finished in a fraction of its usual time, and the number of examples it consumed was about 4 percent of a normal night. The evaluation step still passed, because the holdout drawn from the same input was equally small.
Which two changes would cause a run like this to fail rather than succeed?
(Choose two.)
a) Raise the accuracy threshold that the evaluation step requires before a model is registered, above the level currently expected
b) Add a validation step that fails the run when the example count falls outside an expected range
c) Disable execution caching on the training component so that it always retrains from scratch on each nightly run
d) Increase the number of trainer replicas so that a short run is less likely
e) Have the run wait for the expected input partition to land, and fail if it has not arrived within a defined window
 
04. An assistant answers staff questions by retrieving passages from an internal document corpus and passing them to a foundation model. Some documents contain customer names and account numbers, and the corpus includes departmental material that not every employee is cleared to read.
Which two controls should the design include?
(Choose two.)
a) De-identify the corpus before it is indexed, so personal values are replaced by surrogates in the retrievable text
b) Lower the model's temperature so that each response stays closely tied to the wording of the retrieved passages
c) Add a system instruction telling the model that it must never reveal personal data or any material the user is not cleared to read
d) Log every prompt and response to BigQuery so any disclosure can be found afterwards
e) Filter retrieval by the requesting user's access rights, so only documents that user may read are eligible
 
05. Two annotators label images for a fabric defect detector. On borderline images they disagree often, and evaluation scores swing by several points between retraining runs even though the model code has not changed. The team wants the label set itself to become more reliable.
Which two actions should the team take?
(Choose two.)
a) Have several annotators label the same subset independently, and measure how often they agree
b) Enlarge the unlabeled pool so that each retraining run draws its images from a wider and more varied set of fabric samples
c) Write a labeling guideline with worked examples of the borderline cases, and route disagreements to an adjudicator
d) Have the current model pre-label every image, and ask the annotators only to confirm or reject the label that it proposes
e) Weight each annotator's labels according to how quickly they complete their assignments
 
06. Before committing to a model, a team wants to send the same set of prompts to several foundation models, compare the responses side by side, adjust parameters such as temperature, and save the prompts it settles on. Nobody on the team wants to write code for this stage.
Which Google Cloud surface is intended for this?
a) Model Garden
b) Vertex AI Studio
c) Vertex AI Workbench
d) Vertex AI Experiments
 
07. A claims triage model reports 0.88 accuracy overall, steady for six months. A regional manager reports that decisions for one product line, about 4 percent of volume, are frequently wrong. Recomputing the aggregate metric confirms it is still 0.88.
Why does the aggregate metric not reflect the manager's experience, and what should the team monitor instead?
a) The aggregate is computed on sampled requests, so a small product line may be absent from the sample; raise the sampling rate
b) Accuracy is the wrong measure for an imbalanced problem; monitor precision and recall on the overall population instead
c) Six months of history has diluted any recent change; monitor the metric over a rolling recent window
d) A 4 percent segment cannot move the aggregate; monitor metrics per slice, with thresholds for each slice the model serves
 
08. Drift monitoring on a pricing model fires most days. Reviewing three months of alerts, the team finds that two features are genuinely seasonal and swing well beyond the configured threshold every weekend, while a third feature has drifted only twice and both times reflected a real upstream defect. Engineers have started dismissing the notifications without reading them.
What is the most appropriate change?
a) Raise the single global threshold until the weekend swings on the two seasonal features stop crossing it
b) Set the threshold per feature, sized from each feature's own observed variation, and keep the sensitive setting on the third feature
c) Reduce the sampling rate on the monitoring job so that weekend traffic contributes fewer requests to the comparison
d) Route the alerts to a weekly digest, so that engineers review them together in one batch rather than as a stream of individual notifications
 
09. Monitoring on a tabular classifier reports a categorical domain anomaly: the payment method feature now contains a value that does not appear in the training data, following the launch of a new payment option. Predictions for requests carrying that value cluster tightly around the dataset's base rate, while other requests are unaffected.
What is happening to those requests?
a) The endpoint is rejecting the requests and returning a cached prediction, which is why the values cluster
b) The feature's distribution has drifted, and the monitoring threshold for it needs to be widened to reflect the new mix
c) The unseen category maps to the model's out-of-vocabulary bucket, so those rows carry no usable signal from that feature
d) The serving container is missing a dependency needed to encode the new value, and is substituting a default
 
10. Two teams report materially different evaluation scores for what both describe as the same model trained on the same data. Both used the same architecture and the same hyperparameters, and both read from the same BigQuery table name.
Which two checks are most likely to explain the difference?
(Choose two.)
a) Whether each run read a pinned snapshot of the table or queried it live at a different moment
b) Whether the feature transformations came from one shared definition or from each team's own preprocessing code
c) Whether the two runs used different random seeds for weight initialization
d) Whether each of the two runs used a different accelerator type, which changes low-level floating-point behavior slightly
e) Whether one team trained for more epochs than the other before evaluating

Answers:

Question: 01
Answer: a		 Question: 02
Answer: b		 Question: 03
Answer: b, e		 Question: 04
Answer: a, e		 Question: 05
Answer: a, c
Question: 06
Answer: b		 Question: 07
Answer: d		 Question: 08
Answer: b		 Question: 09
Answer: c		 Question: 10
Answer: a, b

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