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Refer to the Exhibit Practice Questions

Practise Google Professional Machine Learning Engineer practice questions — original exam-style scenarios covering every exam domain, with detailed explanations, wrong-answer analysis, and common exam traps.

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Scenario guide

How to approach refer to the exhibit practice questions

Practise exhibit-style questions that ask you to read a topology, table, command output or diagram before choosing the best answer.

Quick answer

Exhibit-style questions test whether you can read a topology, command output, diagram or table before choosing the best answer.

How to extract the relevant detail from an exhibit.

How topology, command output or routing information affects the answer.

How to avoid answering from memory before reading the evidence.

How to map the exhibit back to the exam objective.

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Practice set

Practice scenarios

Question 1mediummultiple choice
Full question →

Refer to the exhibit. A data engineer is defining a Vertex AI Pipeline step to train a model. The pipeline fails with an error: "Failed to create vertex ai custom job: Invalid resource name." What is the most likely cause of the error?

Network Topology
model_dir=$-training_data=$Refer to the exhibit.```- name: train_modeltask:containerSpec:imageUri: gcr.io/cloud-aiplatform/training/tf-cpu.2-8:latestargs:inputs:- name: model_dirvalue: /tmp/model- name: training_datavalue: projects/my-project/datasets/training_dataoutputs:- name: modelartifactSpec:schema: title: "model"uri: $(inputs.model_dir)resources:machineType: n1-standard-4
Question 2hardmultiple choice
Full question →

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?

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`);
```
Question 3hardmultiple choice
Full question →

Refer to the exhibit. A data scientist trained a BigQuery ML classification model to detect fraudulent transactions. The dataset has 95% non-fraud (class 0) and 5% fraud (class 1). The evaluation metrics show high accuracy (0.91) but low recall (0.60) for fraud detection. Which low-code approach should the data scientist take to improve recall without significantly sacrificing precision?

Network Topology
+Refer to the exhibit.```
Question 4hardmultiple choice
Full question →

A team deployed a prototype classification model to Vertex AI Prediction. After a week, they notice the metrics shown in the exhibit. What is the most likely cause of the performance degradation and latency increase?

Exhibit

Refer to the exhibit.

```
Model accuracy: 0.92
Training data: 10,000 records
Online prediction latency: 95th percentile = 450ms
QPS: 50

After moving to production:
- New data from users: 100,000 records/day
- Data distribution shift detected (new features emerge)
- Prediction latency increases to 95th percentile = 1200ms
- QPS drops to 30
```
Question 5hardmultiple choice
Full question →

Refer to the exhibit. What is being configured?

Exhibit

{
  "name": "projects/my-project/locations/us-central1/endpoints/my-endpoint",
  "displayName": "my-endpoint",
  "deployedModels": [
    {
      "model": "projects/my-project/locations/us-central1/models/12345",
      "displayName": "mymodel",
      "autoscalingMetricSpecs": [
        {
          "metricName": "aiplatform.googleapis.com/prediction/online/requests",
          "target": 100
        }
      ]
    }
  ],
  "trafficSplit": {
    "12345": 100
  }
}
Question 6easymulti select
Full question →

Refer to the exhibit. A data scientist is evaluating a binary classification model trained with BigQuery ML on an imbalanced dataset. The exhibit shows the output of ML.EVALUATE run on two different thresholds. Which TWO actions should the data scientist take to improve model performance? (Choose two.)

Exhibit

Reference the exhibit.

```json
[
  {"precision": "0.85", "recall": "0.78", "accuracy": "0.82", "f1_score": "0.81"},
  {"precision": "0.90", "recall": "0.88", "accuracy": "0.89", "f1_score": "0.89"}
]
```
Question 7easymultiple choice
Full question →

Refer to the exhibit. A data scientist notices that predictions from a deployed model are taking longer than expected. Which Cloud Monitoring metric should be inspected first to identify the bottleneck?

Exhibit

Refer to the exhibit.
```
{
  "insertId": "abc123",
  "jsonPayload": {
    "predictions": [0.98, 0.12],
    "modelVersionId": "1",
    "latencyMs": 450,
    "region": "us-central1"
  },
  "resource": {
    "type": "vertex_ai_endpoint",
    "labels": {
      "endpoint_id": "1234",
      "model_id": "model-xyz"
    }
  },
  "severity": "INFO",
  "timestamp": "2024-03-15T10:30:00Z"
}
```
Question 8hardmultiple choice
Full question →

Refer to the exhibit. A user is trying to upload a Vertex AI pipeline definition. The error indicates an invalid dependency order. What should the user do to fix this?

Exhibit

Refer to the exhibit.

# pipeline.yaml
pipeline:
  name: training-pipeline
  description: End-to-end ML pipeline
  params:
    project_id: {type: String}
    dataset_id: {type: String}
  tasks:
    - task1:
        component: preprocessing
        inputs:
          project_id: {inputValue: project_id}
          dataset_id: {inputValue: dataset_id}
    - task2:
        component: training
        inputs:
          data: {taskOutputs: task1.output}
        dependentTasks: [task1]

Error: (gsutil cp pipeline.yaml gs://my-bucket/pipelines/): RuntimeException: Failed to compile pipeline. Invalid pipeline definition: task 'task2' depends on 'task1' but 'task1' is defined after 'task2' in YAML ordering.
Question 9mediummultiple choice
Full question →

Refer to the exhibit. A user receives the error shown when trying to upload a model to Vertex AI. What is the most likely cause?

Network Topology
gcloud ai models uploadregion=us-central1 \display-name=fraud-detection-v2 \container-image-uri=gcr.io/cloud-aiplatform/prediction/tf2-cpu.2-12:latest \artifact-uri=gs://my-model-artifacts/fraud-detection/v2/ \version-aliases=championRefer to the exhibit.
Question 10easymultiple choice
Read the full NAT/PAT explanation →

Refer to the exhibit. The team notices that the pipeline fails to read data from the specified Cloud Storage path. What is the most likely issue?

Exhibit

pipeline:
  execution_config:
    runner: DataflowRunner
    project: my-project
    region: us-central1
  components:
    - component_type: CsvExampleGen
      component_name: example_gen
      arguments:
        input_basedir: gs://my-bucket/data/
Question 11mediummultiple choice
Full question →

Refer to the exhibit. What is this Cloud Build step doing?

Network Topology
args: ['ai'region=us-central1'display-name=mymodel'container-image-uri=us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-11:latest'artifact-uri=gs://my-bucket/model']steps:- name: 'gcr.io/cloud-builders/gcloud'
Question 12easymultiple choice
Full question →

Refer to the exhibit. A team runs the command above and sees only two models. They know there is a model 'model-v3' created three days ago. What is the most likely reason it is not listed?

Network Topology
gcloud ai models listregion=us-central1Using endpoint [https://us-central1-aiplatform.googleapis.com]List of models:- name: projects/my-project/locations/us-central1/models/123display_name: model-v1create_time: 2023-05-01T10:00:00Z- name: projects/my-project/locations/us-central1/models/456display_name: model-v2create_time: 2023-05-02T10:00:00Z
Question 13easymultiple choice
Full question →

Refer to the exhibit. A team runs this command to upload a model to Vertex AI. They want to create this model as a new version under an existing model named 'my_model'. What is missing from the command?

Network Topology
gcloud ai models uploadregion=us-central1display-name=my_modelartifact-uri=gs://my-bucket/modelcontainer-image-uri=us-docker.pkg.dev/cloud-ai-platform/prediction/tf2-cpu.2-8:latest
Question 14mediummultiple choice
Full question →

Refer to the exhibit. A team runs this Vertex AI Pipeline definition but the deploy component never executes, even though the evaluate step outputs a metric of 0.9. What is the most likely cause?

Exhibit

components:
  - name: evaluate
    container: gcr.io/my-project/evaluate:latest
    outputs:
      metric: {type: double}
  - name: gate
    type: condition
    inputs:
      metric: ${{ evaluate.outputs.metric }}
    condition: ${{ inputs.metric }} > 0.8
    actions:
      - deploy
  - name: deploy
    container: gcr.io/my-project/deploy:latest
    dependsOn: gate
Question 15hardmultiple choice
Full question →

Refer to the exhibit. An engineer notices no drift alerts but the model performance has degraded. What is the likely cause?

Exhibit

modelMonitoringConfig:
  objectiveConfig:
    detectionConfig:
      driftThresholds:
        age: 0.3
        income: 0.1
      skewThresholds:
        age: 0.2
        income: 0.05
  featureAttributionConfig:
    enabled: True

These PMLE practice questions are part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style PMLE questions with detailed explanations, topic-based practice, mock exams, readiness tracking, and study analytics.