PMLE Practice Test 6 — 10 Questions

Question 1

A retail company wants to build a customer churn prediction model using AutoML Tables. They have a dataset with 5000 rows and 50 features, including customer ID, transaction history, and support tickets. The target is a binary column 'churned'. After training, the model shows high accuracy but low recall for the churned class. What is the most likely cause?

Accepted Answer

The churned class is underrepresented, causing the model to favor the majority class.. Option C is correct because in imbalanced datasets, AutoML Tables optimizes for overall accuracy, which can be high if the majority class dominates. Low recall for the churned class indicates the model predicts most instances as non-churned, a classic symptom of class imbalance. AutoML Tables provides class weighting and sampling options to mitigate this, but without them, the model favors the majority class.

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The dataset is too small for AutoML to train effectively.

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The features are not normalized, leading to biased predictions.

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The dataset includes a unique customer ID feature, causing overfitting.

Question 2

A financial institution uses BigQuery ML to train a linear regression model to predict loan default risk. The model is trained on a dataset with 100 million rows and 50 features. During inference, the engineer uses the ML.PREDICT function. However, the query takes several minutes to run and times out frequently. The data is static and updated monthly. What is the most cost-effective and low-code solution to improve prediction latency?

Accepted Answer

Export the trained model as a SQL function using the EXPORT MODEL statement, then use it for predictions.. Option A is correct because exporting the trained model as a SQL function via `EXPORT MODEL` converts the linear regression coefficients into a persistent SQL UDF, eliminating the overhead of model loading and serialization during each `ML.PREDICT` call. This approach is low-code (no external pipeline) and cost-effective since predictions are executed as standard SQL without consuming BigQuery ML slot resources for model inference.

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Create a Dataflow pipeline to precompute predictions and store them in a separate table.

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Use a materialized view to precompute the prediction features.

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Increase the BigQuery compute capacity by reserving more slots.

Question 3

Which TWO actions are best practices when scaling a prototype ML model to production in Google Cloud?

Accepted Answer

Store and manage features in a feature store like Vertex AI Feature Store.. Vertex AI Feature Store centralizes feature management, ensuring consistency between training and serving. This eliminates training-serving skew by providing a single source of truth for features, which is critical when scaling from prototype to production.

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Test the model only on a small sample of the production data to save costs.

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Manually scale inference instances based on historical traffic patterns.

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Use one-hot encoding for all categorical features without considering cardinality.

Question 4

A company deploys a custom ML model on Vertex AI to predict customer churn. The model retrains weekly, and predictions are served via a Vertex AI endpoint. After a recent retraining, the monitoring dashboard shows a sudden increase in prediction requests but a decrease in predicted churn probabilities. The model's accuracy on the validation set remains stable. What is the most likely cause of the observed behavior?

Accepted Answer

The incoming data distribution has changed, e.g., due to a new marketing campaign attracting different customers.. Option C is correct because a sudden increase in prediction requests alongside a decrease in predicted churn probabilities, while validation accuracy remains stable, indicates a shift in the incoming data distribution (covariate shift). This is typical when a new marketing campaign attracts a different customer segment that inherently has lower churn risk. The model itself hasn't degraded; it's simply seeing a different population than it was trained on, which changes the base rate of churn in the live traffic.

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A training-serving skew exists between the training pipeline and the serving endpoint.

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Concept drift has occurred, changing the relationship between features and churn.

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Data leakage during training caused the model to overfit to historical patterns.

Question 5

Which TWO factors should you consider when choosing between BigQuery and Cloud Storage for storing training data? (Choose 2)

Accepted Answer

The format of the data: structured vs. unstructured.. Option A is correct because BigQuery is optimized for structured, tabular data (e.g., CSV, Avro, Parquet) and supports SQL queries, while Cloud Storage is a better fit for unstructured data (e.g., images, videos, raw text files) that does not require schema enforcement. Choosing the right storage depends on whether the training data has a fixed schema and requires relational querying or is blob-based and needs high-throughput access.

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The requirement for data encryption at rest.

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The need for fine-grained access control at the row level.

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The maximum size of the dataset (BigQuery limit 1 TB).

Question 6

A company wants to build a low-code ML pipeline using Vertex AI Pipelines and BigQuery ML. They need to train, evaluate, and deploy a model. Which TWO statements are correct about the integration between Vertex AI Pipelines and BigQuery ML? (Choose TWO.)

Accepted Answer

Vertex AI Pipeline steps can include BigQuery ML training via the BigQueryQueryJob operator.. Option D is correct because Vertex AI Pipelines can integrate with BigQuery ML by using the BigQueryQueryJob operator to execute SQL-based training queries, such as `CREATE MODEL`, as a pipeline step. This allows you to orchestrate BigQuery ML model training within a Vertex AI Pipeline, enabling a low-code ML workflow.

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BigQuery ML models are automatically stored in Vertex AI Model Registry after training.

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BigQuery ML supports hyperparameter tuning using the CREATE MODEL statement.

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Vertex AI Pipelines supports automatic retry of failed steps due to transient errors.

Question 7

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?

Accepted Answer

The training_data input should be a Vertex AI Dataset resource, not a simple string.. Option C is correct because Vertex AI Pipeline steps that use a CustomJob to train a model require the training data input to be a Vertex AI Dataset resource (a Dataset object), not a plain string. When a string is passed instead of a Dataset resource, the pipeline attempts to create a custom job with an invalid resource name, as the backend expects a properly formatted Dataset resource name (e.g., projects/{project}/locations/{location}/datasets/{dataset_id}). This mismatch triggers the 'Invalid resource name' error.

Answer

The container image URI is incorrect; it should be from gcr.io/vertex-ai/training.

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The output artifact schema is missing the 'type' property.

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The machine type n1-standard-4 is not supported for Vertex AI training.

Question 8

A data scientist is using Vertex AI Workbench user-managed notebooks. They need to collaborate with a colleague on the same notebook. The colleague should be able to edit the notebook simultaneously. What should they do?

Accepted Answer

Share the notebook instance URL with the colleague; both can edit simultaneously. Vertex AI Workbench user-managed notebooks support real-time collaboration by sharing the notebook instance URL. When you share the URL with a colleague, both users can edit the notebook simultaneously because the underlying JupyterLab environment is multi-user and supports concurrent editing sessions. This is the intended method for synchronous collaboration on the same notebook instance.

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Store the notebook in Cloud Source Repositories and have the colleague clone it

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Share the underlying Compute Engine VM's SSH access with the colleague

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Export the notebook to Colab and share the link

Question 9

A company stores training data in Cloud Storage and uses Vertex AI Training for model training. They want to implement a data validation pipeline to detect data drift before retraining. Which service should they use?

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Model Monitoring is designed specifically to detect data drift and feature skew in production ML models by continuously comparing prediction requests against a baseline training dataset. It provides automated alerts when statistical distributions shift beyond a defined threshold, making it the correct choice for a data validation pipeline before retraining.

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BigQuery ML

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Cloud Data Loss Prevention

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Dataflow

Question 10

A company trains a model using Vertex AI Training and then deploys it to Vertex AI Prediction. They notice that prediction requests fail with 'InvalidArgument: input tensor shape mismatch'. Which THREE are possible causes?

Accepted Answer

The input data types do not match the expected types (e.g., float vs int). Option C is correct because Vertex AI Prediction expects the input tensor data types to exactly match those used during model training. If the model was trained with float32 inputs but the prediction request sends int32 values, the serving infrastructure detects the mismatch and returns an 'InvalidArgument: input tensor shape mismatch' error, as TensorFlow Serving (which underlies Vertex AI Prediction) validates dtype consistency at the graph level.

Answer

The model was exported in a different format than supported

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The batch size in the request is too large