PMLE Practice Questions 2 — 50 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.

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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.

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The model was exported in a different format than supported

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The batch size in the request is too large

Question 11

A team uses Vertex AI Pipelines with CustomJob components that pull training code from a Cloud Source Repository. The pipeline fails with a 'Permission denied' error when trying to access the repository. The service account used by the pipeline has the 'Source Repository Viewer' role. What is the likely issue?

Accepted Answer

The 'Source Repository Viewer' role is insufficient; the service account needs 'Source Repository Reader' or higher. The 'Source Repository Viewer' role only allows listing and viewing repository metadata, not reading the actual source code. To clone or pull code from a Cloud Source Repository, the service account needs the 'Source Repository Reader' role (or higher), which grants the `source.repos.get` and `source.repos.read` permissions required for Git operations. The pipeline's CustomJob component fails because the service account lacks these permissions when attempting to access the repository.

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The training code contains a dependency that is not available in the custom container

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The pipeline is running in a different project than the repository; cross-project access is not supported

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The repository URL is incorrectly formatted; use the SSH URL instead of HTTPS

Question 12

A data science team deploys a PyTorch model using Vertex AI Prediction. The model requires GPU for inference, but they notice high costs and underutilized GPUs during off-peak hours. What is the most cost-effective solution?

Accepted Answer

Use a GPU instance with min replicas=0 and autoscaling. Option C is correct because setting min replicas to 0 allows Vertex AI Prediction to scale down to zero instances during off-peak hours, eliminating GPU costs when no requests are being served. Combined with autoscaling, the deployment will spin up GPU-backed instances on demand only when traffic arrives, directly addressing the underutilization issue while maintaining low latency for inference requests.

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Move the model to Cloud Functions

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Use a GPU instance with a fixed number of replicas

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Switch to a CPU-only machine type

Question 13

A company serves a scikit-learn model on Vertex AI Prediction but receives a 400 error with 'Prediction failed: Model evaluation error'. What is the most likely cause?

Accepted Answer

The model uses a scikit-learn version not supported by Vertex AI. Vertex AI Prediction supports specific versions of scikit-learn for serving models. If the model was trained with a version that is not in the supported list (e.g., 0.19, 0.20, 0.22, 0.23, 0.24, 1.0, 1.1), the prediction endpoint will fail with a 'Model evaluation error' because the underlying runtime cannot load the serialized model (e.g., pickle or joblib file). This is the most likely cause of a 400 error when the input format is otherwise correct.

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The input data format is incorrect

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The model was trained with a different framework

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The endpoint is overloaded and timing out

Question 14

A company wants to serve a large XGBoost model that exceeds the 2GB limit for Vertex AI Prediction. What should they do?

Accepted Answer

Use a custom container to serve the model. Vertex AI Prediction has a 2GB limit for the model artifact when using pre-built containers. A custom container bypasses this limit because you package the model and serving code into a Docker image, which can be arbitrarily large. This allows you to serve XGBoost models exceeding 2GB without size constraints imposed by the managed serving infrastructure.

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Reduce model size by removing features

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Compress the model using gzip and upload

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Deploy the model on Cloud Run Functions

Question 15

A company wants to reduce costs for serving a model on Vertex AI Prediction without sacrificing availability. Which THREE strategies should they consider?

Accepted Answer

Enable automatic batching to improve throughput per instance. Option C is correct because enabling automatic batching on Vertex AI Prediction allows the model server to group multiple inference requests into a single batch, which increases throughput per instance and reduces the total number of compute resources needed. This directly lowers serving costs without sacrificing availability, as the batching is handled transparently by the Vertex AI Prediction infrastructure.

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Use larger machine types to reduce the number of replicas

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Switch to HTTP/2 to reduce network overhead

Question 16

Your company uses a custom container for model serving on Vertex AI. After a recent update, the model returns predictions but they are clearly wrong (e.g., negative probabilities for a classification model). The logs show no errors. What is the most likely cause?

Accepted Answer

The preprocessing code in the container was updated but the model was not retrained on the new preprocessing. Option A is correct because the most likely cause of a model returning predictions without errors, but with clearly wrong outputs like negative probabilities, is a mismatch between the preprocessing logic used during training and inference. If the preprocessing code in the container was updated (e.g., scaling, normalization, or feature engineering steps changed) but the model was not retrained on data processed with that new logic, the model receives inputs that are out of distribution, leading to nonsensical outputs. Vertex AI containers run inference with the deployed code, so any change in preprocessing directly affects the input tensor values without raising runtime errors.

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The model file is corrupted

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The model file was accidentally replaced with a different model

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The container is using an incompatible version of the serving framework

Question 17

You are monitoring a machine learning pipeline that runs on Vertex AI Pipelines. The pipeline occasionally fails with a 'ResourceExhausted' error when attempting to read data from BigQuery. Which action should you take to resolve this issue?

Accepted Answer

Reduce the complexity of the BigQuery query or increase the reservation size. The 'ResourceExhausted' error when reading from BigQuery indicates that the query is consuming more resources than the BigQuery reservation allows. Option C is correct because reducing query complexity (e.g., using fewer JOINs, aggregations, or partitions) or increasing the reservation size directly addresses the root cause by either lowering resource demand or allocating more capacity. Other options like switching to Cloud Storage or adjusting pipeline memory do not fix the BigQuery-specific quota or slot exhaustion.

Answer

Switch from BigQuery to Cloud Storage for data source

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Increase the memory allocated to the pipeline step

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Reduce the batch size of the data being read

Question 18

An e-commerce company uses a recommendation model that suggests products based on user browsing history. The model was trained on data from the past year and has high accuracy on the test set. However, after deployment, the click-through rate (CTR) on recommendations is much lower than expected. Which three steps should the data scientist take to diagnose and improve the model? (Choose THREE)

Accepted Answer

Set up an A/B experiment comparing the model's recommendations against a baseline. Option B is correct because an A/B experiment directly measures the model's real-world impact by comparing its CTR against a baseline (e.g., random or popularity-based recommendations). This isolates the model's performance from confounding factors like seasonality or user behavior changes, providing a causal estimate of its effectiveness.

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Run offline evaluation on a holdout dataset to confirm accuracy

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Increase the training dataset size by including data from two years ago

Question 19

Which TWO metrics should you monitor to detect data drift in a batch prediction pipeline?

Accepted Answer

Feature distribution drift (e.g., KS test). Feature distribution drift (C) is correct because it directly measures changes in the input data distribution over time using statistical tests like the Kolmogorov-Smirnov (KS) test, which compares the cumulative distribution of a feature in the current batch against a reference baseline. This is a primary indicator of data drift, as shifts in feature distributions can degrade model performance even if labels are not immediately available.

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Model accuracy on recent labeled data

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Model prediction latency

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Training data size

Question 20

Which THREE components should you include in a comprehensive model monitoring dashboard for a production ML system?

Accepted Answer

System resource utilization (CPU, memory, latency). Option B is correct because system resource utilization metrics (CPU, memory, latency) are essential for monitoring the health and performance of the production infrastructure hosting the ML model. These metrics help detect resource bottlenecks, scaling issues, or degradation that could impact inference latency and throughput, which are critical for maintaining service-level objectives (SLOs).

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Team member roles and responsibilities

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Training pipeline code version