PMLE Practice Exam 4 — 20 Questions

Question 1

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.

Answer

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

Answer

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

Answer

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

Question 2

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.

Answer

Move the model to Cloud Functions

Answer

Use a GPU instance with a fixed number of replicas

Answer

Switch to a CPU-only machine type

Question 3

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.

Answer

The input data format is incorrect

Answer

The model was trained with a different framework

Answer

The endpoint is overloaded and timing out

Question 4

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.

Answer

Reduce model size by removing features

Answer

Compress the model using gzip and upload

Answer

Deploy the model on Cloud Run Functions

Question 5

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.

Answer

Use larger machine types to reduce the number of replicas

Answer

Switch to HTTP/2 to reduce network overhead

Question 6

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.

Answer

The model file is corrupted

Answer

The model file was accidentally replaced with a different model

Answer

The container is using an incompatible version of the serving framework

Question 7

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

Answer

Increase the memory allocated to the pipeline step

Answer

Reduce the batch size of the data being read

Question 8

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.

Answer

Run offline evaluation on a holdout dataset to confirm accuracy

Answer

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

Question 9

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.

Answer

Model accuracy on recent labeled data

Answer

Model prediction latency

Answer

Training data size

Question 10

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).

Answer

Team member roles and responsibilities

Answer

Training pipeline code version

Question 11

A hospital wants to deploy a machine learning model for detecting anomalies in patient vital signs. The model was trained on historical data but must comply with HIPAA regulations. The model serving must be low-latency (under 100 ms) and handle up to 1000 requests per second. Which architecture should they use on Google Cloud?

Accepted Answer

Deploy the model to Vertex AI Prediction with a private endpoint and use VPC Service Controls for data isolation. Vertex AI Prediction with a private endpoint and VPC Service Controls meets all requirements: it provides low-latency (sub-100ms) online predictions for up to 1000 QPS, enforces HIPAA compliance by isolating the model within a VPC and preventing data exfiltration, and supports autoscaling. Batch Prediction (A) cannot meet the latency requirement, BigQuery ML (B) is designed for analytical queries not real-time serving, and Cloud Run (C) lacks native HIPAA-compliant data isolation controls.

Answer

Use Vertex AI Batch Prediction to run predictions in batch jobs every hour

Answer

Use BigQuery ML to run predictions directly from a BigQuery table

Answer

Deploy the model as a container on Cloud Run with a load balancer

Question 12

A retail company wants to build a customer churn prediction model using BigQuery ML. The data is stored in BigQuery tables and includes customer demographics, purchase history, and support interactions. The data scientist wants to experiment with different model types quickly without moving data to another environment. Which approach should they use?

Accepted Answer

Use BigQuery ML to create and evaluate models directly in BigQuery.. BigQuery ML (BQML) allows data scientists to create, train, and evaluate machine learning models directly in BigQuery using SQL, without moving data to another environment. This approach supports rapid experimentation with various model types (e.g., logistic regression, boosted trees, deep neural networks) and is ideal for the stated requirement of quick iteration while keeping data in place.

Answer

Use Cloud Composer to orchestrate a custom training pipeline on Vertex AI.

Answer

Use AI Platform Notebooks with pandas and scikit-learn.

Answer

Export the data to Cloud Storage and use Vertex AI AutoML Tables.

Question 13

A company wants to implement a document processing solution that extracts key information from invoices and receipts. They have limited ML expertise and want to use a pre-trained solution as much as possible. Which Google Cloud service should they use?

Accepted Answer

Document AI with a pre-trained invoice processor.. Document AI with a pre-trained invoice processor is the correct choice because it provides a fully managed, pre-trained solution specifically designed for extracting structured data (e.g., vendor name, invoice number, line items) from invoices and receipts. This aligns with the company's limited ML expertise and desire to use a pre-trained solution, requiring no custom model training or complex coding.

Answer

AutoML Natural Language with custom entity extraction.

Answer

Vertex AI Workbench with custom Python scripts.

Answer

Cloud Vision API with OCR.

Question 14

A team has trained a sentiment analysis model using PyTorch on Vertex AI Training. They now want to deploy it for online predictions with low latency. Which TWO actions should they take? (Choose 2)

Accepted Answer

Use a machine type with a GPU for faster inference.. Option B is correct because GPU-accelerated inference significantly reduces latency for deep learning models like sentiment analysis, especially when using PyTorch, which has native CUDA support. Vertex AI Prediction supports GPU machine types (e.g., n1-standard-4 with NVIDIA T4) that can process batched requests faster than CPUs, directly addressing the low-latency requirement.

Answer

Create multiple model versions for A/B testing.

Answer

Enable batch prediction instead of online prediction.

Answer

Convert the model to TensorFlow SavedModel format.

Question 15

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?

Accepted Answer

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.

Answer

Drop more columns to reduce overfitting.

Answer

Increase the training data by including customers without churn dates.

Answer

Change model_type to 'BOOSTED_TREE_CLASSIFIER'.

Question 16

A data scientist wants to quickly train a binary classification model on a tabular dataset stored in BigQuery without writing any code. They have limited ML experience. Which Google Cloud service should they use?

Accepted Answer

BigQuery ML with CREATE MODEL statement using SQL.. Option C is correct because BigQuery ML allows a data scientist to train a binary classification model directly in BigQuery using a `CREATE MODEL` SQL statement, without writing any code or moving data. This is the fastest low-code approach for users with limited ML experience, as it leverages familiar SQL syntax and runs entirely within BigQuery's serverless infrastructure.

Answer

Vertex AI Workbench with a built-in scikit-learn notebook.

Answer

Dataflow with a TensorFlow pipeline.

Answer

AutoML Tables with a direct BigQuery connection.

Question 17

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?

Accepted Answer

Use the ML.PREDICT function with a lower classification threshold (e.g., 0.3 instead of 0.5) to capture more positive cases.. Option A is correct because lowering the classification threshold in ML.PREDICT (e.g., from 0.5 to 0.3) causes the model to classify more transactions as fraud, directly increasing recall. This is a low-code adjustment that does not require retraining or complex feature engineering, and it allows the data scientist to trade off precision for recall as needed.

Answer

Apply feature selection to reduce the number of features and focus on the most predictive ones.

Answer

Increase the number of training iterations by setting the MAX_ITERATIONS option to a higher value.

Answer

Re-train the model using AutoML Tables with class weights to penalize false negatives more heavily.

Question 18

A data science team uses BigQuery to store raw data and Vertex AI for model training. They want to ensure that only authorized users can access training data, and that model artifacts are automatically versioned and tracked. Which combination of Google Cloud services should they use?

Accepted Answer

Vertex AI Feature Store with access control and Vertex AI ML Metadata for model versioning. Vertex AI Feature Store provides fine-grained access control to training data, ensuring only authorized users can access it. Vertex AI ML Metadata automatically tracks and versions model artifacts, lineage, and parameters, which aligns with the requirement for automated versioning and tracking.

Answer

Dataflow for data access control and Vertex AI Experiments for model tracking

Answer

Cloud Storage with bucket-level IAM and Cloud Build for versioning

Answer

Cloud Composer for data access control and Cloud Source Repositories for model versioning

Question 19

Which TWO statements about Vertex AI Feature Store are correct? (Choose 2)

Accepted Answer

Feature Store provides a centralized repository for feature data.. Option D is correct because Vertex AI Feature Store is designed as a centralized repository that organizes, stores, and serves feature data consistently across different models and pipelines. This centralization ensures feature reuse, consistency, and governance, preventing data silos and duplication across the ML lifecycle.

Answer

Feature Store automatically applies feature engineering transformations.

Answer

Feature Store can only store numerical features.

Answer

Feature Store can only be used with Vertex AI models.

Question 20

Which THREE actions are best practices for managing ML models in production on Google Cloud? (Choose 3)

Accepted Answer

Monitor model performance and data drift continuously.. Option B is correct because continuous monitoring of model performance and data drift is essential for maintaining prediction accuracy in production. Google Cloud's Vertex AI Model Monitoring automatically detects skew and drift by comparing serving data against training data distributions, alerting you to degradation before it impacts business outcomes.

Answer

Manually tune hyperparameters for each retraining run.

Answer

Store all raw training data indefinitely for auditability.