PMLE Practice Exam 59 — 20 Questions

Question 1

A data science team is collaborating on a project to build a churn prediction model. They use Vertex AI Workbench instances for development. Each data scientist has their own instance with a persistent disk. They share code via a GitHub repository. They want to ensure that the model training is reproducible across different team members' environments. Currently, they manually install Python packages in their instances, and they have noticed that the model metrics differ slightly between runs on different instances. Which of the following is the best action to ensure reproducibility?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use Vertex AI Experiments with a fixed environment by specifying a prebuilt container.. Vertex AI Experiments with a prebuilt container ensures a fixed, reproducible environment by pinning the exact OS, Python version, and all dependencies. This eliminates the variability introduced by manual package installations and differing instance configurations, directly addressing the team's issue of inconsistent model metrics across runs.

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Standardize the instance machine type and ensure all have the same number of CPUs.

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Use Cloud Functions to run the training code instead.

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Create a custom Docker image with all dependencies and use it in Vertex AI Training jobs.

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Ask all team members to use the same Python virtual environment and install packages from a requirements.txt file.

Question 2

A company uses Vertex AI Model Monitoring to detect data drift. They have a model that predicts house prices. Which dataset should they compare against the training data to detect drift?

Accepted Answer

The latest batch of predictions. Vertex AI Model Monitoring compares the training data (serving as the baseline) against the latest batch of predictions to detect data drift. This batch represents the most recent inference requests, allowing the monitoring service to compute statistical distribution differences (e.g., Jensen-Shannon divergence) and trigger alerts when drift exceeds a configured threshold. Using the latest batch ensures timely detection of shifts in the production data distribution.

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The entire historical prediction data

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A random sample of recent predictions

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The validation data used during training

Question 3

A machine learning team deploys a PyTorch model for online prediction on Vertex AI using a custom container. They notice that the first few requests after scaling up experience high latency. What is the most likely cause and how should they mitigate it?

Accepted Answer

The container has a slow initialization; set initialDelaySeconds in the health probe to give more time before considering the pod ready.. The high latency on the first few requests after scaling up is a classic symptom of a slow container initialization. By setting `initialDelaySeconds` in the health probe, you allow the container more time to start up and become ready before it receives traffic, preventing premature routing that causes timeouts or retries. This is a common tuning parameter for custom containers on Vertex AI, where model loading or dependency initialization can take several seconds.

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The endpoint is not configured for autoscaling; enable min_replica=0 to allow scale-to-zero.

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The model file is corrupted; re-upload to Vertex AI Model Registry.

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Use a smaller machine type (n1-standard-2) to reduce startup overhead.

Question 4

An ML engineer needs to deploy a model to Vertex AI for online predictions and enable autoscaling to zero when not in use. Which THREE conditions must be met? (Choose 3)

Accepted Answer

Enable serverless serving by selecting the appropriate serving mode.. Vertex AI offers a serverless serving mode that automatically scales resources to zero when no requests are being processed. By selecting this mode, the ML engineer enables the endpoint to scale down completely during idle periods, eliminating costs for unused infrastructure. This is distinct from standard serving, which maintains a minimum number of replicas.

Answer

Use a GPU-enabled machine type for faster scaling.

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Deploy the model using a custom container with WebSockets support.

Question 5

A financial services company uses Document AI to process loan applications. They want to ensure that any documents the model cannot process with high confidence are reviewed by a human before finalizing the decision. Which Document AI feature should they enable?

Accepted Answer

Human-in-the-Loop (HITL). Human-in-the-Loop (HITL) allows documents with low confidence scores to be routed to human reviewers. This is a built-in feature of Document AI. AutoML Tables is not directly related. Cloud DLP is for data loss prevention.

Answer

AutoML Tables model retraining

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Cloud DLP for data inspection

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Increase the number of processors

Question 6

A team is serving a large language model (LLM) on Vertex AI using a custom container. They want to reduce tail latency. Which THREE strategies should they consider?

Accepted Answer

Use dynamic batching to combine requests.. Dynamic batching (B) reduces tail latency by grouping multiple inference requests into a single batch, which improves GPU utilization and amortizes overhead across requests. This is particularly effective for LLMs because it allows the model to process more tokens per forward pass, reducing the per-request latency variance that contributes to tail latency.

Answer

Increase the number of replicas.

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Upgrade to a more powerful GPU type.

Question 7

A data scientist wants to create a Vertex AI pipeline component that uses a custom container image stored in Artifact Registry. The component should accept a dataset artifact as input and output a model artifact. Which component type should they use?

Accepted Answer

A container component defined with the ContainerSpec class.. A container component defined with the `ContainerSpec` class is the only Vertex AI component type that allows you to specify a custom container image from Artifact Registry. This component type directly wraps a Docker container, enabling you to define inputs (e.g., a dataset artifact) and outputs (e.g., a model artifact) via the `ContainerSpec` interface, which maps to the container's command-line arguments and environment variables. Lightweight Python components and Python function components cannot use a custom container image without a base image, and pre-built components from GCPC are fixed and do not support custom containers.

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A Lightweight Python component without base_image.

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A Python function component with a custom base_image.

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A pre-built component from GCPC.

Question 8

A data science team wants to build a machine learning pipeline on Vertex AI Pipelines that preprocesses data, trains a model, and evaluates it. They need to ensure that components can be reused across multiple pipelines and that outputs from one component can be passed as inputs to another. Which approach should they take?

Accepted Answer

Use Kubeflow Pipelines SDK v2 to create Python function components decorated with @dsl.component and compose them into a pipeline using @dsl.pipeline.. Kubeflow Pipelines SDK v2 with @dsl.component and @dsl.pipeline decorators is the native way to define reusable, composable components in Vertex AI Pipelines. This approach allows each component to be a self-contained Python function that can be independently versioned and reused across multiple pipelines, with outputs automatically serialized and passed as inputs to downstream components via the pipeline graph.

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Write each component as a Cloud Composer DAG task using Python operators and manage dependencies via Airflow.

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Use Vertex AI pre-built components exclusively and chain them using the Vertex AI SDK without a pipeline definition.

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Define each step as a separate Cloud Build step and chain them via build triggers.

Question 9

Match each feature engineering technique to its description.

Accepted Answer

Polynomial Features: Creates new features as powers of existing features.. This matching question tests your understanding of common feature engineering techniques. Polynomial Features create new features by raising existing features to a power (e.g., x², x³). One-Hot Encoding converts categorical variables into binary columns (0/1) for each category. Feature Scaling standardizes numerical features to have zero mean and unit variance (z-score normalization). Options D and E are incorrect: D confuses Polynomial Features with Label Encoding (which assigns numerical codes), and E confuses One-Hot Encoding with Polynomial Features.

Answer

Polynomial Features: Converts categorical variables into numerical codes.

Answer

One-Hot Encoding: Creates polynomial combinations of features.

Question 10

A data engineering team wants to orchestrate an ML pipeline that includes data preprocessing in Dataflow, AutoML training, and model deployment. They want to minimize operational overhead. Which approach is best?

Accepted Answer

Use Vertex AI Pipelines with custom components. Vertex AI Pipelines with custom components is the best choice because it provides a fully managed, serverless orchestration service that natively integrates with Dataflow, AutoML, and model deployment. This minimizes operational overhead by eliminating the need to manage infrastructure, handle retries, or maintain a separate orchestration server, while offering built-in artifact tracking and pipeline caching.

Answer

Use Cloud Composer with Apache Airflow DAG

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Use AI Platform Training with script

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Use Cloud Scheduler to trigger Cloud Functions

Question 11

A team just moved a model from prototype to production using Vertex AI. They notice prediction errors for certain inputs that were not present in training data. What should they do to detect such issues automatically?

Accepted Answer

Enable Vertex AI Model Monitoring to detect prediction anomalies. Vertex AI Model Monitoring is specifically designed to detect prediction anomalies, such as data drift and feature skew, by comparing production prediction requests against the training data distribution. This allows the team to automatically identify inputs that deviate from the training data, even if those exact inputs were not present during training, without manual inspection.

Answer

Set up Vertex AI Experiments to compare predictions

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Use BigQuery ML to analyze prediction requests

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Enable Cloud Logging and set up alerts for error logs

Question 12

A pharmaceutical company uses Vertex AI Pipelines with custom training containers. Recently, the pipeline has been failing with 'Container failed with exit code 137' (out of memory). The container runs with default memory limit. The team needs to fix this without changing the code. The project quota for CPU and memory is sufficient. What should the team do?

Accepted Answer

Set the 'machineType' field for the training task to a higher memory machine.. The container is running out of memory (exit code 137) with the default memory limit. In Vertex AI Pipelines, when using custom training containers, the default memory allocation is typically 4 GiB. By setting the 'machineType' field to a higher memory machine (e.g., n1-highmem-8), the container automatically receives more memory without requiring code changes. This directly resolves the OOM issue while respecting the constraint of not modifying the code.

Answer

Add a resource hint to the container spec for more memory.

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Increase the model parallelism by using multi-worker training.

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Use a smaller dataset for training.

Question 13

A company wants to build a model to predict housing prices using BigQuery ML. They have a dataset with features like area, number of bedrooms, and location. Which TWO model types are appropriate for this regression task?

Accepted Answer

BOOSTED_TREE_REGRESSOR. BOOSTED_TREE_REGRESSOR (D) is appropriate because it is a tree-based ensemble method specifically designed for regression tasks, and BigQuery ML supports it via the `CREATE MODEL` statement with `model_type='BOOSTED_TREE_REGRESSOR'`. It handles non-linear relationships and interactions between features like area, bedrooms, and location, making it suitable for predicting continuous housing prices.

Answer

LOGISTIC_REG

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K_MEANS

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MATRIX_FACTORIZATION

Question 14

Match each regularization technique to its effect.

Accepted Answer

L1 Regularization: Encourages sparsity by setting some coefficients to zero. This question matches regularization techniques to their effects. L1 regularization (option A) encourages sparsity by setting some coefficients to zero, which is correct. Option E incorrectly states L1 shrinks coefficients uniformly; that is L2 regularization. L2 regularization (option B) shrinks coefficients to prevent overfitting, which is correct. Dropout (option C) randomly drops neurons during training to prevent co-adaptation, which is correct. Option F incorrectly states dropout adds penalty to the loss function; that describes regularization like L1/L2. Early stopping (option D) halts training when validation performance stops improving, which is correct.

Answer

L1 Regularization: Shrinks coefficients uniformly

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Dropout: Adds penalty to the loss function

Question 15

A company has an existing TensorFlow model for fraud detection that they want to use for predictions in BigQuery. They want to call the model from SQL queries without moving data out of BigQuery. How should they deploy the model?

Accepted Answer

Import the TensorFlow model directly into BigQuery ML. BigQuery ML (BQML) natively supports importing TensorFlow models directly, allowing you to use them for predictions via SQL without moving data out of BigQuery. This is the simplest and most efficient approach because it eliminates the need for external services or data export, leveraging BQML's built-in `CREATE MODEL` statement with the `OPTIONS(model_type = 'TENSORFLOW')` clause.

Answer

Deploy the model to Vertex AI Prediction and use a remote model in BigQuery ML

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Export BigQuery data to Cloud Storage and use AI Platform Prediction

Answer

Use AutoML Tables to retrain the model in BigQuery ML

Question 16

You are monitoring a production model that is experiencing gradual decay in AUC. Which THREE metrics should you set up alerts for to diagnose the root cause? (Choose three.)

Accepted Answer

Concept drift score measured by comparing predicted vs actual outcomes.. Concept drift directly measures the degradation of model performance by comparing predicted probabilities against actual outcomes over time. A gradual AUC decay indicates that the relationship between features and the target is shifting, and tracking concept drift via metrics like the PSI or distribution of residuals helps isolate whether the model's predictive power is eroding due to changing data patterns.

Answer

Average prediction latency over the past hour.

Answer

Model staleness (days since last retraining).

Question 17

An ML engineer is monitoring a Vertex AI Feature Store used for online serving. Which metrics are most important to track for ensuring low-latency online serving?

Accepted Answer

Feature value retrieval latency (p99) and error rate.. For online serving, the primary concern is the latency and reliability of feature value retrieval at inference time. The p99 retrieval latency directly measures the worst-case delay experienced by users, while the error rate captures failures that could cause serving disruptions. Other metrics like storage utilization or batch export duration are relevant for offline or batch pipelines, not real-time serving.

Answer

Number of feature stores and feature values.

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Storage utilization and write throughput to the feature store.

Answer

Batch export duration and number of exported features.

Question 18

Which TWO services are commonly used together to implement an end-to-end ML pipeline that automatically retrains and deploys models on Vertex AI? (Choose two.)

Accepted Answer

Vertex AI Pipelines. Vertex AI Pipelines (B) is the correct choice because it provides a serverless, scalable orchestration service specifically designed to build, run, and manage ML pipelines on Vertex AI. It enables you to define a directed acyclic graph (DAG) of steps—including data preprocessing, training, evaluation, and deployment—and can be triggered automatically to retrain and deploy models. Cloud Scheduler (E) is commonly used together with Vertex AI Pipelines to schedule pipeline runs at fixed intervals or in response to time-based triggers, forming a complete end-to-end automated retraining and deployment workflow.

Answer

Cloud Dataflow

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Cloud Composer

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Cloud Source Repositories

Question 19

A machine learning engineer needs to create a pipeline that runs a custom container component on Vertex AI. The container expects a Cloud Storage path as input and outputs a model artifact. Which component type should they define using the Kubeflow Pipelines SDK v2?

Accepted Answer

Container component using @dsl.container_component. The Kubeflow Pipelines SDK v2 provides the @dsl.container_component decorator specifically for defining components that wrap custom container images. This allows the engineer to specify the container image, input/output paths (like a Cloud Storage path), and artifact metadata, enabling Vertex AI to execute the container as a pipeline step and capture the model artifact.

Answer

Google Cloud Pipeline Components (GCPC) for custom containers

Answer

Python function component using @dsl.component

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Importer component to load the container as an artifact

Question 20

A retail company wants to forecast daily sales for inventory planning. They have 3 years of historical sales data with clear weekly and yearly seasonality. Which approach should they use?

Accepted Answer

Use BigQuery ML to train an ARIMA_PLUS model. ARIMA_PLUS in BigQuery ML is specifically designed for time-series forecasting with multiple seasonalities (weekly and yearly). It automatically handles seasonality detection, trend decomposition, and holiday effects, making it ideal for retail sales data with clear periodic patterns.

Answer

Call a pre-built Google Cloud API for sales prediction

Answer

Use a linear regression model in Vertex AI

Answer

Use Vertex AI AutoML Tables with date as feature