PMLE Practice Test 45 — 15 Questions

Question 1

You need to perform batch predictions on 10 TB of data stored in BigQuery using Vertex AI. The model requires some preprocessing that cannot be expressed in SQL. What is the most scalable approach?

Accepted Answer

Use Dataflow to read from BigQuery, perform preprocessing, write results to GCS, then run Vertex AI batch prediction job with GCS source.. Dataflow (Apache Beam) provides a fully managed, auto-scaling, serverless execution environment that can read from BigQuery, apply arbitrary Python/Java preprocessing logic (e.g., feature engineering, normalization) that cannot be expressed in SQL, and write the preprocessed results to Cloud Storage (GCS). Vertex AI batch prediction can then read from GCS as input, making this the most scalable approach for 10 TB of data without requiring custom model container changes or manual data movement.

Answer

Use a Cloud Function to preprocess each row and write to a new BigQuery table, then run batch prediction.

Answer

Use Vertex AI batch prediction with BigQuery source and include preprocessing logic in the model container.

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Export BigQuery data to CSV, run a local Python script for preprocessing, then upload to GCS and start a batch prediction job.

Question 2

You are deploying a pre-trained BERT model for inference on edge devices. The model must be under 500 MB and inference latency under 50 ms. Which approach should you take?

Accepted Answer

Apply post-training INT8 quantization using TensorFlow Lite. Post-training INT8 quantization reduces model size by approximately 75% (from ~440 MB to ~110 MB for BERT-Base) and accelerates inference on edge devices via integer arithmetic, easily meeting the 500 MB and 50 ms constraints. TensorFlow Lite provides hardware-optimized kernels for ARM CPUs and NPUs, making it ideal for edge deployment without requiring retraining.

Answer

Use a larger model like BERT-Large and deploy on GPU

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Prune 50% of the model weights and fine-tune

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Use knowledge distillation to train a smaller student model from scratch

Question 3

A team has deployed a model on Vertex AI Prediction and wants to monitor for data drift. Which TWO metrics should they use to detect drift in numerical features?

Accepted Answer

Jensen-Shannon divergence (JSD). Jensen-Shannon divergence (JSD) is a symmetric, bounded (0 to 1) measure of the difference between two probability distributions, making it ideal for detecting drift in numerical features by comparing the training distribution to the serving distribution. It is a smoothed and normalized version of Kullback-Leibler divergence, and Vertex AI Prediction's Model Monitoring natively supports JSD for numerical feature drift detection.

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Pearson correlation coefficient

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Chi-squared statistic

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Population Stability Index (PSI)

Question 4

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 5

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

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Use a linear regression model in Vertex AI

Answer

Use Vertex AI AutoML Tables with date as feature

Question 6

A company wants to use Vertex AI for hyperparameter tuning. Which three components are required to configure a hyperparameter tuning job? (Choose THREE.)

Accepted Answer

Algorithm (e.g., Bayesian, grid, random). Vertex AI hyperparameter tuning requires specifying the search algorithm (Bayesian, grid, or random) to determine how the hyperparameter space is explored. Bayesian optimization is the default and most efficient for continuous spaces, while grid search is exhaustive and random search is simple. Without an algorithm, Vertex AI cannot decide how to sample trials.

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Machine type for each trial

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Training container image

Question 7

A company has a prototype ML model that works well on historical data, but when deployed to production, the model performance degrades over time. The data distribution shifts gradually. Which strategy should they implement to maintain model accuracy?

Accepted Answer

Implement a retraining pipeline that periodically retrains the model on recent data.. Gradual data distribution shifts (concept drift) require the model to adapt to new patterns over time. A retraining pipeline that periodically retrains on recent data ensures the model remains aligned with the current production distribution, directly addressing the degradation caused by drift without relying on static historical data.

Answer

Increase the regularization strength to prevent overfitting.

Answer

Increase the amount of training data by using more historical records.

Answer

Switch to a more complex model architecture to better capture patterns.

Question 8

An ML engineer is monitoring a Vertex AI Endpoint and notices a spike in 5xx error rates. Which TWO metrics should they examine to diagnose the issue? (Choose 2)

Accepted Answer

GPU utilization on the endpoint. CPU/GPU utilization can indicate resource exhaustion causing errors. Prediction job failures metric directly shows failed predictions.

Answer

Feature drift alert count

Answer

Vertex AI Model Monitoring skew score

Answer

Number of predictions per minute

Question 9

Which THREE of the following are recommended practices for model governance and lineage in Vertex AI?

Accepted Answer

Enable Vertex AI ML Metadata to track artifacts, executions, and contexts.. Vertex AI ML Metadata is a fully managed service that automatically tracks artifacts, executions, and contexts across the ML workflow. By enabling it, you create a lineage graph that records every step from data preparation to model deployment, which is essential for auditability and reproducibility. This is a core recommended practice for model governance because it provides an immutable, queryable history of all model-related activities.

Answer

Store model artifacts in Cloud Storage with metadata in a database.

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Manually record model lineage in a spreadsheet.

Question 10

Which TWO are best practices for implementing a low-code ML solution using Vertex AI AutoML? (Choose 2)

Accepted Answer

Use the AutoML recommended data split (train/validation/test) to avoid overfitting.. AutoML's recommended data split (train/validation/test) is designed to prevent overfitting by ensuring the model is evaluated on unseen data. AutoML automatically handles the split ratio (e.g., 80/10/10) and stratification, which is a best practice for low-code ML solutions where manual split logic is error-prone.

Answer

Impute missing values manually before uploading the dataset.

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Normalize numerical features to zero mean and unit variance.

Answer

Export the data and train a custom model with a different architecture.

Question 11

A data scientist needs to train a time-series forecasting model on historical sales data stored in BigQuery to predict future demand. The data has strong seasonal patterns. Which BigQuery ML model type should they use?

Accepted Answer

ARIMA_PLUS. ARIMA_PLUS is the correct choice because it is specifically designed for time-series forecasting in BigQuery ML, handling seasonal patterns, trend decomposition, and automatic hyperparameter tuning. It models autoregressive (AR) and moving average (MA) components with seasonal differencing, making it ideal for historical sales data with strong seasonal cycles.

Answer

MATRIX_FACTORIZATION

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BOOSTED_TREE_REGRESSOR

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K_MEANS

Question 12

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 comparing the deployed model against a baseline (e.g., current production recommender or popularity-based recommendations) directly measures real-world CTR impact and isolates whether the model itself is underperforming versus other factors like UI placement or latency. Option C is correct because retraining on the most recent three months of data addresses temporal concept drift — user browsing behavior and product trends change quickly in e-commerce, so a model trained on a year-old distribution can have stale item embeddings and relevance signals even with high offline accuracy. Option D is correct because comparing the distribution of predictions (and input feature distributions) against the training set detects data drift and covariate shift, which commonly explain why offline test accuracy fails to translate into online CTR. Option A is not the best step because offline holdout evaluation was already performed and showed high accuracy, so repeating it won't reveal the deployment-time mismatch. Option E is not appropriate because adding two-year-old data would likely worsen drift by reinforcing outdated patterns rather than capturing recent trends.

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 13

An ML engineer needs to monitor a deployed model for data drift. They want to compare the distribution of incoming predictions against a baseline distribution. Which Vertex AI service should they use?

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Model Monitoring is the correct service because it is specifically designed to detect data drift and feature skew in deployed models. It continuously compares the distribution of incoming prediction requests against a baseline distribution (e.g., training data or a previous window) and alerts the engineer when statistically significant drift is detected, using metrics like Jensen-Shannon divergence or L-infinity distance.

Answer

Vertex AI Feature Store

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Vertex AI Experiments

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Vertex AI Explainable AI

Question 14

A company deploys a TensorFlow model on Vertex AI Prediction with a single node. During peak hours, inference latency increases. What should they do first to reduce latency?

Accepted Answer

Enable autoscaling for the deployment. Enabling autoscaling for the deployment is the correct first step because it allows Vertex AI Prediction to dynamically adjust the number of replicas based on incoming traffic. During peak hours, autoscaling can add more nodes to distribute the inference load, directly reducing latency without requiring manual intervention or over-provisioning.

Answer

Increase the machine type of the node

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Decrease the min replicas to 0

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Enable automatic batching of requests

Question 15

A team is monitoring a production ML system that includes multiple models and data processing pipelines. They want to set up a comprehensive alerting strategy that minimizes false positives while ensuring critical issues are promptly addressed. Which approach is the most effective?

Accepted Answer

Use AIOps with anomaly detection to dynamically adjust thresholds. AIOps with anomaly detection uses machine learning to dynamically adjust alert thresholds based on real-time system behavior, reducing false positives while ensuring critical issues are detected promptly. This approach adapts to changing data distributions and traffic patterns, unlike static thresholds that require manual tuning and often miss subtle anomalies. It is the most effective strategy for complex ML production systems where multiple models and pipelines interact, as it can correlate signals across components to identify genuine incidents.

Answer

Set up alerts for all possible error conditions

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Use static thresholds based on historical data

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Rely on manual monitoring during business hours