PMLE Practice Test 43 — 15 Questions

Question 1

A company wants to use Document AI to process a large volume of invoices. They need to extract line items and also have a human review the extracted data for accuracy. Which THREE features should they use? (Choose 3)

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

Accepted Answer

Invoice Parser Processor. The Invoice Parser Processor (C) is a specialized Document AI processor designed to extract structured data from invoices, including line items, which directly meets the requirement for extracting line items from a large volume of invoices. It is pre-trained on invoice layouts and can handle complex table structures, making it the correct choice for this task.

Answer

Online Processing

Answer

Custom Extractor

Question 2

A machine learning team wants to share features across multiple models to reduce training-serving skew and ensure consistency. Which Vertex AI service should they use?

Accepted Answer

Vertex AI Feature Store. Vertex AI Feature Store centralizes feature storage, ensuring the same features are used for training and serving, reducing training-serving skew.

Answer

Vertex AI Workbench

Answer

Vertex AI Model Registry

Answer

Vertex AI Experiments

Question 3

A data scientist wants to log prediction inputs and outputs for model monitoring. Which Google Cloud service is best suited for this?

Accepted Answer

Cloud Logging. Cloud Logging is the best choice because it is designed to ingest, store, and analyze log data, including custom log entries from applications. The data scientist can use the Cloud Logging API to write structured log entries containing prediction inputs and outputs, then query them using Logs Explorer or export them for further analysis. This aligns with the requirement to log prediction inputs and outputs for model monitoring, as Cloud Logging provides a centralized, scalable, and queryable log management service.

Answer

Cloud Monitoring

Answer

Cloud Storage

Answer

BigQuery

Question 4

A data science team has trained a custom model using Vertex AI and wants to deploy it for online predictions with low latency. Which TWO actions should they take to optimize performance?

Accepted Answer

Optimize the model by quantizing to FP16.. Quantizing the model to FP16 reduces its memory footprint and computational requirements, directly lowering inference latency on compatible hardware (e.g., NVIDIA GPUs with Tensor Cores). This optimization is especially effective for online predictions where response time is critical, as it accelerates matrix operations without significantly sacrificing model accuracy.

Answer

Use Vertex AI Endpoints with traffic splitting for canary deployments.

Answer

Enable autoscaling with a large min replicas count to handle bursts.

Answer

Use a machine type with GPU for inference.

Question 5

A logistics company uses a regression model to predict delivery times. The model currently uses features: distance (km), traffic index, weather condition, and time of day. The data scientist notices that the model's predictions are systematically too low for deliveries during peak traffic hours. Which action would best address this issue?

Accepted Answer

Add a cross-feature that multiplies distance by traffic index. The model's systematic underestimation during peak traffic hours indicates a missing interaction effect between distance and traffic. Adding a cross-feature (distance × traffic index) allows a linear model to capture the non-linear relationship where traffic disproportionately increases delivery time over longer distances. This directly addresses the bias without discarding useful data or unnecessarily complicating the model.

Answer

Switch to a deep neural network model

Answer

Remove the traffic index feature as it is causing bias

Answer

Collect more training data during peak traffic hours

Question 6

A company has multiple teams working on different models. They want to enforce consistent data preprocessing steps across all teams. Which approach should they take?

Accepted Answer

Create shared Vertex AI Pipelines components. Vertex AI Pipelines components allow teams to define reusable, versioned, and parameterized preprocessing steps that can be shared across models and pipelines. This ensures consistent execution of data transformations because each component encapsulates the exact code and environment, and pipelines enforce the same DAG of steps regardless of which team triggers them.

Answer

Use Cloud Composer to orchestrate preprocessing

Answer

Write shared Python packages in Artifact Registry

Answer

Use Cloud Dataflow templates

Question 7

You are designing a batch prediction pipeline using Vertex AI. The input data is 50 TB in CSV format on GCS. The model requires feature engineering that involves complex transformations (e.g., datetime parsing, one-hot encoding). Which TWO services or steps should you include in your pipeline?

Accepted Answer

Run Vertex AI batch prediction job with GCS source pointing to the processed TFRecord files.. The pipeline requires two main steps: first, use Dataflow to read CSV files, perform feature engineering, and write the processed data as TFRecords to Cloud Storage; second, run a Vertex AI batch prediction job with the GCS source pointing to these TFRecords. Cloud Functions and Cloud SQL are not suitable for handling 50TB of data. Writing to BigQuery is not appropriate for batch prediction with Vertex AI, which expects TFRecord or JSON Lines input.

Answer

Use Cloud Functions to transform each file individually.

Answer

Use Cloud SQL to store intermediate results.

Answer

Use Dataflow to read CSV, perform feature engineering, and write to BigQuery.

Question 8

A global e-commerce company uses BigQuery ML to forecast daily sales for 10,000 products. They use a time-series model with a horizon of 7 days. Recently, forecasts for a specific product category have been consistently too high. They suspect the model is not capturing a new seasonal pattern. Which action should they take first to diagnose the issue?

Accepted Answer

Run ML.EVALUATE on the recent sales data and compare accuracy metrics. Running ML.EVALUATE on recent sales data allows you to compute accuracy metrics (e.g., MAE, MAPE) specifically for the period where the model is failing. This isolates whether the error is due to a new seasonal pattern or another cause, without retraining or changing the model architecture. It is the standard first diagnostic step in BigQuery ML for time-series models.

Answer

Retrain the model with minimal additional data

Answer

Increase the forecast horizon to 14 days

Answer

Switch to AutoML forecasting via Vertex AI AutoML

Question 9

A mobile app company needs to run an image classification model on-device for real-time performance. The model is a ResNet-50 trained in TensorFlow. They need to reduce latency to under 50ms on a mid-range phone. Which optimization should they apply first?

Accepted Answer

Quantize the model weights to 8-bit integers. Quantizing the model weights to 8-bit integers (option B) is the most effective first optimization because it directly reduces the model size by 4x and leverages integer-arithmetic acceleration on mobile CPUs/GPUs, often cutting inference latency by 2-3x without requiring architectural changes. This is the standard first step for on-device deployment of TensorFlow models, as it preserves the ResNet-50 accuracy while meeting the 50ms target on mid-range hardware.

Answer

Convert the model to TensorFlow Lite

Answer

Replace ResNet-50 with MobileNet

Answer

Apply weight pruning to remove 50% of connections

Question 10

A company is deploying a TensorFlow model on Vertex AI Prediction. The model is memory-intensive and requires GPU acceleration. The team wants to minimize latency and cost. Which TWO configurations should they select? (Select 2)

Accepted Answer

Use NVIDIA T4 GPUs. For GPU-accelerated prediction, the T4 GPU is cost-effective and provides good performance for inference. Autoscaling with a minimum of 1 instance ensures availability while allowing the service to scale down when not in use. A100 is more expensive; batch prediction is for asynchronous large-scale jobs; manual scaling may lead to over-provisioning.

Answer

Use NVIDIA A100 GPUs for faster inference

Answer

Set manual machine count to 10 for consistent performance

Answer

Use batch prediction to reduce cost

Question 11

Which THREE of the following are valid best practices when using Vertex AI AutoML for tabular data?

Accepted Answer

Enable automatic feature engineering to improve model performance. Enabling automatic feature engineering in Vertex AI AutoML for tabular data allows the service to automatically create, select, and transform features (e.g., polynomial combinations, cross features, and numerical transformations) to improve model accuracy without manual intervention. This is a built-in capability that leverages Google's AutoML algorithms to discover the most predictive feature representations from the raw data.

Answer

Normalize the data into multiple tables to reduce data size

Answer

Keep training data with heavy class imbalance as-is to let AutoML correct it

Question 12

An ML engineer is configuring Vertex AI Model Monitoring for drift detection on a deployed endpoint. Which TWO settings directly affect the frequency and accuracy of drift detection? (Choose 2)

Accepted Answer

Sampling rate. Sampling rate controls what fraction of predictions is analyzed; monitoring frequency controls how often the distribution comparison is performed. Both directly impact detection speed and accuracy.

Answer

Model version

Answer

Explanation method

Answer

Alerting threshold

Question 13

A data scientist wants to share a trained model with the team for review before deployment. The model is stored in Vertex AI Model Registry. What is the recommended way to grant the team read access to the model?

Accepted Answer

Grant the IAM role 'roles/aiplatform.viewer' to the team members on the project.. The 'roles/aiplatform.viewer' IAM role grants read-only access to Vertex AI resources, including models in the Model Registry. Option A is incorrect because 'roles/aiplatform.admin' grants full administrative access, which is too broad for read-only needs. Option B is wrong because exporting the model and sharing via a shared drive bypasses version control and security best practices. Option D is incorrect because Cloud Storage bucket ACLs control access to the underlying bucket, not to the Vertex AI Model Registry; the model is managed through Vertex AI IAM.

Answer

Grant the IAM role 'roles/aiplatform.admin' to the team members.

Answer

Export the model as a local file and share it via a shared drive.

Answer

Add the team members to the Cloud Storage bucket ACL with 'READER' access.

Question 14

A retail company wants to build a product recommendation system using customer purchase history and product attributes. They have limited ML expertise and want to minimize custom code. Which approach should they choose?

Accepted Answer

Use Vertex AI AutoML Tables to train a recommendation model.. Vertex AI AutoML Tables is the correct choice because it enables building a recommendation model with minimal ML expertise and custom code, leveraging automated feature engineering, model selection, and hyperparameter tuning on tabular data (customer purchase history and product attributes). It requires no custom code, unlike TensorFlow/Keras, and provides a managed service that handles data preprocessing and training, aligning with the company's limited ML expertise and desire to minimize custom code.

Answer

Use BigQuery ML to create a matrix factorization model.

Answer

Use Vertex AI Vizier for hyperparameter tuning on a pre-built recommendation model.

Answer

Use TensorFlow with Keras to build a custom collaborative filtering model.

Question 15

You are responsible for deploying a real-time recommendation model that uses a large embedding table (5 GB) and a small neural network. The model is served through a custom container on Vertex AI Prediction. The end-to-end latency requirement is under 200 ms. During load testing with 500 QPS, you observe that latency increases linearly with batch size. You are currently using a single replica with an n1-standard-8 machine and one T4 GPU. The embedding table is loaded entirely in GPU memory. However, CPU utilization is at 100% while GPU is at 30%. What is the best approach to meet the latency requirement at scale?

Accepted Answer

Use a machine type with more CPU cores to parallelize embedding lookups.. CPU is the bottleneck; using a machine type with more CPU cores (e.g., n1-highcpu-16) allows parallel embedding lookups and reduces latency. Option A increases resources but not in the bottleneck area. Option B increases replicas but each would still be CPU-bound. Option C is expensive and may not improve latency if model not T PU-compatible.

Answer

Increase the number of replicas and use a global load balancer to distribute traffic.

Answer

Use a custom container that partitions the embedding table across multiple GPUs within a single replica.

Answer

Switch to a TPU v2-8 pod slice to accelerate embedding lookups.