PMLE Practice Test 27 — 15 Questions

Question 1

An engineer is using TensorFlow Transform (tf.Transform) to preprocess training data. They want to ensure that the same preprocessing logic is applied during inference without code duplication. Which approach should they take?

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

Accepted Answer

Use tf.Transform to generate a transform_fn and save it as a SavedModel; then use tf.saved_model.load to apply it in the serving pipeline. TensorFlow Transform outputs a SavedModel that contains the preprocessing graph. This can be exported as a transform_fn and embedded in the serving model, ensuring consistency between training and serving.

Answer

Use tf.Transform at prediction time by running a separate Beam pipeline

Answer

Use Dataflow to preprocess data for both training and serving

Answer

Write separate preprocessing code for training and serving in Python

Question 2

Which API is recommended for high-throughput, low-latency online prediction requests to Vertex AI endpoints?

Accepted Answer

gRPC API. gRPC API is recommended for high-throughput, low-latency online prediction requests to Vertex AI endpoints because it uses HTTP/2 for multiplexed streaming, binary serialization (Protocol Buffers), and supports bidirectional streaming, which reduces latency and improves throughput compared to REST. Vertex AI's prediction service natively supports gRPC for real-time inference, making it the optimal choice for latency-sensitive applications.

Answer

Cloud Functions

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REST API

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Cloud Pub/Sub

Question 3

You have a champion model serving 100% traffic on a Vertex AI endpoint. You want to deploy a challenger model and gradually shift 10% of traffic to it for A/B testing. What is the correct approach?

Accepted Answer

Deploy the challenger on the same endpoint and use the traffic split parameter to allocate 10% traffic to it.. Vertex AI endpoints support traffic splitting by deploying multiple model versions and assigning traffic percentages. You deploy the challenger as a new deployed model on the same endpoint and set traffic split: champion 90%, challenger 10%.

Answer

Use Cloud Run to deploy both models and use Cloud Endpoints for traffic splitting.

Answer

Deploy the challenger on a separate endpoint and use Cloud Armor to split traffic.

Answer

Create a new endpoint for the challenger and route 10% of requests via a load balancer.

Question 4

A logistics company wants to classify shipping documents into categories (invoice, packing slip, bill of lading) using a custom model with minimal code. They have labeled training images. Which Google Cloud service is most appropriate?

Accepted Answer

AutoML Vision for image classification. AutoML Vision for image classification is designed to train custom image classification models with minimal code using labeled images. It automatically handles data preprocessing, model selection, and hyperparameter tuning, making it ideal for classifying shipping documents from images. The other services are either for tabular data, document extraction, or pre-trained label detection without custom training.

Answer

Vertex AI AutoML Tables

Answer

Document AI custom extractor

Answer

Cloud Vision API with label detection

Question 5

A healthcare organization is building a machine learning model to predict patient readmission risk. They have sensitive data stored in BigQuery that includes protected health information (PHI). The data science team uses Vertex AI Workbench notebooks to explore the data and develop models. The organization's security policy requires that all PHI data must be encrypted at rest and in transit, and that access to the data is logged and audited. They also need to ensure that the data used for model training is de-identified to remove direct identifiers such as patient names and SSNs. The team wants to automate the de-identification process as part of the data pipeline. Which approach meets these requirements?

Accepted Answer

Create a Dataflow pipeline that reads from the original BigQuery table, applies Cloud DLP de-identification transforms, and writes to a new BigQuery table. Grant the data science team access to the de-identified table.. It uses Cloud DLP within a Dataflow pipeline to automatically de-identify PHI data as it is read from the original BigQuery table and written to a new, de-identified table. This satisfies the requirement for automated de-identification, while the original table remains encrypted at rest (BigQuery default) and in transit (TLS), and access to the original data can be logged via Cloud Audit Logs. The data science team only gets access to the de-identified table, ensuring PHI is not exposed during model development.

Answer

Enable Shielded VM on Vertex AI Workbench notebooks and use VPC-SC to restrict data access.

Answer

Use Cloud Key Management Service to encrypt the PHI columns in BigQuery, and share the encryption key with the data science team.

Answer

Use BigQuery row-level security to mask PHI columns for the data science team, and train the model directly on the original table.

Question 6

An ML team is using Vertex AI to train a deep learning model on a large dataset. To reduce costs, they want to use preemptible VMs for training jobs. However, training must complete within a bounded time. Which strategy should they use?

Accepted Answer

Use Vertex AI Training with spot VMs and ensure the training code saves checkpoints periodically to Cloud Storage.. Vertex AI Training supports spot VMs (preemptible instances) for cost savings, and periodic checkpointing to Cloud Storage ensures that training can resume from the last saved state if a VM is preempted, allowing the job to complete within a bounded time despite interruptions.

Answer

Use Cloud TPU instead of GPU; TPUs are not preemptible.

Answer

Use Vertex AI Training without spot VMs, because preemptible VMs are not supported for training.

Answer

Use a single powerful non-preemptible VM to avoid interruptions.

Question 7

Match each ML acronym to its definition.

Accepted Answer

ML: A subset of AI that enables systems to learn from data.. The correct matches are A (ML), B (AI), C (NLP), and D (CNN). Options E and F are incorrect because they swap definitions: E assigns the definition of AI to ML, and F assigns the definition of CNN to NLP.

Answer

ML: The simulation of human intelligence by machines.

Answer

NLP: A type of neural network designed for processing grid-like data, especially images.

Question 8

A machine learning model deployed on Vertex AI is returning erroneous predictions. The team needs to investigate the root cause by examining the prediction request and response details. Which Google Cloud tool is best suited for this?

Accepted Answer

Cloud Logging. Cloud Logging is the correct tool because it captures detailed logs of prediction requests and responses, including input features, model outputs, and any errors. By examining these logs, the team can trace the exact data flow and identify discrepancies causing erroneous predictions, such as data preprocessing issues or model version mismatches.

Answer

Cloud Monitoring

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Cloud Debugger

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Cloud Trace

Question 9

A data engineer is troubleshooting a Vertex AI Endpoint that serves a large BERT model. After deployment, many prediction requests fail with 'Out of Memory' errors. The machine type is n1-standard-8 (30 GB memory) with no accelerator. Which action will most likely resolve the issue?

Accepted Answer

Change the machine type to n1-highmem-16 (104 GB memory).. The Out of Memory errors occur because the n1-standard-8 has only 30 GB RAM, which is insufficient to load the large BERT model plus runtime overhead. Switching to n1-highmem-16 provides 104 GB RAM, giving ample headroom for the model and inference batch, directly resolving the OOM condition.

Answer

Use batch prediction instead of online prediction.

Answer

Add a GPU accelerator (e.g., NVIDIA T4) to offload computation.

Answer

Quantize the model from FP32 to INT8.

Question 10

A model deployed on Vertex AI Prediction repeatedly exits with code 137. What is the most likely cause?

Accepted Answer

The model is using more memory than allocated (4GB).. Exit code 137 indicates that the container was killed by the Linux kernel's Out-Of-Memory (OOM) killer. In Vertex AI Prediction, each model deployment has a fixed memory allocation (default 4GB for custom containers). When the model's inference process exceeds this limit, the OOM killer terminates the container, resulting in exit code 137. This is the most direct and common cause for this specific exit code in Vertex AI.

Answer

The model has a disk I/O bottleneck.

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The model is using too much CPU.

Answer

The container image is incompatible with the machine type.

Question 11

A retailer uses BigQuery ML to build a linear regression model for sales forecasting. The model's evaluation shows high RMSE. Which step should they take first?

Accepted Answer

Examine the data for outliers and missing values. High RMSE in a linear regression model often indicates issues with data quality, such as outliers or missing values, which can disproportionately skew the model's predictions. BigQuery ML's linear regression is sensitive to such anomalies, so examining and cleaning the data is the most appropriate first step before considering model complexity or feature engineering.

Answer

Use a more complex model like XGBoost

Answer

Increase the number of features

Answer

Set a larger training budget

Question 12

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 13

You have deployed a regression model that predicts house prices. Over the past month, the model's predictions have been consistently too high. You suspect data drift in the input features. Which monitoring metric should you prioritize to confirm this?

Accepted Answer

Monitor feature distribution drift using a divergence metric like Jensen-Shannon divergence. The question describes a scenario where predictions are consistently too high, which is a symptom of data drift—a change in the distribution of input features. Monitoring feature distribution drift using a divergence metric like Jensen-Shannon divergence directly measures whether the input data has shifted from the training distribution, which would cause the model to make biased predictions. This is the most direct way to confirm data drift in the input features.

Answer

Monitor prediction drift (prediction distribution)

Answer

Monitor feature attribution drift using SHAP values

Answer

Monitor residual distribution drift

Question 14

You have an edge device with limited compute resources. You need to deploy a deep learning model for real-time inference. Which model compression technique should you apply to reduce the model size and latency with minimal accuracy loss?

Accepted Answer

Post-training quantization to INT8. Post-training quantization to INT8 reduces model weights and activations from 32-bit floats to 8-bit integers, cutting model size by roughly 4x and speeding up inference on edge hardware with minimal accuracy loss. It requires no retraining and works with most trained models. This makes it the best fit for limited-compute edge deployment.

Answer

Pruning only

Answer

Knowledge distillation only

Answer

Use full precision FP32 to maintain accuracy

Question 15

Refer to the exhibit. A data scientist runs this Vertex AI training job code. What will be the outcome?

Accepted Answer

A HyperparameterTuningJob is created and runs trials.. The code uses `HyperparameterTuningJob` with `parallel_trial_count=1` and `max_trial_count=10`. This creates a hyperparameter tuning job that runs up to 10 trials, each trial being a separate training run with different hyperparameter values. The `parallel_trial_count=1` means trials run sequentially, not in parallel, but this is valid and does not cause failure.

Answer

The job runs as a regular custom training with 10 replicas.

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A CustomJob is created with hyperparameters from the spec.

Answer

The job fails because parallel_trial_count cannot be less than max_trial_count.