PMLE Practice Test 39 — 15 Questions

Question 1

A company wants to transcribe audio from customer service calls and then analyze the sentiment of the transcribed text. Which TWO Google Cloud services should they use?

Accepted Answer

Natural Language API. Speech-to-Text (option C) is correct because it converts audio from the customer service calls into written text, which is the required transcription step. Natural Language API (option A) is correct because it performs sentiment analysis on text, allowing the company to analyze the sentiment of the transcribed call content. Document AI (option B) is not appropriate here because it processes documents and forms rather than audio or general sentiment analysis. Translation API (option D) only translates text between languages and does not transcribe audio or analyze sentiment. Vision API (option E) analyzes images, so it cannot handle audio transcription or text sentiment analysis.

Answer

Document AI

Answer

Translation API

Answer

Vision API

Question 2

A data analyst wants to train a binary classification model in BigQuery ML on a dataset of 10 million rows with 50 features. They need to evaluate the model's performance on a held-out test set. Which sequence of SQL statements should they run?

Accepted Answer

CREATE MODEL then ML.EVALUATE. To train and evaluate a model in BigQuery ML, you use CREATE MODEL to train the model, and then ML.EVALUATE to assess its performance on a held-out dataset. CREATE MODEL automatically splits the data into training and evaluation sets if specified, or you can use a separate table for evaluation. ML.EVALUATE returns metrics like accuracy, precision, recall, etc. This sequence is standard for model development in BigQuery ML.

Answer

CREATE MODEL then ML.FEATURE_IMPORTANCE

Answer

ML.TRAIN then ML.EVALUATE

Answer

CREATE MODEL then ML.PREDICT

Question 3

A company uses Vertex AI Feature Store for feature engineering. They need to ensure point-in-time correctness to avoid data leakage during training. Which feature retrieval method should they use?

Accepted Answer

Use the offline store with point-in-time join using the `feature_view` with a timestamp column.. Point-in-time correctness requires retrieving feature values as they existed at the timestamp of each training example, which is exactly what the offline store's point-in-time join does when a feature_view is configured with an event/timestamp column. Vertex AI Feature Store uses this timestamp column to perform an as-of join, preventing future data from leaking into the training row. The online store and timestamp-less get_features calls return only the latest values, which is the classic source of label leakage.

Answer

Use the `get_features` API without specifying a timestamp.

Answer

Use BigQuery to manually join features with a sliding window.

Answer

Use the online store to retrieve the latest feature values.

Question 4

You want to reduce training costs by using preemptible VMs on Vertex AI for a fault-tolerant distributed training job that uses checkpointing. Which machine type should you choose in the worker pool configuration?

Accepted Answer

Use spot VMs by setting 'spot' to true in the machine spec. Vertex AI supports spot VMs (the successor to preemptible VMs) by setting the 'spot' field to true in the machine spec of the worker pool. Spot VMs offer up to 60-91% discounts and are suitable for fault-tolerant jobs with checkpointing, since they can be preempted with 30 seconds' notice. This is the documented, supported configuration for cost-optimized training.

Answer

Use custom machine types with preemptible flag

Answer

Use standard VMs and rely on Vertex AI auto-restart

Answer

Use TPU VMs because they are cheaper

Question 5

You are fine-tuning a BERT model from Hugging Face Transformers on Vertex AI. You want to minimise cost for a short experiment. Which compute configuration should you use?

Accepted Answer

A custom training job with a single NVIDIA T4 GPU using spot VMs. A single NVIDIA T4 GPU with spot VMs is the most cost-effective choice for a short BERT fine-tuning experiment on Vertex AI. T4 GPUs are inexpensive and well-suited for moderate training workloads, and spot VMs offer up to 60-70% discount over regular VMs. Since the experiment is short, the risk of preemption is acceptable, and the cost savings are significant. This configuration balances performance and cost effectively.

Answer

A custom training job with a TPU v3-8 pod

Answer

A custom training job with 8 NVIDIA V100 GPUs using regular VMs

Answer

A standard n1-highmem-8 machine with no accelerator

Question 6

A team wants to use Vertex AI Workbench for collaborative notebook development. They need a persistent environment that can be stopped and restarted without losing installed packages and data. Which instance type should they choose?

Accepted Answer

User-managed notebooks. User-managed notebooks are Compute Engine instances where the user controls the environment, including installed packages, kernel state, and persistent disk contents. Because the underlying VM and its boot/data disks persist across stop and start operations, installed packages and data remain intact. This makes them the right choice for a persistent, customizable environment.

Answer

Managed notebooks

Answer

Colab Enterprise notebooks

Answer

Vertex AI Pipelines

Question 7

An engineer wants to use BigQuery ML to explain predictions from a trained boosted tree classifier for a specific set of input rows. Which function should they use?

Accepted Answer

ML.EXPLAIN_PREDICT. ML.EXPLAIN_PREDICT is the BigQuery ML function specifically designed to explain predictions from a trained model for a given set of input rows. It returns the prediction along with feature attributions (e.g., Shapley values) that show how each feature contributed to the prediction. This is the correct function for interpreting individual predictions from a boosted tree classifier.

Answer

ML.EVALUATE

Answer

ML.FEATURE_IMPORTANCE

Answer

ML.PREDICT

Question 8

A team is building a CI/CD pipeline for ML using Cloud Build. The pipeline trains a model and deploys it to Vertex AI. Recently, a change in the data processing step caused the model to be trained with a different data version, leading to a failed deployment because the model was invalid. How should the team prevent this in the future?

Accepted Answer

Use a data versioning tool (e.g., DVC) to track datasets and ensure the pipeline always uses the correct version. The root cause is a data version mismatch, not a code or environment issue. A data versioning tool like DVC (Data Version Control) tracks dataset versions via hash-based pointers in Git, ensuring the pipeline retrieves the exact dataset version used during training. This prevents silent failures when data processing steps change the data schema or content, which library pinning or manual reviews cannot guarantee.

Answer

Add a manual review step before training

Answer

Pin all library versions in the Docker image

Answer

Schedule a cron job to check for data changes

Question 9

Which TWO are best practices when deploying AutoML models to production?

Accepted Answer

Monitor for data drift. Monitoring for data drift (Option A) is a best practice because production models can degrade over time as the statistical properties of input data change. Vertex AI provides a Model Monitoring service that automatically detects skew and drift by comparing serving data distribution against training data distribution, triggering alerts when anomaly thresholds are breached. This ensures model reliability and performance in production.

Answer

Train the model on a disk to reduce latency

Answer

Deploy on sole-tenant nodes

Answer

Use TPUs for model serving

Question 10

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 deploying a PyTorch model for low-latency online predictions on Vertex AI benefits from GPU-backed machine types, which accelerate the matrix/tensor computations of deep neural network inference and reduce per-request latency. Option E is correct because Vertex AI custom containers let you serve a PyTorch model with your own web server (e.g., FastAPI) implementing the required predict/health routes, which is the standard way to deploy non-TensorFlow frameworks for online prediction. Option A is not required for low latency; model versions and A/B testing address traffic splitting and evaluation, not inference speed. Option C is wrong because batch prediction is for asynchronous, bulk scoring and does not provide the low-latency online endpoint requested. Option D is wrong because converting a PyTorch model to TensorFlow SavedModel is unnecessary and would require a framework conversion rather than serving the trained PyTorch artifact.

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 11

Which TWO are best practices for building ML pipelines on Vertex AI Pipelines?

Accepted Answer

Use a container-based approach for each component. Option C is correct because Vertex AI Pipelines executes each pipeline step as a containerized component, so packaging each component as a Docker container (with its dependencies and code) ensures reproducibility, portability, and consistent execution across environments. Option D is correct because Vertex AI Pipelines is built on Kubeflow Pipelines, and the Kubeflow Pipelines SDK (the `kfp` package, e.g., `@dsl.pipeline` and `@component` decorators) is the supported, idiomatic way to define, compile, and submit pipelines to Vertex AI. Option A is wrong because storing models without versioning breaks lineage, rollback, and reproducibility; models should be registered in Vertex AI Model Registry with versioning. Option B is wrong because Cloud Build is a CI/CD build service, not a pipeline orchestrator for ML workflows. Option E is wrong because Cloud Composer (managed Apache Airflow) is a general workflow orchestrator, not the primary tool for authoring and running Vertex AI Pipelines.

Answer

Store all trained models in Cloud Storage without versioning

Answer

Use Cloud Build as the pipeline orchestrator

Answer

Use Cloud Composer as the primary pipeline tool

Question 12

An ML team wants to monitor their recommendation model for fairness. Which TWO metrics should they track to detect potential bias? (Select TWO.)

Accepted Answer

Pair-wise fairness metrics such as equal opportunity difference.. Option A is correct because pair-wise fairness metrics such as equal opportunity difference directly compare model performance (e.g., true positive rates) between groups defined by a sensitive attribute, which is the standard way to quantify disparate impact and bias. Option E is correct because examining the prediction distribution (e.g., top-K recommendations) across sensitive attribute groups reveals whether the model systematically under- or over-represents certain groups in its outputs, a key bias signal for recommender systems. Option B is not sufficient because tracking recall for only the minority group provides no comparison baseline, so it cannot by itself detect bias relative to other groups. Option C is not appropriate because overall accuracy on the test set can mask large disparities between groups and is not a fairness metric. Option D is not appropriate because average prediction confidence per request measures model certainty, not equitable treatment or outcomes across sensitive groups.

Answer

Recall for the minority group only.

Answer

Overall accuracy on the test set.

Answer

Average prediction confidence per request.

Question 13

A team is using Vertex AI Pipelines to automate their ML workflow. They want to ensure that pipeline runs are reproducible and that artifacts are tracked. Which feature should they use?

Accepted Answer

Vertex AI Experiments. Vertex AI Experiments is the correct feature because it captures parameters, metrics, and artifacts for each pipeline run, enabling reproducibility and lineage tracking. This directly supports the team's need to ensure runs are reproducible and artifacts are tracked, as Experiments automatically logs metadata for every execution.

Answer

Vertex AI Feature Store

Answer

Vertex AI Model Registry

Answer

Vertex AI Endpoints

Question 14

A team is using Vertex AI Explainability with a deployed model. They need to generate explanations for image classification predictions. Which explanation method should they configure in the ExplanationSpec?

Accepted Answer

XRAI. XRAI (eXplainable AI) is Google Cloud's explanation method specifically designed for image classification models, producing saliency-based heatmaps that highlight regions of the image contributing to the prediction. It is the recommended method in Vertex AI ExplanationSpec for image data because it outperforms simpler gradient methods on visual tasks.

Answer

SHAP with KernelExplainer

Answer

Sampled Shapley

Answer

Integrated Gradients

Question 15

An ML team is using Population Stability Index (PSI) to monitor feature drift on a Vertex AI Endpoint. The PSI value for a feature is 0.25, which exceeds the alert threshold of 0.2. The feature has high SHAP importance. The team wants to automatically retrain the model. What is the correct end-to-end setup?

Accepted Answer

Cloud Monitoring alert → Pub/Sub → Cloud Functions → Vertex AI Pipeline (with training and deployment steps). The correct end-to-end flow is: Vertex AI Model Monitoring detects the PSI threshold breach and emits a metric to Cloud Monitoring; a Cloud Monitoring alerting policy fires and publishes to a Pub/Sub topic; a Cloud Function subscribes and triggers a Vertex AI Pipeline that retrains and redeploys the model. This chain uses the native monitoring-to-automation path on GCP.

Answer

Vertex AI Model Monitoring alert → Cloud Scheduler → Vertex AI Training

Answer

Cloud Functions → Pub/Sub → Cloud Monitoring → Vertex AI Pipeline

Answer

Cloud Monitoring alert → Cloud Logging → Cloud Functions → Vertex AI Training