PMLE Practice Test 2 — 25 Questions

Question 1

A logistics company uses Vertex AI AutoML Tables to predict delivery delays based on order attributes, weather data, and traffic data. The model is retrained weekly using a Vertex AI Pipeline that runs a BigQuery query to get training data, then triggers AutoML training. Recently, the pipeline fails with the error 'Dataset not found' when the AutoML training step starts. The BigQuery query runs successfully and outputs a table. Which is the most likely cause?

Accepted Answer

The BigQuery output table is not being passed as a Vertex AI Dataset resource.. The error 'Dataset not found' occurs because AutoML Tables requires a Vertex AI Dataset resource (a metadata wrapper) to reference the training data, not just a BigQuery table. The pipeline's BigQuery query produces a table, but if that table is not explicitly converted into or passed as a Vertex AI Dataset resource (via the `aiplatform.Dataset` creation step), AutoML training cannot locate it. Option D correctly identifies this missing step as the root cause.

Answer

The AutoML training step is referencing a different dataset location.

Answer

The training data has been manually deleted from Cloud Storage.

Answer

The pipeline's IAM permissions are insufficient to access BigQuery.

Question 2

A data analyst wants to build a binary classification model to predict customer churn using SQL queries in BigQuery. Which BigQuery ML model type should they use?

Accepted Answer

LOGISTIC_REG. BigQuery ML supports LOGISTIC_REG for binary classification via SQL. LINEAR_REG is for regression, K_MEANS for clustering, and MATRIX_FACTORIZATION for recommendations.

Answer

MATRIX_FACTORIZATION

Answer

LINEAR_REG

Answer

K_MEANS

Question 3

A financial services company has deployed a classification model on Vertex AI to detect fraudulent transactions. The model is monitored using Vertex AI Model Monitoring for skew and drift detection, and also logs predictions to BigQuery for analysis. After a month, the monitoring alerts show a significant drift in one feature (transaction_amount). Which TWO actions should the team take to diagnose and address this issue?

Accepted Answer

Compare the feature distribution in the training data with the recent serving data using statistical tests.. Option A is correct because comparing the feature distribution of the training data with recent serving data using statistical tests (e.g., Kolmogorov-Smirnov or Jensen-Shannon divergence) is the standard first step to quantify the drift and confirm it is statistically significant. This diagnostic action helps the team understand the nature and magnitude of the drift before deciding on remediation steps. Vertex AI Model Monitoring already performs such comparisons, but the team should independently verify the results in BigQuery to ensure accuracy.

Answer

Increase the frequency of model monitoring checks to every hour.

Answer

Increase the sampling rate for prediction logging to ensure full data capture.

Answer

Reduce the alert threshold to minimize false positives.

Question 4

A company needs to reduce inference latency for their online prediction service on Vertex AI. Which two actions would help? (Choose 2)

Accepted Answer

Deploy the model on a GPU-enabled machine. Option B is correct because deploying the model on a GPU-enabled machine significantly accelerates matrix operations and parallel computations inherent in deep learning inference, directly reducing per-request latency. Option C is correct because model quantization reduces the precision of model weights (e.g., from FP32 to INT8), which decreases memory footprint and speeds up computation, especially on compatible hardware like TPUs or GPUs.

Answer

Increase the maximum number of replicas

Answer

Use a smaller machine type with less memory

Answer

Enable autoscaling with a lower target CPU utilization

Question 5

A company is deploying a new model version to an existing Vertex AI endpoint. They want to test the new version with 5% of traffic before fully rolling it out. What is the correct approach?

Accepted Answer

Deploy the new version as a separate model on the same endpoint and use the `traffic_split` parameter in the deployment request.. Option D is correct because Vertex AI endpoints support traffic splitting between multiple deployed models. By deploying the new model version to the same endpoint and setting `traffic_split` to 5% for the new version and 95% for the existing version, the endpoint automatically routes a corresponding proportion of inference requests to each model without any client-side changes.

Answer

Create a new endpoint for the new version and update the client to call both endpoints.

Answer

Deploy the new version and set the minimum replicas to 0, then gradually increase.

Answer

Use Cloud Load Balancing to distribute traffic between two endpoints.

Question 6

Which Vertex AI service is best suited for finding similar items in a large dataset based on embedding vectors, such as product recommendations or image similarity search?

Accepted Answer

Vertex AI Matching Engine. Vertex AI Matching Engine is specifically designed for high-performance vector similarity search (also known as approximate nearest neighbor search) using embedding vectors. It scales to billions of vectors and is ideal for use cases like product recommendations and image similarity search, where you need to find the most similar items based on dense vector representations.

Answer

Vertex AI Prediction Endpoint

Answer

Vertex AI Model Monitoring

Answer

Vertex AI Feature Store

Question 7

A company uses Vertex AI for online predictions with a large ensemble model that requires GPU acceleration. They want to reduce inference latency by batching multiple requests into a single GPU inference call. What should they configure?

Accepted Answer

Deploy the model with NVIDIA Triton Inference Server configured for dynamic batching.. NVIDIA Triton Inference Server supports dynamic batching, which automatically groups multiple inference requests into a single GPU call. This reduces overhead and improves GPU utilization, directly addressing the need to lower latency for online predictions with a large ensemble model on Vertex AI.

Answer

Use Vertex AI Model Optimization for automatic compilation.

Answer

Increase the number of GPU replicas to handle higher concurrency.

Answer

Enable model quantization using TensorRT.

Question 8

An application serving predictions from a Vertex AI endpoint receives many identical requests within a short time window. The team notices redundant computation and wants to cache responses to reduce latency and cost. What is the recommended solution?

Accepted Answer

Implement a cache layer using Cloud Memorystore for Redis, hashing prediction requests.. Option C is correct because Vertex AI does not provide built-in request caching; instead, the recommended pattern is to implement an external cache like Cloud Memorystore for Redis. By hashing the prediction request payload and using it as a cache key, identical requests within the short time window can be served from Redis, eliminating redundant model inference and reducing both latency and cost.

Answer

Deploy the model on a larger machine type to handle duplicate requests faster.

Answer

Enable Vertex AI endpoint caching by setting the `enable_cache` flag.

Answer

Use Cloud CDN in front of the endpoint.

Question 9

A team is deploying a model that has strict latency requirements: p99 response time under 100 ms. The model is CPU-only and will receive up to 1000 QPS. They want to minimize cost while meeting the SLO. Which machine type and scaling configuration is most appropriate?

Accepted Answer

n1-standard-4 with min_replicas=1 and max_replicas=5, CPU utilization target 60%. Option D is correct because it uses a CPU-only machine (n1-standard-4) with autoscaling based on CPU utilization target of 60%, which balances cost and performance for a latency-sensitive, CPU-bound inference workload at 1000 QPS. The min_replicas=1 ensures a baseline capacity, while max_replicas=5 allows scaling to handle spikes without over-provisioning, keeping p99 under 100 ms.

Answer

GPU-enabled machine with min_replicas=1 and max_replicas=2

Answer

n1-standard-8 with min_replicas=3 and max_replicas=3 (fixed)

Answer

n1-highmem-2 with min_replicas=2 and max_replicas=10

Question 10

A team is building a batch prediction pipeline that processes raw data from Cloud Storage, performs complex preprocessing, and then runs predictions using a large model. The preprocessing step is compute-intensive and the prediction step is I/O-bound. Which TWO Google Cloud services should they combine to optimize cost and performance? (Choose 2)

Accepted Answer

Dataflow for preprocessing and writing results to Cloud Storage. Dataflow is ideal for the compute-intensive preprocessing step because it can horizontally scale across many workers to handle complex transformations in parallel, and it writes results directly to Cloud Storage, which serves as the input source for Vertex AI Batch Prediction. Vertex AI Batch Prediction is optimized for I/O-bound inference workloads: it reads batches of data from Cloud Storage, runs predictions using the large model, and writes results back to Cloud Storage, all without requiring a persistent serving endpoint, which minimizes cost for offline predictions.

Answer

Cloud Functions to preprocess data row by row

Answer

Cloud Run to serve the preprocessed data as an API

Answer

Vertex AI Batch Prediction with BigQuery source

Question 11

A company runs batch predictions on Vertex AI every hour using a custom container. They want to reduce costs by minimizing idle time while ensuring the batch job completes within 10 minutes. Which endpoint configuration should they use?

Accepted Answer

Use Vertex AI batch prediction job with a custom service account and set machine_type to 'n1-standard-4' and batch_size to optimize throughput.. Option B is correct because Vertex AI batch prediction jobs are designed for high-throughput, asynchronous processing of large datasets without maintaining persistent infrastructure. By tuning `machine_type` and `batch_size`, you can minimize idle time and ensure the job completes within the 10-minute window, as the job only runs while actively processing and scales resources as needed.

Answer

Use Vertex AI online prediction with a load balancer in front to distribute requests.

Answer

Create a Dataflow pipeline to read from BigQuery and write predictions to GCS, using the trained model as a side input.

Answer

Deploy the model to an endpoint with min_replicas=0 and max_replicas=10, then send batch requests to the endpoint.

Question 12

A company is migrating from an on-premises ML serving infrastructure to Vertex AI. They have multiple models that need to be served from the same endpoint with different traffic percentages. They also need to monitor prediction quality. Which THREE actions should they take? (Choose 3)

Accepted Answer

Deploy multiple model versions on the same endpoint with traffic_split parameter.. Option A is correct because Vertex AI endpoints support deploying multiple model versions and using the `traffic_split` parameter to distribute traffic percentages among them. This allows the company to serve different models from a single endpoint while controlling the proportion of requests each model receives, meeting the requirement for a unified serving infrastructure.

Answer

Deploy each model as a separate endpoint and use Cloud Load Balancing.

Answer

Export logs to BigQuery for manual analysis only.

Question 13

A fintech company needs to deploy a TensorFlow model for real-time fraud detection with strict latency SLO (p99 < 100ms). They expect variable traffic with spikes. They also want to minimize cold-start latency. Which two configurations should they use? (Choose 2)

Accepted Answer

Use a GPU-enabled machine type (e.g., N1 with T4) to accelerate inference.. Option B is correct because GPU-enabled machine types (e.g., N1 with T4) significantly accelerate TensorFlow model inference, which is critical for meeting the p99 < 100ms latency SLO. GPUs parallelize matrix operations common in deep learning models, reducing per-request latency even under variable traffic spikes.

Answer

Set min_replicas = 0 to allow scale-to-zero and save costs.

Answer

Enable Vertex AI Model Optimization for automatic quantization.

Answer

Use batch prediction instead of online prediction.

Question 14

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.. Option C is correct because 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.

Answer

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

Answer

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

Answer

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

Question 15

A machine learning engineer is building a Vertex AI pipeline that uses a pre-built AutoML Tables component to train a classification model. The pipeline also includes a conditional step that deploys the model to an endpoint only if the evaluation metrics exceed a threshold. Which KFP feature should be used to implement the conditional deployment?

Accepted Answer

dsl.Condition. The `dsl.Condition` feature from KFP (Kubeflow Pipelines) is specifically designed to conditionally execute pipeline steps based on the output of a previous component. In this scenario, the AutoML Tables component produces evaluation metrics; `dsl.Condition` allows the pipeline to check whether those metrics exceed a threshold and, if true, run the deployment step. This is the correct, native KFP construct for implementing branching logic within a pipeline.

Answer

dsl.ParallelFor

Answer

dsl.ExitHandler

Answer

dsl.Collected

Question 16

A team is using Vertex AI Pipelines to deploy a model. They have a component that evaluates the model and produces a ClassificationMetrics artifact. The pipeline should deploy the model only if the precision is greater than 0.9. They use dsl.If to check the metric. However, the condition always evaluates to False. What is the most likely cause?

Accepted Answer

The ClassificationMetrics artifact is not accessible in the condition context.. Option C is correct because in Vertex AI Pipelines, `ClassificationMetrics` artifacts are not directly accessible as primitive values within the `dsl.If` condition context. The `dsl.If` condition can only evaluate pipeline parameters or primitive outputs (like strings, integers, floats) that are explicitly passed as pipeline-level parameters or task outputs. A `ClassificationMetrics` artifact is a complex object that must be parsed or have its specific metric values extracted (e.g., via a custom component or `dsl.Metrics`) before they can be used in a conditional check.

Answer

The precision value is stored as a float but the condition expects a string.

Answer

The evaluation component did not output the metric correctly.

Answer

The dsl.If block is placed incorrectly in the pipeline definition.

Question 17

A team has a Vertex AI pipeline that includes a container component for data preprocessing. The team notices that the component is re-executed every time the pipeline runs, even when the inputs and code haven't changed. They want to leverage pipeline caching to avoid redundant executions. What should they do to enable caching for this component?

Accepted Answer

Ensure that the component does not have 'dsl.cache_options(enable_cache=False)' set.. Option D is correct because Vertex AI pipeline caching is enabled by default for all components unless explicitly disabled using `dsl.cache_options(enable_cache=False)`. The component re-executing every time indicates that caching was likely disabled on that specific component. Removing or ensuring this setting is not present will allow the pipeline to reuse cached outputs when inputs and code have not changed.

Answer

Set the 'caching' flag to 'True' in the pipeline definition using 'pipeline.caching = True'.

Answer

Set the environment variable 'ENABLE_CACHE' to 'true' on the pipeline run request.

Answer

Re-compile the pipeline with the '--enable-cache' flag.

Question 18

You are designing an ML pipeline for a large-scale recommendation system that runs weekly retraining on historical user interaction data. The pipeline uses TensorFlow and is deployed on Google Cloud. The pipeline must be orchestrated and automated with minimal manual intervention. Which THREE options should you include in your design? (Choose three.)

Accepted Answer

Use Vertex AI Pipelines to define the ML pipeline as a Directed Acyclic Graph (DAG) of components.. Vertex AI Pipelines (option B) is correct because it provides a managed, serverless orchestration service for building, testing, and deploying ML pipelines as Directed Acyclic Graphs (DAGs). This directly supports the requirement for automated, minimal-intervention weekly retraining by allowing you to define reusable components and schedule pipeline runs via Cloud Scheduler or event triggers, integrating natively with TensorFlow and Google Cloud services.

Answer

Use BigQuery scheduled queries to run the training script on a schedule.

Answer

Use AI Platform Notebooks to schedule the training job on a recurring basis.

Question 19

You have a Vertex AI pipeline that trains a model and outputs a Model artifact. You want to register this model in the Vertex AI Model Registry. Which pre-built Google Cloud Pipeline Components component should you use?

Accepted Answer

VertexModelUploadOp. The correct component is VertexModelUploadOp because it is specifically designed to upload a trained model artifact to the Vertex AI Model Registry, creating a new model version or a new model if one does not exist. This component takes the model artifact from a pipeline step and registers it, making it available for deployment or version management.

Answer

VertexEndpointDeployOp

Answer

CreateModelVersionsOp

Answer

ModelRegisterOp

Question 20

A machine learning team uses Vertex AI Pipelines to orchestrate their training pipeline. They want to trigger the pipeline automatically in response to new data arriving in a Cloud Storage bucket, and also support a scheduled run every day at 6 AM. Which combination of services should they use to achieve both event-driven and schedule-based triggers?

Accepted Answer

Cloud Scheduler for the schedule, and Cloud Functions triggered by Cloud Storage events to call the Vertex AI API for event-driven.. Option C is correct because Cloud Scheduler can trigger the pipeline at 6 AM daily via a cron job, while Cloud Functions, triggered by Cloud Storage events (e.g., object finalize), can call the Vertex AI API to start the pipeline when new data arrives. This combination provides both schedule-based and event-driven triggers without requiring custom infrastructure.

Answer

Cloud Scheduler for the schedule, and Cloud Pub/Sub with Push subscription to Vertex AI for event-driven.

Answer

Cloud Functions for both schedule and event-driven, using cron trigger.

Answer

Vertex AI Pipelines built-in scheduler for schedule, and Cloud Pub/Sub for event-driven.