PMLE Practice Test 42 — 15 Questions

Question 1

A team wants to implement CI/CD for their ML pipeline using Cloud Build. They want to automatically compile and deploy the pipeline when code is pushed to the main branch. Which three steps should they include in the Cloud Build configuration? (Choose three.)

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

Accepted Answer

Create or update the pipeline in Vertex AI using the compiled file. Option D is correct because the Cloud Build configuration must first set up the environment by installing the Kubeflow Pipelines (KFP) SDK and then compile the pipeline definition into a compiled YAML/JSON artifact, which is the essential build step for a CI/CD ML pipeline. Option B is correct because the compiled pipeline artifact needs to be uploaded to a Cloud Storage bucket so it can be referenced and used by Vertex AI when creating or updating the pipeline. Option A is correct because the final deployment step is to create or update the pipeline in Vertex AI using the compiled file, which is the actual CD action that makes the pipeline available in Vertex AI Pipelines. Option C is not correct because running the pipeline immediately after deployment is not a required CI/CD configuration step; deployment and execution are separate concerns, and the scenario only asks to compile and deploy on push. Option E is not correct because Cloud Scheduler is used for time-based triggering, whereas the scenario requires triggering on code push to the main branch, which is handled by Cloud Build triggers, not Cloud Scheduler.

Answer

Run the pipeline immediately after deployment

Answer

Configure Cloud Scheduler to trigger on push

Question 2

A company trains a model using Vertex AI Training and then deploys it to Vertex AI Prediction. They notice that prediction requests fail with 'InvalidArgument: input tensor shape mismatch'. Which THREE are possible causes?

Accepted Answer

The input data types do not match the expected types (e.g., float vs int). Option C is correct because Vertex AI Prediction validates request tensors against the model's signature, and a dtype mismatch such as sending int32 where the signature expects float32 raises InvalidArgument due to incompatible tensor types. Option D is correct because the input tensor's feature dimension must match the model's expected input shape; supplying a different number of features produces the shape mismatch error. Option E is correct because if the serving function omits the preprocessing applied during training (for example, normalization, tokenization, or reshaping), the raw request tensor will not match the shape the model's signature expects, triggering the same error. Option A is not the cause here because an unsupported export format would typically fail at model upload or deployment with a format/import error rather than at prediction time with a tensor shape mismatch. Option B is not the cause because an oversized batch size generally results in resource or memory errors, not an InvalidArgument tensor shape mismatch.

Answer

The model was exported in a different format than supported

Answer

The batch size in the request is too large

Question 3

A company deploys a model on Vertex AI Prediction with autoscaling enabled. They notice that during a traffic spike, new instances take several minutes to become available, causing high latency. What is the best solution?

Accepted Answer

Set a higher min replicas to maintain a baseline of warm instances. Setting a higher min replicas ensures that a baseline number of instances are always warm and ready to serve traffic. During a traffic spike, new instances still take time to provision (cold start), but the warm instances handle the initial surge without latency spikes. This directly addresses the observed high latency during spikes.

Answer

Disable autoscaling and use a fixed number of replicas

Answer

Increase the max replicas setting

Answer

Decrease the machine type to reduce provisioning time

Question 4

You have an online prediction model that is showing increasing prediction latency. You have already verified that the request rate and input data size are unchanged. Which of the following should you investigate next?

Accepted Answer

Check if the model was recently updated to a larger version. If request rate and input data size are unchanged, increased prediction latency often points to a change in the model itself. A larger model (e.g., deeper neural network, more parameters) requires more computation per inference, directly increasing latency. This is a common root cause when monitoring ML pipelines, as model version updates can silently alter performance characteristics.

Answer

Check the monitoring dashboard configuration

Answer

Check if the feature engineering logic was changed

Answer

Check the geographic location of the endpoint

Question 5

A data scientist needs to forecast daily sales for the next 30 days using historical sales data stored in BigQuery. They want to use BigQuery ML. Which model type should they choose?

Accepted Answer

ARIMA_PLUS. ARIMA_PLUS is the correct choice because it is specifically designed for time-series forecasting, such as predicting daily sales over a future horizon. BigQuery ML's ARIMA_PLUS model automatically handles seasonality, trend, and holiday effects, making it ideal for 30-day sales forecasts from historical data.

Answer

LINEAR_REG

Answer

BOOSTED_TREE_REGRESSOR

Answer

K_MEANS

Question 6

A company deploys a model on Vertex AI Endpoints for real-time inference. They notice latency spikes during peak hours. Which action is most effective to reduce latency without sacrificing accuracy?

Accepted Answer

Enable autoscaling based on CPU utilization. Latency spikes during peak hours indicate the endpoint is under-provisioned for concurrent load. Enabling autoscaling based on CPU utilization (or a custom metric like request count per replica) lets Vertex AI add replicas as demand rises, absorbing the spike without changing the model or sacrificing accuracy. This is the most direct and effective action for peak-hour latency.

Answer

Use a larger machine type

Answer

Reduce model size by pruning

Answer

Implement client-side caching

Question 7

The exhibit shows a Vertex AI PipelineJob submission command. The pipeline fails because the component cannot find the input data. What is the most likely cause?

Accepted Answer

The input data path is not accessible by the Vertex AI Pipelines service account. The most likely cause of the pipeline failing to find input data is that the Vertex AI Pipelines service account lacks the necessary permissions to access the specified input data path. Vertex AI Pipelines uses the Compute Engine default service account (or a custom service account) to read data from Cloud Storage or other sources; if this account does not have the `storage.objectViewer` role (or equivalent) on the bucket or object, the component will fail with a permission-denied error, even if the path is syntactically correct.

Answer

The pipeline root path is incorrect

Answer

The pipeline name is misspelled

Answer

The region does not support the component

Question 8

You have a TensorFlow training script that runs on a single machine. To speed up training on Vertex AI with 8 GPUs on a single machine, which strategy should you use?

Accepted Answer

tf.distribute.MirroredStrategy. tf.distribute.MirroredStrategy is designed for synchronous, data-parallel training across multiple GPUs on a single machine. It replicates the model on each GPU, splits each batch across replicas, and uses all-reduce (via NCCL) to aggregate gradients, which is exactly the scenario described: 8 GPUs on one machine. This is the canonical strategy for single-node multi-GPU TensorFlow training.

Answer

tf.distribute.ParameterServerStrategy

Answer

tf.distribute.TPUStrategy

Answer

tf.distribute.MultiWorkerMirroredStrategy

Question 9

A company uses Vertex AI Matching Engine for real-time recommendations. They need to serve queries with low latency and support frequent updates. Which two configurations are appropriate? (Choose 2)

Accepted Answer

Enable streaming updates for the index. Option B is correct because Vertex AI Matching Engine supports streaming updates, which allow the index to be modified incrementally as new data arrives without requiring a full rebuild, directly satisfying the requirement for frequent updates in a real-time recommendation system. Option D is correct because deploying the index to a Vertex AI Matching Engine endpoint is the standard mechanism for serving low-latency, real-time nearest-neighbor queries at scale, which is exactly what the scenario demands. Option A is not appropriate because storing the index in Cloud Storage and querying it via Python does not provide the managed, low-latency serving infrastructure of Matching Engine. Option C is not appropriate because a brute-force index performs exhaustive comparisons, which is far too slow for low-latency real-time serving at scale. Option E is not appropriate because batch-only updates cannot keep pace with frequent updates and would introduce staleness in a real-time recommendation system.

Answer

Store the index in Cloud Storage and query via Python

Answer

Use a brute-force index for exact results

Answer

Use batch updates only

Question 10

Your team is training a very large transformer model that does not fit on a single GPU. They are using Vertex AI custom training with PyTorch. Which distributed training approach should they use?

Accepted Answer

Model parallelism using pipeline parallelism. When a transformer model is too large to fit on a single GPU, model parallelism (specifically pipeline parallelism) is required because it splits the model's layers across multiple devices, with each device holding a subset of the model's parameters. Data parallelism (DDP) replicates the entire model on each GPU, which fails if the model exceeds a single GPU's memory. Pipeline parallelism allows training very large models by partitioning the model into stages and passing activations and gradients sequentially between devices.

Answer

Data parallelism using PyTorch DistributedDataParallel (DDP)

Answer

Horovod with allreduce

Answer

Vertex AI distributed training with TF_CONFIG

Question 11

A real-time recommendation model deployed on Vertex AI Endpoints is experiencing increased latency, especially during peak hours. The model is hosted on a single machine with 4 CPUs. Which set of actions should you take to diagnose and resolve the issue?

Accepted Answer

Enable autoscaling on the endpoint and analyze request patterns to set min/max instances.. Enabling autoscaling on a Vertex AI Endpoint allows the deployment to dynamically adjust the number of serving instances based on real-time traffic, directly addressing peak-hour latency. Analyzing request patterns to set appropriate min/max instances ensures that the endpoint scales proactively without over-provisioning, which is the standard diagnostic and resolution approach for latency issues caused by insufficient capacity under variable load.

Answer

Increase the machine type to with 32 CPUs and disable autoscaling.

Answer

Switch the endpoint to use GPUs and enable batch requests.

Answer

Change the serving framework to use TensorFlow Serving with gRPC.

Question 12

Which TWO statements about Vertex AI Feature Store are correct? (Choose 2)

Accepted Answer

Feature Store provides a centralized repository for feature data.. Option D is correct because Vertex AI Feature Store acts as a centralized repository where feature data is registered, versioned, and managed as feature groups and features, so multiple teams and models can share a single consistent source of features. Option E is correct because Feature Store supports both online serving, which returns low-latency feature values for real-time predictions, and offline serving, which reads historical feature values in bulk for training and batch scoring. Options A, B, and C are incorrect: Feature Store does not automatically perform feature engineering transformations (you define and ingest the transformed values yourself), it is not limited to numerical features (it supports types such as strings, booleans, and arrays as well), and it is not restricted to Vertex AI models since features can be served to any application or model via the online/offline APIs.

Answer

Feature Store automatically applies feature engineering transformations.

Answer

Feature Store can only store numerical features.

Answer

Feature Store can only be used with Vertex AI models.

Question 13

You are using tf.Transform to preprocess data at scale. Which TWO services are required to run tf.Transform on Google Cloud? (Choose 2)

Accepted Answer

Dataflow. tf.Transform requires Apache Beam for execution, which on GCP is typically run on Dataflow. The processed data and transform artifacts are stored in Cloud Storage.

Answer

Cloud Functions

Answer

Vertex AI Training

Answer

BigQuery

Question 14

An MLOps engineer needs to collect ground truth labels for a deployed classification model to compare predictions against actuals. Where should the engineer store the ground truth data to enable Vertex AI model quality monitoring?

Accepted Answer

BigQuery. Vertex AI Model Monitoring for model quality requires ground truth labels to be stored in BigQuery. The monitoring job compares the model's online predictions (logged to BigQuery via prediction logging) against the actual observed labels, which must also reside in BigQuery so the service can join them on a shared key column. BigQuery is the only supported sink for ground truth data in Vertex AI model quality monitoring.

Answer

Firestore

Answer

Cloud Spanner

Answer

Cloud Storage

Question 15

A company has deployed a fraud detection model on Vertex AI Prediction. After three months, the model's accuracy has degraded, and the business is losing money due to undetected fraud. What should the team implement to proactively detect such issues?

Accepted Answer

Enable Vertex AI Model Monitoring to track prediction drift and alert when metrics exceed thresholds.. Vertex AI Model Monitoring tracks prediction drift and alerts when metrics exceed thresholds, enabling proactive detection of model degradation. Option B is wrong because Cloud Logging captures requests and responses but does not automatically detect drift, requiring manual review. Option C is wrong because shuffling training data does not help detect drift. Option D is wrong because scheduling retraining without monitoring cannot proactively detect issues before they cause loss.

Answer

Set up Cloud Logging to capture all prediction requests and responses for manual review.

Answer

Randomly shuffle the training data before retraining to improve robustness.

Answer

Schedule a monthly job to retrain the model with the latest data without monitoring.