PMLE Practice Test 49 — 15 Questions

Question 1

An ML team is using Vertex AI Pipelines to run automated retraining workflows. They want to monitor pipeline execution and receive alerts when a pipeline run fails. Which Google Cloud service should they use to set up such alerts?

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

Accepted Answer

Cloud Monitoring. Cloud Monitoring (formerly Stackdriver Monitoring) is the correct service because it provides alerting policies that can be triggered based on pipeline run status metrics, such as failure counts or run state changes. Vertex AI Pipelines automatically exports execution metrics to Cloud Monitoring, allowing you to define conditions (e.g., metric 'pipeline/run_count' with filter 'status=FAILED') and configure notifications via channels like email, Pub/Sub, or PagerDuty.

Answer

Vertex AI Metadata

Answer

Cloud Logging

Answer

Cloud Scheduler

Question 2

A company deploys a custom TensorFlow model to Vertex AI Endpoint for online predictions. After deployment, prediction latency is consistently high (over 500ms) even under low traffic. The model is CPU-only and the default machine type (n1-standard-2) is used. Which action will most likely reduce prediction latency?

Accepted Answer

Change the machine type to n1-highcpu-16 with a GPU accelerator.. Changing the machine type to n1-highcpu-16 with a GPU accelerator provides significantly more compute resources for the custom TensorFlow model. The n1-highcpu-16 offers 16 vCPUs (vs. 2 in n1-standard-2), which reduces CPU-bound inference time, and adding a GPU accelerates matrix operations common in TensorFlow models, directly reducing latency per request. Option A is wrong because increasing max_replica_count allows more parallel requests but does not improve the processing time of a single request. Option C is wrong because setting min_replica_count ensures always-on capacity to avoid cold starts, but does not reduce steady-state latency. Option D is wrong because increasing batch size in the prediction request increases throughput by processing multiple inputs together, but does not reduce latency for a single prediction—it may actually increase the time to return a result for a given request.

Answer

Increase the max_replica_count to 10 to allow more parallel requests.

Answer

Set min_replica_count to 3 to ensure always-on capacity.

Answer

Increase the batch size in the prediction request.

Question 3

Which TWO options can help detect model performance degradation in production? (Choose two.)

Accepted Answer

Vertex AI Model Monitoring (drift detection). The correct answers are D and E. Vertex AI Model Monitoring (D) detects drift in input features, which can indicate model performance degradation. Storing predictions in BigQuery and comparing with ground truth (E) directly measures performance over time. Option A (Vertex AI Experiments) is for training and experimentation, not for monitoring production models. Option B (Cloud Logging for prediction errors) logs errors but does not directly detect degradation. Option C (Cloud Monitoring custom metrics from serving logs) can monitor infrastructure metrics like latency or error rates, but it does not directly measure model performance degradation.

Answer

Vertex AI Experiments on historical data

Answer

Cloud Logging for prediction errors

Answer

Cloud Monitoring custom metrics from serving logs

Question 4

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). Vertex AI Prediction expects the input tensor data types to exactly match those used during model training. If the model was trained with float32 inputs but the prediction request sends int32 values, the serving infrastructure detects the mismatch and returns an 'InvalidArgument: input tensor shape mismatch' error, as TensorFlow Serving (which underlies Vertex AI Prediction) validates dtype consistency at the graph level.

Answer

The model was exported in a different format than supported

Answer

The batch size in the request is too large

Question 5

A healthcare organization wants to build a model to predict patient readmission risk using structured electronic health record (EHR) data. They need to train a model using SQL in BigQuery, but they also want to leverage AutoML's ability to automatically search for the best architecture. Which approach should they take?

Accepted Answer

Use BigQuery ML with the AUTOML_CLASSIFIER model type. BigQuery ML's AUTOML_CLASSIFIER model type automatically performs architecture search and hyperparameter tuning, making it ideal for users who want to leverage AutoML capabilities directly within SQL on structured EHR data. This approach avoids manual model selection while staying entirely within BigQuery's SQL interface, which is the stated requirement.

Answer

Use a pre-built Vision API model via BigQuery ML remote model

Answer

Use AutoML Tables with Vertex AI and export predictions

Answer

Use BigQuery ML with a DNN_CLASSIFIER and manual hyperparameter tuning

Question 6

An engineer needs to compile a Kubeflow Pipeline defined in Python to a JSON format that can be run on Vertex AI Pipelines. Which command should they use?

Accepted Answer

kfp.compiler.Compiler().compile(pipeline_func, 'pipeline.json'). The Kubeflow Pipelines SDK provides the `kfp.compiler.Compiler().compile()` method to convert a Python-based pipeline function into a JSON or YAML format that is compatible with Vertex AI Pipelines. This JSON representation defines the pipeline's components, dependencies, and execution graph, enabling it to be submitted to Vertex AI for orchestration. The `compile()` method is the standard way to produce a portable pipeline specification from Python code.

Answer

gcloud ai pipelines compile command.

Answer

kfp.Client().upload_pipeline()

Answer

dsl.pipeline decorator automatically compiles at runtime.

Question 7

Which TWO practices are important when scaling a prototype ML model to production on Google Cloud? (Choose two.)

Accepted Answer

Set up model monitoring for data drift and concept drift. Model monitoring for data drift and concept drift is essential in production ML on Google Cloud. Services like Vertex AI Model Monitoring automatically track feature distributions and prediction quality over time, alerting when the statistical properties of incoming data deviate from the training baseline. Without this, a model's accuracy can silently degrade as real-world data shifts, leading to poor business decisions.

Answer

Manually engineer features for each training iteration

Answer

Run the model on a single high-memory Compute Engine VM

Answer

Use proprietary libraries to maximize performance regardless of lock-in

Question 8

Which THREE actions should be taken to automate a machine learning pipeline using Cloud Build and Vertex AI?

Accepted Answer

Write a cloudbuild.yaml that builds a training container and submits a Vertex AI PipelineJob. Cloud Build's cloudbuild.yaml can define a step that builds a custom training container and submits it as a Vertex AI PipelineJob. This directly automates the ML pipeline by using Cloud Build to trigger a Vertex AI pipeline, which is the recommended pattern for CI/CD of ML workflows.

Answer

Use Cloud Functions to retrain the model each time a build completes

Answer

Set up a Cloud Scheduler job to poll for new build artifacts

Question 9

You are using Vertex AI hyperparameter tuning with a custom container. The training job reports the objective metric but Vizier is not converging. Which configuration change could improve convergence?

Accepted Answer

Increase the number of parallel trials. Increasing the number of parallel trials can help explore more of the hyperparameter space, but the specified number of max trials must be high enough. Reducing parallel trials may slow exploration. Bayesian optimisation benefits from more trials.

Answer

Use a larger machine type for each trial

Answer

Reduce the number of parallel trials

Answer

Switch from Bayesian to grid search

Question 10

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 11

A manufacturing company wants to predict equipment failure using sensor data. The data is highly imbalanced (only 1% failures). They are using a gradient boosted tree model with class weights. The model achieves 0.99 recall but 0.2 precision on the test set. Which two actions should they take to improve precision without significantly hurting recall? (Choose TWO)

Accepted Answer

Try an anomaly detection algorithm like Isolation Forest. Anomaly detection algorithms like Isolation Forest are designed to identify rare events by isolating anomalies rather than modeling the majority class, which can improve precision when the minority class is extremely rare (1%). Option E is correct because increasing the decision threshold for classifying a positive reduces false positives by requiring higher confidence for a positive prediction, directly improving precision while only minimally reducing recall if the model's probability scores are well-calibrated.

Answer

Oversample the minority class using SMOTE

Answer

Add more features to the model

Answer

Increase the class weight for the minority class

Question 12

An ML engineer needs to run batch predictions on tens of petabytes of data using a trained model. The data is stored in Cloud Storage. Which service should they choose?

Accepted Answer

Vertex AI Batch Prediction. Vertex AI Batch Prediction is the correct choice because it is a managed service specifically designed for high-throughput, large-scale batch inference on data stored in Cloud Storage. It automatically handles sharding, scaling, and resource management for tens of petabytes, without requiring the engineer to manage infrastructure or write custom distributed processing code.

Answer

Cloud Dataflow with the model as a side input

Answer

Cloud Dataproc running Spark ML

Answer

Cloud Run with multiple revisions

Question 13

A team is training a large image classification model using transfer learning from a pre-trained ResNet50. The model will be deployed on mobile devices. They want to fine-tune only the last few layers while keeping the earlier layers frozen. Which approach should they use?

Accepted Answer

Load ResNet50, freeze all layers, add new classification layers, and train only the new layers. Transfer learning typically involves loading a pre-trained model (e.g., ResNet50 from Keras Applications) without the top classification layer, freezing all layers, adding new trainable layers on top, and then training. The base model's layers are frozen (trainable=False). After initial training, one can optionally unfreeze some top layers.

Answer

Load ResNet50, set trainable=False for all layers, and replace the final dense layer only

Answer

Load ResNet50 from Keras Applications, set trainable=True for all layers, add new layers, and train the entire model

Answer

Use AutoML Vision to transfer learn without coding

Question 14

A machine learning team uses Vertex AI Pipelines to run a multi-step training pipeline. They want to implement a continuous delivery (CD) process where a model is automatically promoted from staging to production only if it passes an evaluation gate. Which TWO actions should they include in their CI/CD pipeline? (Choose two.)

Accepted Answer

Store model versions in Vertex AI Model Registry with version aliases (e.g., 'staging', 'production').. Vertex AI Model Registry supports version aliases like 'staging' and 'production', which allow the CI/CD pipeline to automatically promote a model from staging to production only after it passes the evaluation gate. This enables a controlled, automated CD process without manual intervention.

Answer

Use Cloud Functions to periodically check for new model versions and deploy.

Answer

Manually approve each model version before deployment.

Answer

Deploy every model version directly to production without evaluation.

Question 15

You have a model that predicts equipment failure. The model is retrained every week with new data. You notice that the model's precision is stable but recall drops suddenly. Which monitoring strategy would best help you understand the cause?

Accepted Answer

Monitor the distribution of the model's predicted probabilities and compare to the empirical failure rate over time.. A drop in recall (more false negatives) while precision stays stable suggests the model's decision threshold may be misaligned with the current data distribution. Monitoring the distribution of predicted probabilities against the empirical failure rate over time directly reveals if the model's confidence calibration has shifted, indicating concept drift or a change in the underlying failure rate that requires threshold recalibration.

Answer

Monitor feature drift for all input features.

Answer

Compare the number of predictions per day with previous weeks.

Answer

Check the request latency at the endpoint.