PMLE Practice Test 22 — 15 Questions

Question 1

Your team is deploying a large model on edge devices and needs to reduce its size by 80% while maintaining reasonable accuracy. Which THREE techniques should they consider? (Choose 3.)

Accepted Answer

Quantisation to INT8. Quantisation to INT8 reduces the precision of model weights and activations from 32-bit floating point to 8-bit integers, cutting memory usage by approximately 75% (4x compression). This directly addresses the 80% size reduction target while often preserving accuracy within 1-2% through careful calibration and scaling, making it a primary technique for edge deployment.

Answer

Transfer learning from a larger model

Answer

Increasing model capacity with more layers

Question 2

A machine learning engineer wants to use Vertex AI Vizier to tune three hyperparameters: learning rate (log scale), number of layers (integer), and optimizer (categorical). They have 50 parallel trials available. Which parameter specification types should they define?

Accepted Answer

learning_rate: DOUBLE (unit_log_scale), layers: INTEGER (unit_linear_scale), optimizer: CATEGORICAL. Vertex AI Vizier requires parameter specifications that match the nature of each hyperparameter. Learning rate is best explored on a logarithmic scale, so it should be a DOUBLE parameter with unit_log_scale. Number of layers is a discrete integer count, so it should be an INTEGER parameter with unit_linear_scale. Optimizer is a categorical choice among named algorithms, so it should be CATEGORICAL. This combination correctly reflects the mathematical and structural properties of each hyperparameter.

Answer

learning_rate: CATEGORICAL, layers: INTEGER, optimizer: CATEGORICAL

Answer

learning_rate: DOUBLE (unit_log_scale), layers: DOUBLE (unit_linear_scale), optimizer: DISCRETE

Answer

learning_rate: DOUBLE (unit_linear_scale), layers: INTEGER (unit_linear_scale), optimizer: CATEGORICAL

Question 3

A team is designing a ML pipeline that includes training, evaluation, and conditional deployment. They want to use Vertex AI Pipelines. Which THREE concepts should they use? (Choose three.)

Accepted Answer

Artifact types (e.g., Model, Metrics) for passing outputs. Option A is correct because Vertex AI Pipelines is built on ML Metadata, and typed artifacts such as Model, Metrics, Dataset, and Artifact let components pass structured outputs between training and evaluation steps so downstream steps and lineage tracking work correctly. Option D is correct because pre-built Google Cloud Pipeline Components (e.g., CustomTrainingJobOp, ModelEvaluationOp) provide ready-made, versioned steps for training and evaluation, reducing boilerplate and integrating natively with Vertex AI services. Option E is correct because conditional deployment requires branching logic in the pipeline graph, which is expressed with the Kubeflow Pipelines DSL construct dsl.If (or dsl.Condition) to run a deployment component only when evaluation metrics meet a threshold. Option B is not appropriate because manual approval via the Cloud Console is not a Vertex AI Pipelines concept for conditional deployment; gating is done programmatically in the pipeline DAG. Option C is not appropriate because intermediate results in Vertex AI Pipelines are passed as artifacts and metadata, not stored in Cloud SQL, which is a relational database service unrelated to pipeline data flow.

Answer

Manual approval via Cloud Console

Answer

Cloud SQL for storing intermediate results

Question 4

A data scientist wants to evaluate the performance of a BigQuery ML classification model on a test dataset. Which function should they use?

Accepted Answer

ML.EVALUATE. ML.EVALUATE is the correct function because it computes classification metrics (e.g., precision, recall, accuracy, F1 score, ROC AUC) directly on a trained BigQuery ML model using a provided test dataset or evaluation input. This is the dedicated function for assessing model performance after training, aligning with the task of evaluating a classification model on held-out test data.

Answer

ML.PREDICT

Answer

ML.FEATURE_IMPORTANCE

Answer

ML.TRAIN

Question 5

A machine learning engineer wants to monitor model performance on Vertex AI for a regression model. Which metric is most appropriate to track the average prediction error?

Accepted Answer

RMSE. RMSE (Root Mean Squared Error) is the most appropriate metric for tracking average prediction error in a regression model because it measures the standard deviation of residuals (prediction errors) in the same units as the target variable. On Vertex AI, RMSE is a built-in evaluation metric for regression models, directly quantifying how far predictions deviate from actual values on average.

Answer

F1 score

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Precision

Answer

Accuracy

Question 6

You are tasked with building a robust ML pipeline that must be idempotent and handle data skew between training and serving. Which three practices should you implement?

Accepted Answer

Store intermediate data in Cloud Storage with unique run IDs.. Option A is correct because writing intermediate artifacts to Cloud Storage under a unique run ID (e.g., gs://bucket/run_id/...) isolates each pipeline execution, so reruns don't overwrite or collide with prior outputs — a key requirement for idempotency. Option C is correct because comparing training feature distributions against serving feature distributions (e.g., via statistics like mean, variance, or histogram distance) is the standard way to detect training/serving skew and trigger remediation. Option E is correct because deterministic components that yield identical outputs for identical inputs make reruns safe and reproducible, which is the essence of an idempotent pipeline. Option B is wrong because passing large datasets as serialized in-memory objects is fragile, memory-bound, and non-idempotent; components should exchange data via durable storage references instead. Option D is wrong because a fixed random seed only aids reproducibility of stochastic steps and does nothing to guarantee idempotency or address training/serving skew.

Answer

Pass large datasets between components as serialized in-memory objects.

Answer

Use the same random seed for every run to ensure reproducibility.

Question 7

A machine learning engineer needs to run batch predictions on 50 TB of data stored in BigQuery using a Vertex AI model. The model is a custom container. What is the most efficient way to set up the batch prediction job?

Accepted Answer

Create a Vertex AI batch prediction job with BigQuery source and BigQuery destination.. Vertex AI batch prediction natively supports BigQuery as both input source and output destination, allowing the service to read the 50 TB directly from BigQuery and write predictions back without exporting data. This avoids data movement, leverages BigQuery's scalability, and is the most efficient, fully managed approach for large-scale batch inference with a custom container.

Answer

Use Dataflow to process the data and call the model via Vertex AI online prediction.

Answer

Export BigQuery data to CSV in GCS, then create a batch prediction job with GCS source.

Answer

Create a Cloud Function to iterate over BigQuery rows and call the endpoint.

Question 8

A company deploys a model on Vertex AI Endpoint and expects high traffic spikes during promotional events. The current configuration uses manual scaling with 2 replicas. Which autoscaling configuration should they use to handle spikes while minimizing cost during normal traffic?

Accepted Answer

Enable basic scaling with target_cpu_utilization=0.6 and set min_replica_count=2, max_replica_count=10.. Enabling autoscaling with a CPU utilization target and setting min/max replica counts allows Vertex AI to scale replicas up during traffic spikes and down during normal traffic, balancing performance and cost. The min of 2 preserves baseline capacity while the max of 10 caps cost during spikes.

Answer

Keep manual scaling but increase replicas to 10.

Answer

Set min_replica_count=2 and max_replica_count=10 with no scaling metric.

Answer

Use custom metric scaling with a Cloud Monitoring metric for prediction latency.

Question 9

A data scientist wants to track machine learning experiments, including parameters, metrics, and artifacts, and compare runs. Which Vertex AI service should they use?

Accepted Answer

Vertex AI Experiments. Vertex AI Experiments is the service designed to track ML experiments, including parameters, metrics, and artifacts, and to compare runs. It integrates with Vertex AI Metadata to log experiment lineage and provides a UI for comparing runs. This directly matches the data scientist's requirement.

Answer

Vertex AI Metadata

Answer

Vertex AI Feature Store

Answer

Vertex AI Model Registry

Question 10

You are designing a batch prediction pipeline using Vertex AI. The input data is 100 TB of images stored in Cloud Storage. The model is a custom TensorFlow model that expects TFRecord format. The pipeline must be cost-effective and run within a time window of 2 hours. Which THREE steps should you include?

Accepted Answer

Create a Vertex AI batch prediction job with input from GCS (TFRecord files).. Option B is correct because Vertex AI batch prediction natively accepts TFRecord input files from Cloud Storage, which matches the custom TensorFlow model's expected format and lets the managed service handle the large-scale inference job within the 2-hour window. Option C is correct because Dataflow is a scalable, cost-effective managed service for reading 100 TB of images from GCS and transforming them into TFRecord files, a task Cloud Functions cannot handle at this volume. Option D is correct because Vertex AI batch prediction writes its output to Cloud Storage, which is the appropriate and cost-effective destination for large result sets from 100 TB of input. Option A is not appropriate because BigQuery is not the default or cost-effective sink for massive batch prediction outputs and would add unnecessary loading overhead. Option E is not appropriate because Cloud Functions has execution time, memory, and concurrency limits that make it unsuitable for converting 100 TB of images to TFRecord.

Answer

Store batch prediction results in BigQuery.

Answer

Use Cloud Functions to convert images to TFRecord.

Question 11

Which TWO actions are appropriate when you detect that a production model's prediction distribution has shifted significantly from the training distribution?

Accepted Answer

Retrain the model using the most recent data. Option C is correct because when a production model's prediction distribution has drifted from the training distribution, the underlying data distribution has likely changed, so retraining the model on the most recent data is the standard remediation to restore alignment between the model and current conditions. Option D is correct because before applying any fix, you must investigate the root cause of the shift — whether it is genuine data drift, a data pipeline/schema change, or a feature-engineering bug — since the appropriate corrective action depends on the cause. Option A is not appropriate as an automatic first step because rolling back only helps if the previous version is still valid for the current data distribution, and it may not address the underlying drift. Option B is not appropriate because simply increasing logging does not correct the shifted predictions, though it may support diagnosis. Option E is not appropriate because reducing traffic does not fix the drift and may harm service availability without addressing the model's degraded relevance.

Answer

Immediately roll back to the previous model version

Answer

Increase logging for future predictions

Answer

Reduce the traffic to the model to minimize impact

Question 12

An organization is deploying a mission-critical model on Vertex AI Endpoints. They need to ensure high availability and meet a strict SLO of 99.9% uptime. Which THREE steps should they take? (Choose 3)

Accepted Answer

Set minReplicas to at least 2 to ensure redundancy within a region.. Option B is correct because setting minReplicas to at least 2 on a Vertex AI Endpoint ensures that multiple model server replicas run within the region, so a single replica failure or rolling update does not cause an outage and the 99.9% SLO can be maintained. Option D is correct because deploying the endpoint in multiple regions provides geographic redundancy, so a regional outage of Vertex AI or its underlying infrastructure does not take the mission-critical model offline. Option E is correct because configuring health checks (Vertex AI endpoint health checks/readiness probes) lets the service detect unhealthy replicas and route traffic away from or replace them, preserving availability. Option A is not appropriate because Cloud CDN caches HTTP responses at the edge and does not address the availability of the Vertex AI prediction backend or its SLO. Option C is not appropriate because using a single large instance creates a single point of failure and provides no redundancy, which works against high availability.

Answer

Use Cloud CDN to cache responses.

Answer

Use a single large instance instead of multiple small ones.

Question 13

An ML engineer is scaling a prototype to production using Vertex AI Pipelines. The pipeline includes data validation, preprocessing, training, and deployment steps. They want to ensure that the pipeline can be reproduced and audited. What is the best practice?

Accepted Answer

Define the pipeline using Kubeflow Pipelines SDK and run it on Vertex AI Pipelines.. Defining the pipeline with the Kubeflow Pipelines (KFP) SDK and running it on Vertex AI Pipelines produces a versioned, declarative pipeline specification (a compiled YAML/JSON IR) that captures each step, its container image, inputs, outputs, and parameters. Vertex AI Pipelines stores this as a reproducible pipeline run with lineage tracking, so any execution can be audited and re-run deterministically. This is the canonical Google-recommended approach for reproducible, auditable ML workflows on Vertex AI.

Answer

Use a Docker container with fixed tags and manually record runs.

Answer

Store all data and models in a single Cloud Storage bucket with no versioning.

Answer

Pin all library versions in a requirements.txt file.

Question 14

A machine learning engineer needs to pass a large dataset between two components in a Vertex AI pipeline. What is the recommended way to pass this data?

Accepted Answer

Store the dataset as a Dataset artifact and pass the artifact between components.. In Vertex AI Pipelines, the recommended way to pass large datasets between components is to use a `Dataset` artifact. Artifacts are metadata references that point to the underlying data stored in Cloud Storage, enabling efficient, scalable, and type-safe data passing without serialization overhead or size limits. This approach leverages the Kubeflow Pipelines SDK's artifact tracking, which automatically handles lineage and versioning.

Answer

Write the dataset to a temporary BigQuery table and pass the table name.

Answer

Serialize the dataset to a string and pass it as a pipeline parameter.

Answer

Use a Cloud Storage bucket and pass the bucket name as a parameter.

Question 15

An ML engineer needs to update a model deployed on a Vertex AI endpoint without downtime. They want to gradually shift traffic to the new version while monitoring for errors. What is the correct procedure?

Accepted Answer

Deploy the new model to the same endpoint with 0% traffic initially, then gradually increase traffic while monitoring.. Vertex AI supports deploying multiple models to the same endpoint and splitting traffic via a deployed model's traffic percentage. The correct canary procedure is to deploy the new model version to the existing endpoint with 0% traffic, then gradually increase its traffic share while monitoring error rates and latency. This avoids downtime and enables safe rollback by shifting traffic back to the old version.

Answer

Use a canary deployment by deploying to a separate endpoint and using a load balancer with weighted routing.

Answer

Deploy the new model to a new endpoint, then update DNS to point to the new endpoint.

Answer

Delete the old model and deploy the new one with the same endpoint.