PMLE Practice Test 50 — 15 Questions

Question 1

A data scientist has trained a TensorFlow model locally and wants to deploy it to Vertex AI for online predictions. The model accepts a single input tensor of shape (1, 224, 224, 3) and outputs a probability distribution over 10 classes. The data scientist wants to minimize deployment effort and ensure the model is served with low latency. What is the simplest way to deploy this model on Vertex AI?

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

Accepted Answer

Export the model as a SavedModel and upload it to Vertex AI Model Registry, then deploy to an endpoint with a pre-built TensorFlow Serving container.. The simplest and most efficient way to deploy a standard TensorFlow model on Vertex AI for online predictions is to export it as a SavedModel and use the pre-built TensorFlow Serving container. This requires no custom serving code, leverages Vertex AI's managed infrastructure, and provides low-latency predictions. It also integrates with Vertex AI Model Registry for versioning and monitoring.

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Convert the model to ONNX format and use a custom container with ONNX Runtime for serving.

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Use Vertex AI Batch Prediction with a pre-built TensorFlow container, and then set up a Cloud Function to serve online requests.

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Package the model in a Docker container with a Flask app that loads the model and exposes a REST API, then deploy as a custom container on Vertex AI.

Question 2

A media company wants a low-code pipeline that ingests uploaded video files, detects scenes and on-screen text, and stores structured metadata for search. They prefer managed services and minimal custom code. Which TWO Google Cloud capabilities should they combine? (Choose two.)

Accepted Answer

Cloud Storage and Pub/Sub to trigger processing when new videos arrive. The Video Intelligence API supplies managed shot change and text detection on video, producing the scene and on-screen text metadata needed for search. Pairing it with Cloud Storage object notifications to Pub/Sub creates an event-driven trigger so each upload is analyzed automatically. Together they deliver a managed, low-code pipeline without custom frame extraction or model training.

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Cloud Vision API for detecting labels and text in sampled video frames

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Vertex AI Vision for building a custom object-tracking pipeline with trained detectors

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Cloud Data Fusion for building a visual ETL pipeline for video ingestion

Question 3

An ML team is using Vertex AI Pipelines to orchestrate a training workflow. They need to pass a large dataset (500 GB) between two components. The first component preprocesses the data and writes the output to Cloud Storage. The second component trains a model using that preprocessed data. The team wants to minimize pipeline execution time and cost. Which two strategies should they use? (Choose two.)

Accepted Answer

Have the first component write the preprocessed data to a Cloud Storage location and output the URI as a string parameter to the second component.. For large data, the pipeline should pass references (URIs) rather than the data itself. Using a Dataset artifact or a string parameter to convey the Cloud Storage location lets each component access the data independently, minimizing data movement and pipeline overhead. Serializing or sharing via disks is not feasible at this scale and would increase cost and time.

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Mount a Cloud Storage bucket as a Persistent Disk to both components so they can share the data via a common file system.

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Serialize the preprocessed data into a single TFRecord file and pass it as a pipeline parameter to the second component.

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Use a Model artifact to pass the preprocessed data to the training component, and have the training component load the data from the artifact's URI.

Question 4

A team has deployed a model on a Vertex AI Endpoint and enabled Vertex AI Model Monitoring for feature skew. They notice that the skew metric for a categorical feature with high cardinality is consistently high, even though the feature's distribution appears stable to the team. What is the most likely cause of this high skew metric?

Accepted Answer

The training dataset and live data have different sets of categories for that feature, causing divergence.. For high-cardinality categorical features, the presence of categories in live data that were not in the training data (or vice versa) can cause large divergence in distribution comparisons. Vertex AI Model Monitoring uses metrics like Jensen-Shannon divergence, which are sensitive to disjoint category sets. This mismatch can result in consistently high skew even if the overall distribution appears stable. The correct cause is the difference in category sets between training and live data.

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The monitoring job is sampling too few requests, leading to statistical noise.

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The model's predictions are drifting, which indirectly increases feature skew.

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The feature is not included in the training dataset schema, so the skew computation fails.

Question 5

An ML engineer is monitoring a deployed model on Vertex AI Endpoints and wants to detect anomalies in the distribution of a categorical feature 'product_category' which has 50 possible values. The engineer configures Vertex AI Model Monitoring with training-serving skew detection. After a week, they receive an alert that the feature is skewed. Upon investigation, they find that a new product category was introduced in the serving data that was not present in the training data. What is the most likely reason for the alert?

Accepted Answer

The monitoring system treats the new category as an unknown value and flags it as skew.. Vertex AI Model Monitoring's skew detection compares the distribution of feature values between training and serving. When a new category appears in serving that was absent in training, the distribution diverges, and the system flags it as skew. This is because the model has never seen that category and may not handle it well, so monitoring alerts the team to investigate.

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The model's predictions are incorrect for the new category, causing the monitoring system to flag the feature.

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The training data already contained the new category but it was not properly encoded.

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The monitoring configuration has a bug that misinterprets the new category as a numerical value.

Question 6

A financial services company uses a Vertex AI Endpoint to serve a credit risk model. The model must always be available, even during maintenance windows, and they need to control the exact distribution of traffic across two model versions for a gradual rollout. They also want to minimize cold-start latency. Which deployment configuration should they use?

Accepted Answer

Deploy both model versions to the same endpoint, set minReplicaCount to at least 1 for each, and use traffic splitting to route a percentage of requests to each version.. Deploying both versions to the same endpoint with dedicated replicas and traffic splitting satisfies availability, precise rollout control, and low latency. It leverages Vertex AI's native capabilities for version management and gradual traffic shifting without additional infrastructure.

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Deploy a single model version with minReplicaCount=0 and maxReplicaCount=10, and use a custom prediction routine to handle both versions based on request headers.

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Create two separate endpoints, one for each model version, and use a global load balancer to distribute traffic equally between them.

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Deploy the model to a Vertex AI Batch Prediction job and schedule it to run every hour, then use a Cloud Function to serve predictions from the latest batch results.

Question 7

An ML engineer has a Vertex AI pipeline that trains a model and then evaluates it. The engineer wants the pipeline to deploy the model to an endpoint only if the evaluation metric exceeds a threshold defined at pipeline submission time. The threshold must be changeable without recompiling the pipeline. Which mechanism should the engineer use?

Accepted Answer

Define the threshold as a pipeline input parameter and use a dsl.Condition on that parameter to gate the deployment component.. Vertex AI Pipelines distinguishes compile-time structure from runtime parameters. Making the threshold a pipeline input parameter allows it to be supplied at submission time without recompiling, and dsl.Condition on that parameter creates the deployment gate. The other options either bake the threshold into the pipeline definition, misuse Model Registry aliases, or rely on an unsupported environment variable mechanism that the compiler cannot see.

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Set the threshold as an environment variable in the pipeline's service account and read it inside the deployment component.

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Hardcode the threshold in the evaluation component and use a dsl.Condition on the evaluation component's output.

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Use a Vertex AI Model Registry alias and configure the endpoint to only serve models whose alias matches the threshold value.

Question 8

An ML engineer has deployed a tabular binary classification model to a Vertex AI Endpoint. After enabling Vertex AI Model Monitoring with training-serving skew detection, the engineer notices that the feature 'customer_age' is flagged as skewed. The feature values at serving time appear to be shifted upward by about 10 years compared to the training data. Which of the following is the most likely root cause?

Accepted Answer

The training dataset contained a different feature distribution than the serving data because the model was trained on historical data from an older customer base.. Training-serving skew detection compares the distribution of feature values in production against the training baseline. A systematic upward shift in 'customer_age' indicates that the serving population is older than the training population. This is a common scenario when a model is trained on historical data and deployed later, as demographic shifts occur.

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The endpoint is receiving malformed requests that contain incorrect age values.

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The model's predictions are biased, causing the monitoring system to misinterpret the feature values.

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The monitoring configuration uses an incorrect sampling rate, leading to a false positive alert.

Question 9

You are deploying a scikit-learn model to Vertex AI for online prediction. The model requires a custom preprocessing step that involves scaling numerical features and one-hot encoding categorical features. You have packaged the preprocessing and model into a single Python script that uses a custom prediction routine. You need to ensure that the online prediction service can handle varying input formats and provide low-latency responses. What should you do?

Accepted Answer

Use Vertex AI's custom prediction routine with a preprocessor that accepts raw input and transforms it, and deploy the model as a model artifact with the custom routine.. Using a custom prediction routine with a preprocessor allows you to encapsulate preprocessing and model logic in a single package, ensuring consistency between training and serving. Vertex AI manages the infrastructure, providing low-latency and scalability. This is the most efficient and maintainable approach for scikit-learn models with custom preprocessing.

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Deploy the model as a custom container on Vertex AI, and implement the preprocessing logic inside the container's HTTP server.

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Use Vertex AI's feature store to perform preprocessing, and then feed the features directly to the model.

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Deploy the model using Vertex AI's built-in scikit-learn container, and perform preprocessing on the client side before sending requests.

Question 10

A retail company wants to build a low-code ML solution to predict customer lifetime value (CLV) using historical transaction data stored in BigQuery. They have limited ML expertise and want to use BigQuery ML. Which two steps are necessary to train and evaluate a model using BigQuery ML? (Choose two.)

Accepted Answer

Use ML.EVALUATE to assess the model's performance on a test dataset.. Training a model in BigQuery ML requires the CREATE MODEL statement, which defines and trains the model using SQL. Evaluation is performed with ML.EVALUATE to compute metrics like RMSE or accuracy. These two steps are essential for building and assessing a model. Other steps like exporting or predicting are optional or occur after evaluation, and manual data splitting is unnecessary due to automatic splitting.

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Use ML.PREDICT to generate predictions on new data.

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Manually split the data into training and test sets using a CREATE TABLE statement.

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Export the model to a TensorFlow SavedModel for deployment.

Question 11

Your team trains a scikit-learn model locally and uploads it to Vertex AI Model Registry. A colleague needs to deploy it to a Vertex AI Endpoint for online prediction with a prebuilt container. The model artifacts are stored in a Cloud Storage bucket. Which deployment approach should they use?

Accepted Answer

Import the model with the prebuilt scikit-learn container image and deploy it to an Endpoint using the Model Registry UI or the gcloud ai endpoints deploy-model command.. The scikit-learn model was trained locally and stored in Cloud Storage, so it should be imported into Vertex AI Model Registry using the prebuilt scikit-learn container. This allows direct deployment to a Vertex AI Endpoint for online prediction. The prebuilt container removes the need for custom inference code and supports the standard deployment workflow via console or gcloud CLI.

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Create a custom prediction routine in a Python source distribution, upload it as a model artifact, and deploy it without specifying a serving container.

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Export the model to a SavedModel format, import it with a TensorFlow prebuilt container, and deploy it to an Endpoint.

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Upload the model artifact to Vertex AI Model Registry as a BigQuery ML model and deploy it using the BigQuery ML serving container.

Question 12

You are training a scikit-learn random forest on a dataset that fits in memory using Vertex AI custom training. The prototype notebook took 15 minutes, but the Vertex AI job takes over an hour and occasionally fails with a resource error. You want the production job to complete reliably without changing the model or preprocessing. What should you do?

Accepted Answer

Select a machine type with more memory and vCPUs, and set the appropriate boot disk size for the training job.. The job fits in memory but fails on the default Vertex AI worker configuration, which points to insufficient RAM or vCPUs on the training machine. Choosing a larger machine type and an adequate boot disk gives the scikit-learn process the resources it needs. Distributed training is unnecessary and would require changing the algorithm, while preemptible VMs reduce reliability rather than improve it.

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Switch the training job to use a preemptible VM to reduce contention on shared resources.

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Configure the training job to run on a single n1-standard-4 machine with no accelerator.

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Increase the number of worker replicas and use a distributed reduction strategy for the random forest.

Question 13

An ML engineer is using Vertex AI Pipelines to orchestrate a workflow that includes a custom component for data validation. The component takes a dataset URI and outputs a validation report. The engineer wants to fail the pipeline immediately if the validation report indicates that the data is invalid, without running subsequent steps. How should the engineer implement this?

Accepted Answer

Have the data validation component raise an exception if the data is invalid, causing the task to fail and the pipeline to stop.. The most effective way to fail the pipeline immediately upon invalid data is to have the data validation component raise an exception. This causes the task to fail, and Vertex AI Pipelines will not run any downstream tasks that depend on its output. The pipeline will be marked as failed, which can trigger alerts. This approach is simple, reliable, and leverages the native failure handling of the pipeline orchestrator.

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Use a conditional step after validation that checks the report and only proceeds if valid, otherwise skips subsequent steps.

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Write the validation result to a Cloud Storage bucket and use a Cloud Function to monitor the bucket and cancel the pipeline if invalid.

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Configure the pipeline to use a 'fail_fast' execution option that stops the pipeline if any component outputs a failure status.

Question 14

A retail company wants to forecast daily sales for each of its 500 stores for the next 90 days. They have three years of historical daily sales data stored in BigQuery, including promotions, holidays, and store attributes. The data science team has minimal ML expertise and wants to use SQL to build and deploy the model with minimal coding. Which approach should they use?

Accepted Answer

Create an ARIMA_PLUS model in BigQuery ML using the sales time series and specify the store ID as the time series identifier.. BigQuery ML's ARIMA_PLUS is purpose-built for time series forecasting and can handle multiple related time series by specifying an identifier column. It automatically models seasonality, holidays, and trends, and supports additional regressors. This requires only SQL, making it ideal for teams with limited ML expertise who need per-store forecasts.

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Build a custom LSTM model in Vertex AI using TensorFlow, training on the historical sales data.

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Use AutoML Forecasting in Vertex AI, exporting the data from BigQuery to Cloud Storage.

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Train a linear regression model in BigQuery ML with store ID as a feature and date as a numeric feature.

Question 15

Your team is preparing to hand a trained model to a separate operations team that will deploy it to a Vertex AI endpoint. The operations team needs to understand the model's input schema, the training run that produced it, and which alias currently points to production. Which two Vertex AI resources should you share with them to provide this information? (Choose two.)

Accepted Answer

The registered model resource in Vertex AI Model Registry, including its version and alias.. A complete handoff to operations requires the registered model resource for version and alias information, and the training run record for parameters, metrics, and input schema. Feature stores, pipeline jobs, and raw data buckets serve different purposes and do not provide the deployment team with the model-specific governance details they need.

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The Vertex AI Pipeline job resource that was used to schedule nightly retraining.

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The Cloud Storage bucket containing the raw training data.

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The Vertex AI Feature Store online store resource used during training.