PMLE Automating and Orchestrating ML Pipelines Practice Test 8 — 15 Questions

Question 1

A team develops a pipeline that trains a model and evaluates it. They want to pass the test accuracy (a float) from the evaluation component to a subsequent deployment component. Which KFP SDK type should the evaluation component output be annotated with?

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

Accepted Answer

Output[float]. In the KFP SDK, a component output annotated as Output[float] is treated as a lightweight scalar parameter that is passed directly between components via the pipeline's execution graph. Since test accuracy is a single floating-point value (not a file, dataset, or structured metric object), Output[float] is the correct type to declare so the downstream deployment component can consume it as a typed input. KFP serializes this primitive and makes it available for parameter passing without needing artifact storage.

Answer

Output[Metrics]

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Output[Artifact]

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Output[ClassificationMetrics]

Question 2

An ML pipeline runs on Vertex AI and includes a component that uses a third-party library not available in the default Python environment. The team wants to avoid building a custom container image. Which approach should they use?

Accepted Answer

Use the packages_to_install parameter in @dsl.component. The `packages_to_install` parameter in the `@dsl.component` decorator allows you to specify a list of third-party Python packages (e.g., via pip) that will be installed at runtime in the component's execution environment, without needing to build a custom container image. This is the recommended approach in Vertex AI Pipelines when you need to use a library not present in the default Python environment, as it avoids the overhead of custom container creation while ensuring the dependency is available for that specific component.

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Install the library using pip in the pipeline definition

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Use a container component with a pre-built image

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Add the library to the Vertex AI custom training image

Question 3

A pipeline uses the Google Cloud Pipeline Components to perform AutoML training and batch prediction. Which two components from the GCPC library should they use? (Choose two.)

Accepted Answer

AutoMLTabularTrainingJobRunOp. Option C, AutoMLTabularTrainingJobRunOp, is correct because it is the Google Cloud Pipeline Components (GCPC) operator that wraps the Vertex AI AutoML tabular training job, allowing the pipeline to launch an AutoMLTabularTrainingJob and produce a model artifact. Option D, BatchPredictOp, is correct because it is the GCPC component that submits a Vertex AI batch prediction job against a trained model and a specified input data source, which is exactly the batch prediction step described in the scenario. Option A, CustomJobRunOp, is not appropriate here because it runs a custom training container rather than an AutoML training job. Option B, DataflowPythonOp, is a Dataflow-based Python execution component and does not perform AutoML training or batch prediction. Option E, EndpointPredictOp, performs online prediction against a deployed Vertex AI endpoint, not batch prediction, so it does not fit the scenario.

Answer

CustomJobRunOp

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DataflowPythonOp

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EndpointPredictOp

Question 4

An ML pipeline must run a set of preprocessing tasks for each data shard in parallel. Which KFP SDK features should they use to implement this? (Choose two.)

Accepted Answer

dsl.ParallelFor. dsl.ParallelFor (A) is correct because it is the KFP SDK construct that iterates over a list (such as the set of data shards) and fans out the loop body into parallel task executions, which is exactly what is needed to run preprocessing per shard concurrently. dsl.Collected (C) is correct because it is used with dsl.ParallelFor to gather the outputs of all parallel iterations into a single list, allowing downstream pipeline steps to consume the aggregated results of the per-shard preprocessing. dsl.PipelineParam (B) is not the mechanism for parallel iteration; it only represents a runtime parameter passed into a pipeline or component. dsl.Condition (D) implements conditional branching (if/else) rather than parallel fan-out, and dsl.ExitHandler (E) defines cleanup logic that runs when a scope exits, neither of which provides the required parallel execution over shards.

Answer

dsl.PipelineParam

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dsl.Condition

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dsl.ExitHandler

Question 5

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.)

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 6

A pipeline includes a component that produces a model artifact. The team wants to automatically detect skew between the training data distribution and the serving data distribution. Which three best practices should they implement? (Choose three.)

Accepted Answer

Compare statistics using a dedicated component and alert on threshold exceedance. Option A is correct because skew detection requires a dedicated comparison component that evaluates training versus serving statistics and raises an alert when a configured threshold (e.g., a divergence metric like KL divergence or PSI) is exceeded. Option C is correct because you must compute statistics on the serving data itself, typically via a component that ingests the live inference data and emits a statistics artifact for comparison. Option E is correct because the training data statistics should be persisted and passed as a Dataset artifact so the comparison component can consume them reproducibly across pipeline runs. Option B is wrong because in-memory data passing does not persist artifacts and is unsuitable for cross-component, cross-run statistical comparison. Option D is wrong because disabling caching does not improve statistical correctness and only adds unnecessary recomputation cost.

Answer

Use in-memory data passing for efficiency

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Disable caching to ensure fresh statistics

Question 7

A company runs a Vertex AI Pipeline that includes a hyperparameter tuning step followed by a training step. The tuning step outputs the best hyperparameters. The engineer wants the training step to use these hyperparameters and to ensure that the training step only runs if tuning succeeds. Which approach should the engineer take?

Accepted Answer

Pass the output of the tuning step as an input parameter to the training step and set the `after` parameter of the training step to include the tuning step.. To pass data between tasks in Vertex AI Pipelines, the output of one task must be provided as an input to another. The tuning step's output (best hyperparameters) is passed as an input to the training step, creating a data dependency. Additionally, the `after` parameter ensures the training step runs only after the tuning step succeeds. This dual approach guarantees both correct data flow and execution order, and if tuning fails, the pipeline stops before training.

Answer

Use a conditional expression in the training step to check if the tuning step succeeded, and only then proceed.

Answer

Configure the tuning step to write hyperparameters to a Cloud Storage bucket, and have the training step read from that bucket at runtime.

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Run the tuning and training steps in parallel to save time, and use a shared memory store to exchange hyperparameters.

Question 8

An ML engineer has a Vertex AI pipeline that trains a model and then evaluates it. The engineer wants the pipeline to automatically deploy the model to an endpoint only if the evaluation metric (e.g., accuracy) exceeds a threshold. The pipeline is defined using the Kubeflow Pipelines SDK. Which approach should the engineer use to implement this conditional deployment?

Accepted Answer

Use a Vertex AI Pipelines condition with a comparison of the metric output to the threshold, and place the deployment component inside the condition's 'then' branch.. Conditional deployment in Vertex AI Pipelines is achieved by using dsl.Condition to evaluate a metric against a threshold. The deployment component is placed inside the condition's true branch, ensuring it only executes when the metric meets the requirement. This provides a clean, orchestrated way to gate deployment without external services or manual intervention.

Answer

Set the deployment component's 'condition' parameter to the metric value, and rely on the component to skip itself if the value is below threshold.

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Configure the pipeline to always deploy, and use a Cloud Function triggered by a Pub/Sub message from the evaluation step to roll back if the metric is below threshold.

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Use a Vertex AI Model Evaluation component and set its 'deploy' flag to true, which automatically deploys if the metric meets the threshold.

Question 9

An ML engineer is building a Vertex AI pipeline that trains a model and then evaluates it. The evaluation component must compare the new model's accuracy against a fixed threshold and, if the model passes, trigger a downstream deployment component. The engineer wants to avoid running the deployment component when the model fails. Which approach should the engineer use?

Accepted Answer

Define a condition on the deployment component using the evaluation metric output from the evaluation component.. Conditional execution in Vertex AI Pipelines is implemented using the dsl.Condition construct, which allows a component to run only if a specified condition is met. By referencing the evaluation metric output, the pipeline can automatically decide whether to deploy the model. This approach avoids running unnecessary components and keeps the pipeline logic self-contained.

Answer

Use a pipeline parameter to pass the evaluation result to the deployment component and let the deployment component decide whether to run.

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Configure the evaluation component to raise an exception if the model fails, which will stop the entire pipeline before reaching the deployment component.

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Create a separate pipeline that only contains the deployment component and trigger it manually after reviewing the evaluation results.

Question 10

An ML engineer is building a Vertex AI pipeline that includes a component to train a model. The component takes a long time to run and occasionally fails due to transient errors (e.g., network timeouts). The engineer wants to automatically retry the component a few times if it fails. How should the engineer configure this?

Accepted Answer

Set the retry policy on the pipeline task using the 'retry' parameter in the component's task definition, specifying the number of retries and backoff.. To automatically retry a component on failure, the engineer should set a retry policy on the task. This is done by specifying the retry parameter in the task definition, including max_retries and backoff settings. This leverages Vertex AI Pipelines' built-in retry mechanism, which is designed for transient failures.

Answer

Use a Cloud Scheduler job to trigger the pipeline again if it fails, and rely on the pipeline to resume from the failed step.

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Set the pipeline's execution timeout to a high value and hope that the component succeeds on its own.

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Configure the component to catch exceptions and loop internally until it succeeds, with no limit on retries.

Question 11

An ML engineer is designing a Vertex AI Pipeline that includes a custom training component. The component must read a dataset from a Cloud Storage bucket and write the trained model to another Cloud Storage location. The engineer wants the component to be reusable across pipelines and to ensure that the pipeline tracks the exact dataset and model artifacts. Which approach should the engineer take?

Accepted Answer

Pass the Cloud Storage URIs as component inputs and outputs, and declare them as artifacts of type Dataset and Model.. Using artifact inputs and outputs with types Dataset and Model ensures that Vertex AI Pipelines tracks lineage and metadata. It keeps the component reusable because the actual URIs are passed at runtime. Declaring artifacts also enables the pipeline to display them in the UI and to use them for caching and conditional execution.

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Hardcode the Cloud Storage URIs inside the component's container code and use environment variables for configuration.

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Use a single string parameter for the dataset URI and a single string parameter for the model URI, without declaring artifact types.

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Mount a Cloud Storage bucket as a volume in the component container and write the model directly to the mounted path.

Question 12

A team is using Vertex AI Pipelines to orchestrate a multi-step ML workflow. They need to pass a large dataset (several terabytes) between two components: a preprocessing component and a training component. The preprocessing component outputs a preprocessed dataset that the training component consumes. The team wants to minimize data transfer time and cost. What is the most efficient way to pass the data between these components?

Accepted Answer

Write the preprocessed data to a Cloud Storage bucket and pass the bucket URI as an output parameter to the training component.. For large datasets, the best practice is to write data to Cloud Storage and pass the URI as a parameter. This avoids moving data through the pipeline's control plane and leverages Cloud Storage's scalability and high throughput. The training component can then read directly from the bucket.

Answer

Mount a Filestore instance as a shared volume to both components and write the data there.

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Use a `Dataset` artifact to pass the data, and configure the artifact to store the data in the pipeline's metadata store.

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Have the preprocessing component output the data as a base64-encoded string parameter, and pass it to the training component.

Question 13

An ML engineer is authoring a Vertex AI Pipelines component that runs a custom Python script. The component must accept a GCS path to training data and output a model artifact. The engineer wants the component interface to be strongly typed and to automatically generate the component specification from the Python function. Which approach should the engineer use?

Accepted Answer

Define the component using the @dsl.component decorator from the kfp package, annotating the function parameters with types like str and Output[Model].. The Kubeflow Pipelines SDK v2 @dsl.component decorator introspects Python type annotations to build a component specification, including input and output artifacts. This provides strong typing and automatic generation, which is exactly what the engineer needs. Other approaches either require manual YAML definition or are intended for different use cases like custom training jobs.

Answer

Define the component using the @component decorator from the google.cloud.aiplatform.v1alpha1 package, specifying the input and output types as function annotations.

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Use the google.cloud.aiplatform.CustomContainerTrainingJob class to package the script and run it as a pipeline step.

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Write a Dockerfile that installs the required dependencies and exposes the script as an entrypoint, then build and push the image to Artifact Registry.

Question 14

A company runs a Vertex AI Pipeline that includes a custom component for hyperparameter tuning. The component uses a large search space and runs many trials. The pipeline is taking too long to complete, and the team wants to reduce the execution time without sacrificing model quality. They have already optimized the training code. Which Vertex AI Pipelines feature should they use to speed up the tuning component?

Accepted Answer

Use the ParallelFor loop in Vertex AI Pipelines to run multiple trials concurrently as separate tasks.. ParallelFor in Vertex AI Pipelines enables concurrent execution of independent tasks, which is well-suited for hyperparameter tuning trials. By running multiple trials in parallel, the overall tuning time is significantly reduced while maintaining the same search space and model quality. This leverages the pipeline's orchestration capabilities and is more effective than caching or increasing machine type.

Answer

Enable pipeline caching for the tuning component so that repeated runs with identical parameters are skipped.

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Reduce the number of trials by using a random search instead of a grid search.

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Increase the machine type of the tuning component to a higher CPU or GPU configuration.

Question 15

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.

Answer

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.