PMLE Automating and Orchestrating ML Pipelines Practice Test 9 — 15 Questions

Question 1

An ML engineer is building a Vertex AI Pipeline that includes a data validation component. The component should fail the pipeline if the input data does not meet certain statistical thresholds. The engineer wants to ensure that the pipeline stops immediately and does not proceed to training if validation fails. Which mechanism should the engineer use in the component?

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

Accepted Answer

Return a non-zero exit code from the component's container.. A non-zero exit code from a component's container is the standard way to signal failure in Vertex AI Pipelines. When a component fails, the pipeline task fails, and the pipeline stops by default, preventing downstream tasks from executing. This enforces the validation gate effectively.

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Raise an exception in the component code and catch it in the pipeline definition.

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Write a warning to the logs and continue execution.

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Use a conditional branch in the pipeline to skip training if validation fails.

Question 2

A team runs a Vertex AI pipeline that includes a component which downloads a large dataset from BigQuery and writes it to Cloud Storage. The pipeline's caching is enabled by default. During iterative development, the engineer modifies the SQL query inside the component to include an additional feature column, but the pipeline still uses the previously cached output because the component's input parameters and code hash are unchanged. The engineer needs the component to re-execute with the updated query without disabling caching for the entire pipeline. What should the engineer do?

Accepted Answer

Add the SQL query as a pipeline parameter and pass it to the component as an input.. Vertex AI Pipelines caching uses a fingerprint derived from the component's code and input parameters. When a component's internal logic changes but its inputs and code hash remain the same, the cache is considered valid. To force re-execution without disabling caching entirely, the engineer should expose the changing element—the SQL query—as an input parameter. This changes the cache key only when the query changes, preserving caching for other runs. Rebuilding the image or disabling caching are less precise and more disruptive.

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Set the component's `enable_caching` argument to False in the pipeline definition.

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Modify the component's container image tag to a new version and rebuild the pipeline.

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Clear the pipeline's cache by deleting the pipeline run's metadata from Vertex ML Metadata.

Question 3

An ML engineer is designing a Vertex AI pipeline that trains a model, evaluates it, and conditionally deploys it only if the evaluation metric meets a threshold. The pipeline must pass the evaluation metric from the evaluation component to a downstream conditional. Which Vertex AI Pipelines feature should the engineer use to implement this flow?

Accepted Answer

Use the output of the evaluation component as an input parameter to a condition in the pipeline definition, referencing the component's output parameter.. In Vertex AI Pipelines, component outputs can be promoted to pipeline parameters, which can then be used in conditions to control downstream execution. The evaluation component should output the metric as a parameter, and the condition compares it to the threshold. This maintains a single, traceable pipeline run and avoids external services.

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Use the Vertex AI Model Registry to compare the new model's metric with the deployed model's metric and let the registry automatically deploy if better.

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Write the metric to a Cloud Storage file and use a Cloud Function to trigger a separate pipeline for deployment.

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Store the metric in a Vertex AI Experiment run and then use the Experiment's metric in the condition.

Question 4

A company is deploying a Vertex AI pipeline that trains a model and then runs a custom evaluation component. The evaluation component must only run if the training component succeeds and the model's accuracy exceeds a threshold. The pipeline must also support retries for transient errors in the training component. The engineer needs to configure the pipeline to meet these requirements. Which two actions should the engineer take? (Choose two.)

Accepted Answer

Set the `retry` policy on the training component to retry on specific exit codes or exceptions.. The evaluation component must run only when training succeeds and accuracy exceeds a threshold, so a dsl.Condition is used to gate its execution based on the accuracy metric. To handle transient errors in training, a retry policy on the training component is configured. These two actions together meet the requirements. Other options either do not enforce the condition, do not provide retries, or are not applicable.

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Configure the pipeline to run the evaluation component in parallel with training to reduce latency.

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Set `enable_caching=False` on the evaluation component to ensure it always runs after training.

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Use a `dsl.ExitHandler` to catch failures in the training component and retry it manually.

Question 5

A team is using Vertex AI Pipelines to orchestrate a training workflow. They want to ensure that the pipeline can be reproduced exactly six months later for auditing purposes. They need to capture all necessary information to rerun the pipeline and obtain identical results. (Choose two.)

Accepted Answer

Use a fixed pipeline template with pinned component versions and container image digests.. To reproduce a pipeline run exactly, you must pin the pipeline template and container images to immutable versions, and store the exact parameters and input artifacts in a versioned location. These two practices ensure that both the code and data are preserved, allowing an identical rerun. Other options like caching or experiment tracking do not provide the necessary immutability.

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Rely on the default caching behavior of Vertex AI Pipelines to reuse previous step outputs.

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Use the same pipeline name and run it again with the same parameters.

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Enable Vertex AI Experiments to log metrics and parameters for each run.

Question 6

An ML engineer is building a Vertex AI pipeline that must run a custom training component for each of 12 hyperparameter combinations. The component is defined as a custom Python function (Lightweight Python component). The engineer wants each combination to run as a separate parallel task so the pipeline completes faster, and wants the pipeline to fail fast if any single trial fails. Which approach should the engineer take?

Accepted Answer

Use a ParallelFor loop over the hyperparameter list and set the component's retry policy to 0.. Vertex AI Pipelines supports dsl.ParallelFor to fan out a list of values into parallel component tasks in one DAG, which is the idiomatic way to run 12 hyperparameter combinations concurrently. Setting retries to 0 on the component ensures a failed trial stops the pipeline rather than being retried and hidden. The other approaches either collapse trials into a single job, serialize them, or split them across pipelines, none of which meet the parallel fan-out and fail-fast requirements.

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Wrap the training component in a single Vertex AI CustomJob with a hyperparameter tuning job specification and let the service manage trials.

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Create 12 separate pipelines, one per hyperparameter combination, and schedule them with Cloud Scheduler at the same time.

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Define a sequential for-loop in the pipeline function that calls the training component 12 times in order.

Question 7

An ML engineer is creating a Vertex AI Pipeline that includes a component to train a model. The component requires a machine type with a GPU. The engineer wants to specify the machine type and GPU type for the component. Which approach should the engineer use?

Accepted Answer

Set the machine type and accelerator in the component's container specification using the Vertex AI SDK.. To specify a machine type and GPU for a Vertex AI Pipelines component, you set the resource requirements in the component's container specification. This ensures the component runs on the desired hardware. Other approaches like command-line arguments or global configuration do not correctly allocate the resources for the specific component.

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Configure the GPU in the pipeline's runtime configuration, which applies to all components.

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Use a pre-built training container from Vertex AI that automatically selects the appropriate GPU.

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Pass the machine type and GPU type as command-line arguments to the component's container.

Question 8

An ML engineer is designing a Vertex AI pipeline that includes a custom training component. The component must read training data from a Cloud Storage bucket and write the trained model to a Vertex AI Model Registry. The engineer wants to ensure the component can access these resources securely. Which two configurations should the engineer implement? (Choose two.)

Accepted Answer

Specify the pipeline's service account when submitting the pipeline run.. To securely access Cloud Storage and Vertex AI Model Registry, the pipeline's service account must have the required IAM roles, and that service account must be specified when submitting the pipeline run. This ensures the component authenticates with the correct permissions without embedding credentials.

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Use the default Compute Engine service account for all pipeline runs.

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Set the component's environment variable GOOGLE_APPLICATION_CREDENTIALS to point to a mounted secret.

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Embed a service account key file in the component's container image.

Question 9

An ML engineer is authoring a Vertex AI pipeline where a custom training component must read a dataset from a BigQuery table and write the trained model to a Cloud Storage bucket. The engineer wants the component to be reusable across projects and environments without hardcoding project IDs or bucket names. Which design should the engineer use?

Accepted Answer

Pass the BigQuery table URI and the Cloud Storage output URI as component input parameters, and let the pipeline caller supply them at runtime.. Component reusability in Vertex AI Pipelines comes from parameterizing all environment-specific values as inputs. Passing the BigQuery table URI and Cloud Storage output URI as component inputs lets the same component definition run in any project or environment, with the pipeline caller providing concrete values. The other options embed environment-specific values in the container or rely on implicit defaults, which breaks portability and can cause cross-environment mistakes.

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Use the Vertex AI SDK's aiplatform.init() with no arguments inside the component and rely on the default project and bucket.

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Read the project ID and bucket name from environment variables set inside the component's container image at build time.

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Hardcode the production project ID and bucket name in the component, then override them with a pipeline-level parameter only when running in non-production.

Question 10

An ML engineer is using Vertex AI Pipelines to orchestrate a workflow that includes a data preprocessing step, a training step, and an evaluation step. The evaluation step must run only if the training step succeeds and the evaluation metric meets a threshold. The engineer wants to define this logic natively in the pipeline without writing a custom component that exits with a specific code. Which Vertex AI Pipelines feature should they use?

Accepted Answer

Use the kfp.dsl.Condition context manager to conditionally execute the evaluation step based on the metric value.. Vertex AI Pipelines supports conditional execution through the kfp.dsl.Condition context manager. This allows you to branch based on the value of a task output, such as an evaluation metric. You can wrap the evaluation step in a Condition that checks if the metric exceeds a threshold. The other options either misuse trigger policies, misunderstand exit handlers, or rely on static pipeline parameters, none of which provide runtime conditional execution based on a metric.

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Use a pipeline parameter of type 'bool' to control whether the evaluation step runs, and set it based on the metric before submitting the pipeline.

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Configure the pipeline's 'exit handler' to check the metric and either continue or fail the pipeline.

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Set the evaluation step's 'trigger' policy to 'on_success' and pass the metric threshold as a pipeline parameter.

Question 11

A company runs a Vertex AI Pipeline that trains a model and then deploys it to a Vertex AI Endpoint. The pipeline uses a conditional deployment step based on the model's evaluation metric. The team wants to ensure that if the evaluation metric falls below a threshold, the pipeline fails and no deployment occurs. Which approach should they use?

Accepted Answer

Use a Condition to check the metric, and in the else branch, include a component that raises an exception or returns a failure status.. To conditionally deploy and fail the pipeline when the metric is unsatisfactory, the pipeline should use a Condition with an else branch that triggers a failing component. This ensures that deployment only happens when the metric passes, and the pipeline fails otherwise. Other methods either do not cause failure or rely on unsupported features.

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Add a custom component that evaluates the metric and, if below threshold, calls the Vertex AI API to cancel the pipeline.

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Configure the pipeline to use a fail-fast strategy by setting the pipeline's failure_policy to FAIL_FAST.

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Use a Condition in the pipeline that checks if the metric is greater than the threshold, and only then execute the deployment component.

Question 12

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 13

An ML engineer is running a Vertex AI pipeline that includes a data validation component and a training component. The engineer wants the pipeline to stop before training if data validation fails, but wants the validation component to record its result as an output artifact for later inspection. Which combination of pipeline features should the engineer use?

Accepted Answer

Have the validation component raise an exception on failure and write a validation report artifact before raising.. In Vertex AI Pipelines, a task that raises an exception fails, and dependent tasks are not executed. Writing the validation report artifact before raising ensures the artifact is captured for later inspection. The other options either let training proceed, rely on retries that do not address deterministic data failures, or move the validation check into the training component, none of which stop the pipeline before training while preserving the report.

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Configure the validation component with a retry policy of 3 so transient validation errors are retried, then let training proceed.

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Use a dsl.Condition to skip training when validation fails, and have the validation component always succeed while writing the report.

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Have the validation component write the report artifact and return a success status, then check the artifact contents in the training component before training.

Question 14

An ML engineer is designing a Vertex AI Pipeline that includes a hyperparameter tuning step. The tuning step runs multiple trials and outputs the best model. The engineer wants to ensure that the pipeline can resume from the tuning step if it fails later, without re-running all the tuning trials. What should the engineer do?

Accepted Answer

Enable caching for the tuning step by setting the component's 'enable_caching' attribute to True.. The correct method is to enable caching for the tuning component. Vertex AI Pipelines caching stores the outputs of a component based on a hash of its inputs, code, and environment. When the pipeline is re-run, if the tuning step's inputs are unchanged, the cached outputs are used, and the tuning trials are not re-executed. This saves time and cost, and allows the pipeline to resume from the tuning step.

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Configure the tuning step to use a Vertex AI HyperparameterTuningJob with a persistent resource pool.

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Store the tuning results in a Cloud Storage bucket and add a conditional step that checks for existing results before running tuning.

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Set the pipeline's 'resume' option to True when submitting the pipeline.

Question 15

A team is using Vertex AI Pipelines to orchestrate a machine learning workflow. They want to ensure that the pipeline can be reproduced with the same results even if the underlying data changes. Which practice should they follow?

Accepted Answer

Use a fixed random seed in the training component and snapshot the training data.. Reproducibility in ML pipelines requires controlling both the code and the data. A fixed random seed makes training deterministic, and snapshotting the training data ensures the same input is used across runs. Without data versioning, changes in the source data would lead to different results. Therefore, combining a fixed seed with data snapshots is the correct approach.

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Run the pipeline on a dedicated Vertex AI training cluster with the same machine type and accelerators.

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Version all pipeline components and pin their dependencies to specific versions.

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Store the pipeline definition in a Git repository and use the same pipeline template for all runs.