PMLE Automating and Orchestrating ML Pipelines Practice Test 5 — 15 Questions

Question 1

A machine learning engineer is building a Vertex AI pipeline that uses a pre-built AutoML Tables component to train a classification model. The pipeline also includes a conditional step that deploys the model to an endpoint only if the evaluation metrics exceed a threshold. Which KFP feature should be used to implement the conditional deployment?

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

Accepted Answer

dsl.Condition. The `dsl.Condition` feature from KFP (Kubeflow Pipelines) is specifically designed to conditionally execute pipeline steps based on the output of a previous component. In this scenario, the AutoML Tables component produces evaluation metrics; `dsl.Condition` allows the pipeline to check whether those metrics exceed a threshold and, if true, run the deployment step. This is the correct, native KFP construct for implementing branching logic within a pipeline.

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dsl.ParallelFor

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dsl.ExitHandler

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dsl.Collected

Question 2

A team uses Cloud Build to automatically trigger a Vertex AI pipeline when changes are pushed to the model code repository. They have a cloudbuild.yaml file that builds a container image and submits the pipeline. However, they want to run the pipeline only if the commit includes changes to the 'training/' directory. Which Cloud Build configuration option should be used to filter the trigger?

Accepted Answer

Set the 'includedFiles' field to 'training/**' in the trigger configuration.. Cloud Build triggers support an `includedFiles` field that specifies a glob pattern. When set to `training/**`, the trigger will only fire if the commit includes changes to files under the `training/` directory. This is the native, declarative way to filter triggers based on changed file paths without additional infrastructure.

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Add a 'ignoreFiles' field with 'training/**' to the trigger.

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Use a 'substitutions' field with a regex pattern to filter commits.

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Configure a Cloud Function to check the commit diff and call Cloud Build API conditionally.

Question 3

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.

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

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

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

Question 4

A company runs a Vertex AI pipeline that uses a container component to preprocess data. The component downloads a large file from a public URL and saves the output to Cloud Storage. The pipeline fails intermittently with a 'timeout' error. Which THREE steps should the team take to improve reliability? (Choose three.)

Accepted Answer

Make the component idempotent by checking for existing output before processing.. Option A is correct because making the component idempotent (e.g., checking whether the output already exists in Cloud Storage before re-downloading and reprocessing) prevents redundant work on retries and ensures a re-run after a transient timeout does not corrupt or duplicate results. Option B is correct because the intermittent 'timeout' error indicates the component's configured timeout is too short for the large download plus preprocessing; raising the timeout setting gives the component enough time to finish on slow runs. Option D is correct because implementing retries with exponential backoff inside the component lets it recover from transient network or service failures when downloading from the public URL, which is the typical cause of intermittent timeouts. Option C is not appropriate because reducing the file size changes the data being processed and is not a reliability fix the team can simply apply to the existing pipeline. Option E is not appropriate because increasing the machine type addresses CPU or memory constraints, not a timeout caused by download duration or transient network failures.

Answer

Reduce the size of the file being downloaded.

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Increase the machine type for the component.

Question 5

A data scientist is creating a Vertex AI pipeline using the Kubeflow Pipelines SDK v2. Which TWO statements about pipeline parameters are correct? (Choose two.)

Accepted Answer

Pipeline parameters are defined as inputs to the pipeline function decorated with @dsl.pipeline.. Option A is correct because in the Kubeflow Pipelines SDK v2, pipeline parameters are declared as typed arguments of the function decorated with @dsl.pipeline, which defines the pipeline's input interface. Option D is correct because these parameters are runtime inputs: when submitting a run (e.g., via the Vertex AI Pipelines API or the SDK's create_run_from_pipeline_func), you can supply new values that override the defaults specified in the pipeline definition. Option B is wrong because KFP v2 handles parameter serialization automatically; you do not manually JSON-serialize parameters before use. Option C is wrong because KFP v2 parameters support multiple types such as int, float, bool, str, list, and dict, not only str. Option E is wrong because parameters are intended for small scalar/structured configuration values; large datasets should be passed between components as artifacts (e.g., Dataset, Model), not as parameters.

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Pipeline parameters must be serialized to JSON before use.

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Pipeline parameters can only be of type str.

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Pipeline parameters can be used to pass large datasets between components.

Question 6

A data science team wants to build a Vertex AI pipeline that trains a model, evaluates it, and conditionally deploys it if the accuracy exceeds 0.9. They want to use the Kubeflow Pipelines SDK v2. Which construct allows them to conditionally execute the deployment step based on the evaluation metric?

Accepted Answer

dsl.If. In Kubeflow Pipelines SDK v2, `dsl.If` is the correct construct for conditionally executing pipeline steps based on runtime metrics or parameters. It allows you to define a condition that, when evaluated to true, triggers the deployment step only if the model accuracy exceeds 0.9. This is the standard way to implement branching logic in v2 pipelines.

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dsl.Conditional

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dsl.ExitHandler

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dsl.Collected

Question 7

A team wants to implement continuous training for their ML model. The pipeline should be triggered when new training data arrives in a Cloud Storage bucket. Which combination of services should they use?

Accepted Answer

Cloud Storage → Cloud Functions → Vertex AI Pipelines. The correct combination is Cloud Storage → Cloud Functions → Vertex AI Pipelines. Cloud Storage hosts the new training data; a Cloud Function triggered by an object-finalize event (or Pub/Sub notification) detects the new data and invokes a Vertex AI Pipeline to retrain the model. This creates a fully event-driven continuous training pipeline without polling or manual intervention.

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Cloud Storage → BigQuery → Vertex AI Pipelines

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Cloud Storage → Cloud Build → Vertex AI Pipelines

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Cloud Storage → Cloud Scheduler → Vertex AI Pipelines

Question 8

You are using KFP SDK v2 to define a pipeline. You need to pass a large dataset between components. What is the best practice for passing data?

Accepted Answer

Write the data to Cloud Storage and pass the GCS URI as an artifact.. In KFP SDK v2, passing large datasets between components is best done by writing the data to Cloud Storage and passing the GCS URI as an artifact. This approach leverages KFP's built-in artifact tracking, ensures data persistence across container restarts, and avoids memory or disk limitations of ephemeral containers. The artifact is automatically serialized and passed as an input/output parameter, enabling efficient, scalable data exchange.

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Use the component's temporary directory to share data between containers.

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Pass the data as a serialized Python object in memory.

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Store the data in a BigQuery table and pass the table reference.

Question 9

You are defining a Python function component in KFP SDK v2. Which decorator should you use?

Accepted Answer

@dsl.component. In KFP SDK v2, the `@dsl.component` decorator is used to define a Python function as a lightweight, reusable pipeline component that can be executed independently. This decorator automatically generates a containerized component from the function's signature and type annotations, enabling type-safe inputs and outputs without requiring a separate component YAML specification.

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@dsl.task

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@component

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@dsl.pipeline

Question 10

You are building a CI/CD pipeline for an ML model using Cloud Build. When code is pushed to the main branch, you want to automatically build a training image, run a Vertex AI pipeline, and if the model evaluation passes, deploy it to a staging endpoint. Which two components are essential for this CI/CD pipeline?

Accepted Answer

Vertex AI Pipelines to orchestrate training and evaluation.. Option D is correct because a Cloud Build trigger is the native mechanism that responds to push events on the main branch, automatically starting the build of the training image and initiating the pipeline workflow. Option C is correct because Vertex AI Pipelines orchestrates the training and evaluation steps, and its evaluation component determines whether the model passes the quality gate before deployment to the staging endpoint. Option A is incorrect because Cloud Scheduler triggers on a time-based schedule, not on code push events, so it does not satisfy the push-to-main requirement. Option B is incorrect because Cloud Functions is not the deployment mechanism for Vertex AI models; deployment is handled through Vertex AI endpoints or pipeline components. Option E is incorrect because Vertex AI Continuous Training is a managed retraining feature, not the CI/CD orchestration component needed to trigger and gate deployments on code changes.

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Cloud Scheduler to trigger the pipeline on a schedule.

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Cloud Functions to deploy the model.

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Vertex AI Continuous Training service.

Question 11

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.

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

Question 12

An organization wants to trigger a Vertex AI pipeline whenever new data arrives in a Cloud Storage bucket. Which approach should they use?

Accepted Answer

Use Cloud Functions triggered by Cloud Storage events to call the Vertex AI pipeline API.. Cloud Functions can be directly triggered by Cloud Storage events (e.g., `google.storage.object.finalize`) and can then call the Vertex AI pipeline API using the Cloud SDK or client libraries. This provides a serverless, event-driven architecture that reacts immediately to new data without polling or additional infrastructure.

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Configure a Vertex AI pipeline trigger directly on the bucket using the GCP Console.

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Use Pub/Sub notifications from the bucket and a Dataflow job to start the pipeline.

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Set up a Cloud Scheduler job that runs every minute and checks for new files in the bucket.

Question 13

A team has a pipeline that trains a model and then evaluates it. They want to conditionally deploy the model to a staging endpoint only if evaluation metrics exceed a threshold. Which KFP feature should they use?

Accepted Answer

Use dsl.Condition (deprecated) or dsl.If to check metrics and conditionally run deployment.. KFP provides `dsl.Condition` (deprecated) and `dsl.If` as first-class pipeline constructs to conditionally execute pipeline components based on runtime metrics or other pipeline outputs. By wrapping the deployment step inside a `dsl.If` block that checks whether evaluation metrics exceed a threshold, the pipeline can deploy the model to a staging endpoint only when the condition is met, avoiding unnecessary deployments for underperforming models.

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Use dsl.ParallelFor to evaluate and deploy in parallel.

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Use an exit handler to deploy regardless of metrics.

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Split the pipeline into two separate pipelines and run the second only if metrics are good.

Question 14

What is the primary benefit of using pipeline caching in Vertex AI Pipelines?

Accepted Answer

It reduces execution time and cost by reusing unchanged component outputs.. Pipeline caching in Vertex AI Pipelines automatically detects when a component's inputs and code have not changed from a previous execution and reuses the cached output artifacts. This avoids redundant computation, directly reducing both execution time and cost by skipping re-execution of unchanged steps.

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It encrypts data at rest.

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It automatically scales the pipeline resources.

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It enables parallel execution of components.

Question 15

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.

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kfp.Client().upload_pipeline()

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dsl.pipeline decorator automatically compiles at runtime.