Google Cloud · Free Practice Questions · Last reviewed May 2026
42real exam-style questions organised by domain, each with the correct answer highlighted and a plain-English explanation of why it's right — and why the others are wrong.
18% of exam · 6 sample questions below
A machine learning engineer is building a Vertex AI pipeline that uses a pre-built Google Cloud Pipeline Components (GCPC) to train a custom model. Which component should the engineer use to submit a custom training job to Vertex AI?
HyperparameterTuningJob
CustomJob
The CustomJob component submits a custom training job to Vertex AI, letting the pipeline run bespoke training code within a managed job rather than a pre-built trainer. It satisfies the requirement to launch a custom training workload as a pipeline step.
BatchPredictionJob
ModelDeploy
A team has a Vertex AI pipeline that includes a container component for data preprocessing. The team notices that the component is re-executed every time the pipeline runs, even when the inputs and code haven't changed. They want to leverage pipeline caching to avoid redundant executions. What should they do to enable caching for this component?
Set the 'caching' flag to 'True' in the pipeline definition using 'pipeline.caching = True'.
Set the environment variable 'ENABLE_CACHE' to 'true' on the pipeline run request.
Re-compile the pipeline with the '--enable-cache' flag.
Ensure that the component does not have 'dsl.cache_options(enable_cache=False)' set.
Caching is enabled by default in Kubeflow Pipelines; the component re-executes because cache_options(enable_cache=False) was explicitly set, disabling it. Removing that setting restores default caching behaviour, so unchanged inputs and code reuse the cached execution instead of rerunning.
A data engineer wants to orchestrate a complex workflow that includes running a Vertex AI pipeline, then a BigQuery job, and finally a Dataflow pipeline. The workflow must handle dependencies, retries, and monitoring. Which Google Cloud service is most suitable for this orchestration?
Cloud Tasks
Cloud Composer
Cloud Composer is managed Apache Airflow, whose DAGs natively orchestrate heterogeneous tasks across Vertex AI, BigQuery and Dataflow with dependency handling, retries and monitoring. This satisfies the stem's requirement for cross-service workflow orchestration with dependencies and retries.
Cloud Scheduler
Workflows
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?
dsl.ParallelFor
dsl.ExitHandler
dsl.Condition
dsl.Condition wraps pipeline steps in a conditional branch whose predicate is evaluated at runtime, so the deployment step executes only when the evaluation metrics exceed the threshold. This directly implements the stem's conditional deployment requirement within the KFP pipeline definition.
dsl.Collected
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?
Add a 'ignoreFiles' field with 'training/**' to the trigger.
Use a 'substitutions' field with a regex pattern to filter commits.
Configure a Cloud Function to check the commit diff and call Cloud Build API conditionally.
Set the 'includedFiles' field to 'training/**' in the trigger configuration.
The includedFiles field with 'training/**' restricts the trigger to commits touching that directory, directly satisfying the requirement to run the pipeline only for training-code changes. Cloud Build evaluates this glob against changed paths before executing cloudbuild.yaml, avoiding unnecessary builds.
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?
Store the dataset as a Dataset artifact and pass the artifact between components.
Dataset artifacts are references to Cloud Storage or BigQuery URIs, so components exchange a lightweight metadata pointer rather than the bytes themselves. This satisfies the stem's large-dataset constraint, since passing raw data through component inputs would exceed the metadata limits.
Write the dataset to a temporary BigQuery table and pass the table name.
Serialize the dataset to a string and pass it as a pipeline parameter.
Use a Cloud Storage bucket and pass the bucket name as a parameter.
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Practice this domain20% of exam · 6 sample questions below
A data scientist wants to deploy a trained TensorFlow model to Vertex AI for online predictions. They need to serve predictions with low latency and want to leverage GPU acceleration. Which machine type should they select when creating the Vertex AI endpoint?
n1-standard-4 with 1 NVIDIA Tesla T4
NVIDIA Tesla T4 GPUs attached to n1-standard-4 instances provide the GPU acceleration the stem demands, while n1-standard-4 supplies sufficient vCPU and memory for low-latency online inference of a TensorFlow model. Vertex AI supports this accelerator pairing directly, satisfying both the GPU and latency constraints when deploying the endpoint.
n1-standard-4
e2-standard-4
n1-highmem-8
You need to serve a large embedding model for similarity search with low latency. The model was trained to generate 256-dimensional embeddings. You plan to use Vertex AI Vector Search. Which index type should you choose to balance accuracy and performance for a dataset with 10 million vectors?
Tree-based index
Approximate nearest neighbor (ANN) index using ScaNN
ScaNN builds an approximate nearest neighbour index, trading exact recall for substantially lower query latency and memory at 10 million vectors. This satisfies the stem's balance of accuracy and performance, where exact search would be too slow at that scale.
Brute-force index
Hash-based index
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?
Create a Vertex AI batch prediction job with BigQuery source and BigQuery destination.
Native BigQuery source and destination lets Vertex AI read and write directly without exporting 50 TB to Cloud Storage, avoiding costly intermediate copies. This satisfies the efficiency constraint by keeping data in place and streaming results back to BigQuery.
Use Dataflow to process the data and call the model via Vertex AI online prediction.
Export BigQuery data to CSV in GCS, then create a batch prediction job with GCS source.
Create a Cloud Function to iterate over BigQuery rows and call the endpoint.
You are deploying a PyTorch model on Vertex AI using a custom container with NVIDIA Triton Inference Server. The model is a large transformer that requires GPU. You want to optimize GPU utilization and reduce memory footprint. Which technique should you apply?
Enable dynamic batching in Triton.
Use CPU-only instances to avoid GPU memory issues.
Increase the number of GPU replicas.
Apply model quantization using TensorRT.
TensorRT quantisation converts FP32 weights to lower precision such as FP16 or INT8, shrinking memory footprint and boosting throughput on NVIDIA GPUs. Triton serves the optimised engine, so GPU utilisation improves while latency drops, satisfying the memory and utilisation constraints.
A company wants to cache predictions for identical requests to reduce latency and cost. They use Vertex AI Prediction with a custom container. Which GCP service should they use to implement prediction caching?
Cloud Bigtable
Cloud Memorystore for Redis
Memorystore for Redis provides sub-millisecond key-value lookups, letting the custom container return cached predictions for identical requests before invoking the model. This satisfies the latency and cost reduction goal, since repeated inference is skipped entirely.
Cloud Storage
Cloud Firestore
You have a Vertex AI endpoint that serves a model for real-time predictions. You want to update the model to a new version with zero downtime. Which approach should you take?
Delete the endpoint and recreate it with the new model.
Deploy the new model version to the same endpoint and then set traffic to 100% for the new version.
Deploying the new version alongside the existing one on the same endpoint, then shifting traffic to 100%, uses Vertex AI's traffic splitting to avoid downtime. Requests route to the old version until the switch, satisfying the zero-downtime requirement.
Use Cloud Load Balancing to switch traffic between two endpoints.
Create a new endpoint and update the client application to point to the new endpoint.
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Practice this domain13% of exam · 6 sample questions below
A retail company wants to predict customer churn using historical purchase data stored in BigQuery. The data includes customer demographics, transaction history, and support interactions. The team is comfortable writing SQL and wants to avoid moving data to a separate environment. Which approach should they take?
Use the Cloud Natural Language API to analyze customer support interactions and combine results with purchase data in BigQuery.
Export the data to a CSV file and use Vertex AI AutoML Tables to train a classification model.
Use BigQuery ML to create a logistic regression model (LOGISTIC_REG) on the data directly in BigQuery.
BigQuery ML trains LOGISTIC_REG models using SQL directly against BigQuery-resident data, so no extraction or separate environment is needed. This satisfies the team's SQL comfort and the constraint of avoiding data movement for churn prediction.
Create a Dataflow pipeline to stream data to Cloud SQL and use Cloud SQL's built-in ML functions.
A data scientist needs to train a time-series forecasting model on historical sales data stored in BigQuery to predict future demand. The data has strong seasonal patterns. Which BigQuery ML model type should they use?
MATRIX_FACTORIZATION
BOOSTED_TREE_REGRESSOR
ARIMA_PLUS
ARIMA_PLUS handles seasonality natively through automatic seasonal decomposition and multiple seasonal period detection, satisfying the strong seasonal patterns constraint in the historical sales data. It also supports forecasting horizons directly in BigQuery ML, letting the data scientist train and predict demand without exporting data.
K_MEANS
A healthcare provider needs to extract structured information from incoming PDF forms (e.g., patient intake forms). They want to automate data extraction without writing custom models. Which Google Cloud service should they use?
Document AI with a form parser processor
Document AI's form parser processor is a pretrained model that extracts key-value pairs and tables from PDFs, requiring no custom model training. This directly satisfies the constraint of automating structured extraction from intake forms without writing custom models.
Natural Language API for entity extraction
Vision API
AutoML Vision for object detection
A company wants to build a product recommendation engine for their e-commerce website. They have historical purchase data and user interaction logs. They want a managed service that can quickly generate personalized recommendations without building custom models. Which service should they use?
Dataflow with TensorFlow
BigQuery ML with MATRIX_FACTORIZATION
AutoML Tables
Recommendations AI
Recommendations AI is a managed Google Cloud service that trains on historical purchase and interaction data to serve personalised product recommendations, satisfying the requirement to avoid building custom models. It handles model training, tuning and serving, so the team gains personalisation without ML engineering effort.
A media company wants to automatically moderate user-uploaded videos by detecting explicit content (e.g., violence, adult material). They need a solution that integrates with their video processing pipeline and scales to millions of videos. Which approach should they take?
Use Video Intelligence API with explicit content detection
The Video Intelligence API provides explicit content detection purpose-built for video, analysing frames and audio for violence and adult material. It integrates into automated pipelines and scales elastically, satisfying the stem's requirement to moderate millions of videos without building custom models.
Use AutoML Video to train a custom explicit content detection model
Use Natural Language API on video transcripts
Use Vision API to analyze each video frame
A company wants to transcribe customer service calls in real-time to detect sentiment and identify urgent issues. They need a solution with low latency. Which combination of pre-built APIs should they use?
Text-to-Speech and Natural Language API
Speech-to-Text and Translation API
Video Intelligence API
Speech-to-Text and Natural Language API
Speech-to-Text streams audio into text with low latency, and the Natural Language API then performs sentiment analysis and entity detection on that text. Together they deliver real-time transcription plus sentiment and urgency identification without training custom models.
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Practice this domain20% of exam · 6 sample questions below
You have a TensorFlow training script that runs on a single machine. To speed up training on Vertex AI with 8 GPUs on a single machine, which strategy should you use?
tf.distribute.ParameterServerStrategy
tf.distribute.MirroredStrategy
MirroredStrategy performs synchronous, all-reduce data-parallel training across multiple GPUs within one machine, replicating the model on each device and aggregating gradients. This directly satisfies the stem's constraint of 8 GPUs on a single machine, where MultiWorkerMirroredStrategy would add unnecessary cross-machine networking overhead.
tf.distribute.TPUStrategy
tf.distribute.MultiWorkerMirroredStrategy
A data science team is building a feature engineering pipeline that processes large-scale data from BigQuery daily. They need to compute aggregate features and store the results in Vertex AI Feature Store for both online serving and offline training. Which Google Cloud service is best suited for this batch computation?
Cloud Composer
Dataproc
Cloud Functions
Dataflow
Dataflow runs Apache Beam pipelines that read from BigQuery, compute aggregates at scale, and write to Vertex AI Feature Store for both online serving and offline training. Its managed batch processing satisfies the daily large-scale computation requirement without provisioning servers.
You are fine-tuning a large language model (LLM) from Hugging Face Transformers using Vertex AI Training. The model has 7 billion parameters and does not fit into the memory of a single GPU. You need to train across multiple GPUs, splitting the model layers across devices. Which distributed training approach should you use?
Model parallelism using pipeline parallelism
Pipeline parallelism splits the model's layers across GPUs, with each device holding a subset and passing activations onward. This addresses the constraint that seven billion parameters exceed single-GPU memory, unlike data parallelism which replicates the full model per device.
Data parallelism with MultiWorkerMirroredStrategy
Mixed precision training (FP16)
Data parallelism with tf.distribute.MirroredStrategy
A company is using Vertex AI Vizier for hyperparameter tuning of a model with 5 integer hyperparameters, each with a range of 10-100. They have a budget of 50 trials and want to maximize the chance of finding the best configuration. Which Vizier algorithm should they use?
Grid search
Simulated annealing
Bayesian optimization (GP bandit)
Bayesian optimisation with a Gaussian process bandit models the objective surface and selects trials that balance exploration against exploitation, converging efficiently within a limited budget. With 50 trials across five integer parameters, it maximises the chance of locating the best configuration.
Random search
You want to use a pre-trained model from TensorFlow Hub for image classification, but you need to adapt it to classify your own custom categories with a small dataset. Which Vertex AI approach is most appropriate?
Write a custom training script that loads the pre-trained model and fine-tunes it on your dataset
Fine-tuning loads the TensorFlow Hub pre-trained model's weights into a custom training script and continues training on the small labelled dataset, adapting output categories. This suits limited data far better than training from scratch, which would require far more examples.
Deploy the pre-trained model as-is via Vertex AI JumpStart
Build a custom container with the pre-trained model and deploy to Vertex AI Endpoints
Use Vertex AI AutoML for image classification
Your Vertex AI custom training job is failing with an out-of-memory error on a single GPU. You need to reduce memory usage without changing the model architecture. Which approach should you try first?
Decrease the batch size
Reducing the batch size lowers the number of samples held in GPU memory per step, directly cutting activation and gradient memory consumption. It requires no architecture change, satisfying the stem's constraint, and is the least invasive first remedy before considering gradient checkpointing or mixed precision.
Implement model parallelism across GPUs
Use gradient accumulation
Enable mixed precision training (FP16)
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Practice this domain11% of exam · 6 sample questions below
A machine learning team wants to share features across multiple models to reduce training-serving skew and ensure consistency. Which Vertex AI service should they use?
Vertex AI Workbench
Vertex AI Model Registry
Vertex AI Feature Store
Vertex AI Feature Store provides a centralised repository where features are computed once and served identically to both training and prediction pipelines, directly eliminating training-serving skew. Sharing one feature definition across multiple models satisfies the consistency requirement, since online and batch serving draw from the same managed source rather than duplicated transformations.
Vertex AI Experiments
An organization uses Vertex AI Pipelines and wants to track the lineage of datasets, models, and metrics across pipeline runs. They need to query upstream and downstream dependencies of an artifact. Which service should they use?
Vertex AI Feature Store
Vertex AI Experiments
Vertex AI Model Registry
Vertex AI Metadata
Vertex AI Metadata stores pipeline resources as a lineage graph of executions, artifacts and contexts, so you can query upstream and downstream dependencies of any artifact. Cloud Logging records events but holds no typed lineage relationships between datasets, models and metrics.
A team uses Vertex AI Feature Store with an online store for low-latency serving. They need to support frequent updates to features (e.g., every minute) and require high write throughput (thousands of writes per second). Which online store type should they choose?
Optimized online store
Firestore online store
Bigtable online store
Bigtable online store supports high write throughput and frequent feature updates, scaling to thousands of writes per second with low-latency reads. This satisfies the stated requirement for minute-level updates and high-throughput ingestion that the default online store cannot match.
Cloud SQL online store
A data engineer needs to version large datasets (multiple TB) in a Data Lake on Google Cloud. They require ACID transactions to ensure consistency when multiple jobs read/write concurrently. Which solution should they use?
Delta Lake on Dataproc
Delta Lake provides ACID transactions and scalable metadata handling over Parquet files in Cloud Storage, letting concurrent Dataproc jobs read and write multi-terabyte datasets consistently. Its transaction log delivers the snapshot isolation and versioning the stem demands.
BigQuery table snapshots
DVC (Data Version Control)
Vertex AI Feature Store
A team wants to use Vertex AI Workbench for collaborative notebook development. They need a persistent environment that can be stopped and restarted without losing installed packages and data. Which instance type should they choose?
User-managed notebooks
User-managed notebooks give a persistent Compute Engine instance with a customisable environment, so installed packages and data survive stopping and restarting. This satisfies the stem's requirement for persistence, unlike managed notebooks with ephemeral or containerised runtimes.
Managed notebooks
Colab Enterprise notebooks
Vertex AI Pipelines
A team is building ML pipelines with Vertex AI. They want to reuse standard pipeline components across teams and enforce governance. What approach should they take?
Use Vertex AI Pipelines with pre-built and custom components organized in a component registry.
Vertex AI Pipelines executes containerised components, and storing pre-built and custom components in a component registry lets teams share and version them centrally, enforcing governance and reuse across teams. Components are defined by YAML specs, so standard interfaces are preserved.
Store pipeline definitions in a shared Cloud Storage bucket and copy them manually.
Use Cloud Composer to orchestrate ad-hoc scripts.
Have each team build their own pipelines independently.
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Practice this domain13% of exam · 6 sample questions below
A data scientist has deployed a model on Vertex AI Endpoints and wants to monitor the model's predictions for any drift over time. Which Vertex AI service should they use?
Vertex AI Feature Store
Vertex AI Predictions
Vertex AI Explainable AI
Vertex AI Model Monitoring
Vertex AI Model Monitoring continuously evaluates deployed endpoint predictions against a training baseline, detecting training-serving skew and prediction drift. It satisfies the requirement to monitor predictions over time, unlike feature-level logging or scheduled batch jobs, which capture data but perform no drift computation.
An MLOps engineer needs to collect ground truth labels for a deployed classification model to compare predictions against actuals. Where should the engineer store the ground truth data to enable Vertex AI model quality monitoring?
BigQuery
Vertex AI model quality monitoring reads ground truth labels from a BigQuery table, joining them against logged predictions to compute accuracy, precision and recall. Storing labels in BigQuery satisfies this requirement because the monitoring pipeline expects that source, unlike Cloud Storage objects or local files.
Firestore
Cloud Spanner
Cloud Storage
An ML team wants to automatically retrain a model when data drift is detected. They have set up a Cloud Monitoring alert on drift. What service should they use to trigger a retraining pipeline in response to the alert?
Cloud Functions
Cloud Functions provides an event-driven, serverless trigger that fires when the Cloud Monitoring drift alert publishes to a Pub/Sub topic, invoking the retraining pipeline. This satisfies the requirement to react automatically to drift alerts without managing servers.
Cloud Scheduler
Vertex AI Feature Store
Vertex AI Model Monitoring
A company has a model serving predictions on Vertex AI Endpoints and wants to monitor for prediction drift. They enable Vertex AI Model Monitoring but also need to see a confusion matrix over time. How should they set up the confusion matrix monitoring?
Use Cloud Monitoring to create a custom dashboard with a confusion matrix chart
Export predictions to Cloud Storage and run a Dataflow job to compute confusion matrices
Upload ground truth data to BigQuery and use Vertex AI Model Monitoring's model quality monitoring
Model quality monitoring computes confusion matrices, but it requires labelled ground truth to compare against predictions. Uploading ground truth to BigQuery supplies that reference data, satisfying the stem's need for confusion matrix metrics over time on the Vertex AI Endpoint.
Enable Vertex AI Explainable AI and configure it to output confusion matrices
A team is monitoring a model and observes that the error rate (prediction failures) has increased. They have enabled request/response logging on the Vertex AI Endpoint. How can they set up a metric and alert for prediction error rate?
Configure Cloud Monitoring to pull error rate from Cloud Endpoints
Use Vertex AI Model Monitoring to monitor error rate directly
Create a log-based metric in Cloud Logging for error logs and set up an alert in Cloud Monitoring
Request/response logging writes prediction failures to Cloud Logging, so a log-based metric counts matching error entries. Cloud Monitoring then alerts on that metric, satisfying the need to detect a rising prediction error rate from the endpoint's existing logs.
Enable Vertex AI Pipelines to track errors
A company has deployed a model for image classification and wants to monitor for feature drift using XRAI attributions. However, they notice that the XRAI attribution maps are too large and are causing high latency in the monitoring pipeline. What is the most effective way to reduce the overhead of explainability monitoring for image models?
Disable XRAI and use integrated gradients instead
Reduce the sampling rate for the explainability feature
XRAI attribution maps are generated per prediction, so reducing the sampling rate lowers how many explanations are computed, cutting pipeline latency. This satisfies the overhead constraint while still providing statistically useful drift signal from the sampled subset.
Use a smaller image size for the model
Increase the number of replicas on the endpoint
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Practice this domain5% of exam · 6 sample questions below
Which TWO statements about Vertex AI Feature Store are correct? (Choose 2)
Feature Store automatically applies feature engineering transformations.
Feature Store can only store numerical features.
Feature Store can only be used with Vertex AI models.
Feature Store provides a centralized repository for feature data.
Vertex AI Feature Store acts as a centralised repository, letting teams register, version and share feature data across projects and models instead of duplicating feature engineering per pipeline, which is the defining architectural property this statement asserts.
Feature Store supports both online and offline serving.
Vertex AI Feature Store separates online serving, which returns low-latency feature values for real-time prediction, from offline serving, which reads historical feature values from the offline store for training and batch scoring, so both serving modes are supported.
Which THREE actions are best practices for managing ML models in production on Google Cloud? (Choose 3)
Manually tune hyperparameters for each retraining run.
Monitor model performance and data drift continuously.
Continuous monitoring of model performance and data drift detects degradation after deployment, satisfying the production oversight requirement. Unlike static validation, it tracks live inference distributions against training baselines, triggering retraining when drift exceeds thresholds. This directly addresses the stem's demand for ongoing operational management of deployed ML models.
Use a central model registry for model governance.
A central model registry provides versioned lineage, stage transitions and approval gates for every artefact, directly satisfying the governance and reproducibility constraints of production ML. It decouples deployment from training, letting teams promote validated models while retaining audit trails — the mechanism the scenario demands for controlled, traceable releases.
Version all model artifacts and training datasets.
Versioning both model artefacts and training datasets creates an immutable lineage record, letting you reproduce any deployed model exactly and roll back to a prior artefact when performance degrades. This directly satisfies the stem's production-management constraint, since traceability between a prediction and the exact data and weights that produced it is otherwise unattainable.
Store all raw training data indefinitely for auditability.
A healthcare organization is building a machine learning model to predict patient readmission risk. They have sensitive data stored in BigQuery that includes protected health information (PHI). The data science team uses Vertex AI Workbench notebooks to explore the data and develop models. The organization's security policy requires that all PHI data must be encrypted at rest and in transit, and that access to the data is logged and audited. They also need to ensure that the data used for model training is de-identified to remove direct identifiers such as patient names and SSNs. The team wants to automate the de-identification process as part of the data pipeline. Which approach meets these requirements?
Create a Dataflow pipeline that reads from the original BigQuery table, applies Cloud DLP de-identification transforms, and writes to a new BigQuery table. Grant the data science team access to the de-identified table.
Cloud DLP de-identification transforms applied in a Dataflow pipeline remove direct identifiers before the data lands in a separate BigQuery table, satisfying the de-identification requirement while BigQuery's default encryption at rest and TLS in transit plus audit logging cover the remaining policy constraints.
Enable Shielded VM on Vertex AI Workbench notebooks and use VPC-SC to restrict data access.
Use Cloud Key Management Service to encrypt the PHI columns in BigQuery, and share the encryption key with the data science team.
Use BigQuery row-level security to mask PHI columns for the data science team, and train the model directly on the original table.
Drag and drop the steps to deploy a trained TensorFlow model to Vertex AI Prediction in the correct order.
Export the model, then upload to GCS, then register as a model, then deploy to endpoint, then test
This is the correct order because you must first export the trained model to a standard format (e.g., SavedModel), then upload it to Google Cloud Storage for accessibility, register it as a model resource in Vertex AI, deploy it to an endpoint for serving, and finally test the endpoint to ensure it works.
Upload to GCS, then export the model, then register as a model, then deploy to endpoint, then test
Export the model, then register as a model, then upload to GCS, then deploy to endpoint, then test
Export the model, then upload to GCS, then deploy to endpoint, then register as a model, then test
A team of ML engineers is collaborating on a project using Vertex AI. They want to ensure that only approved models are deployed to production. Which approach should they use?
Store all models in a Cloud Storage bucket and manually control access via IAM permissions.
Deploy models directly from training jobs to an endpoint without version tracking.
Use Vertex AI Model Registry with version aliases to manage model versions and promote them after approval.
Vertex AI Model Registry with version aliases lets the team track model versions and control which alias points to an approved artefact, so only vetted models reach production. Promotion after approval enforces the governance gate the scenario requires.
Use Cloud Dataflow to transform raw predictions and then store them in BigQuery for analysis.
A company uses a Cloud Composer DAG to run a daily ML pipeline that includes Dataflow jobs and model training on Vertex AI. The pipeline frequently fails due to insufficient permissions when the Dataflow worker accesses data in Cloud Storage. What is the most efficient way to resolve this issue?
Create a custom service account with required permissions and assign it to the Dataflow job.
Dataflow workers execute under a service account, so granting the required Cloud Storage permissions to a dedicated custom service account and assigning it to the job fixes the access failures directly. This is more efficient than broadly widening project-level roles.
Grant the 'roles/storage.objectViewer' role to 'allUsers' on the Cloud Storage bucket.
Use the Composer environment's service account for all pipeline components.
Move the Dataflow job to run after the pipeline so that data is already processed.
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Practice this domainThe PMLE exam has 60 questions and must be completed in 120 minutes. The passing score is 720/1000.
Scenario-based questions covering exam objectives with detailed answer explanations.
The exam covers 7 domains: Automating and Orchestrating ML Pipelines, Serving and Scaling Models, Architecting Low-Code ML Solutions, Scaling Prototypes into ML Models, Collaborating Within and Across Teams to Manage Data and Models, Monitoring ML Solutions, Collaborating to manage data and models. Questions are weighted by domain — higher-weight domains appear more on your actual exam.
No. These are original exam-style practice questions written against the official Google Cloud PMLE exam objectives. They are not copied from the real exam. Courseiva focuses on genuine understanding, not memorisation of braindumps.
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