Google Professional Machine Learning Engineer Practice Exams
Last updated on Oct 09,2026- Exam Code: Professional Machine Learning Engineer
- Exam Name: Google Professional Machine Learning Engineer
- Certification Provider: Google
- Latest update: Oct 09,2026
Question #78
You have built a model that is trained on data stored in Parquet files. You access the data through a Hive table hosted on Google Cloud. You preprocessed these data with PySpark and exported it as a CSV file into Cloud Storage. After preprocessing, you execute additional steps to train and evaluate your model. You want to parametrize this model training in Kubeflow Pipelines.
What should you do?
- A . Remove the data transformation step from your pipeline.
- B . Containerize the PySpark transformation step, and add it to your pipeline.
- C . Add a ContainerOp to your pipeline that spins a Dataproc cluster, runs a transformation, and then saves the transformed data in Cloud Storage.
- D . Deploy Apache Spark at a separate node pool in a Google Kubernetes Engine cluster. Add a ContainerOp to your pipeline that invokes a corresponding transformation job for this Spark instance.
Correct Answer: C
C
Explanation:
The best option for parametrizing the model training in Kubeflow Pipelines is to add a ContainerOp to the pipeline that spins a Dataproc cluster, runs a transformation, and then saves the transformed data in Cloud Storage.
This option has the following advantages:
It allows the data transformation to be performed as part of the Kubeflow Pipeline, which can ensure the consistency and reproducibility of the data processing and the model training. By adding a ContainerOp to the pipeline, you can define the parameters and the logic of the data transformation step, and integrate it with the other steps of the pipeline, such as the model training and evaluation. It leverages the scalability and performance of Dataproc, which is a fully managed service that runs Apache Spark and Apache Hadoop clusters on Google Cloud. By spinning a Dataproc cluster, you can run the PySpark transformation on the Parquet files stored in the Hive table, and take advantage of the parallelism and speed of Spark. Dataproc also supports various features and integrations, such as autoscaling, preemptible VMs, and connectors to other Google Cloud services, that can optimize the data processing and reduce the cost.
It simplifies the data storage and access, as the transformed data is saved in Cloud Storage, which is a scalable, durable, and secure object storage service. By saving the transformed data in Cloud Storage, you can avoid the overhead and complexity of managing the data in the Hive table or the Parquet files. Moreover, you can easily access the transformed data from Cloud Storage, using various tools and frameworks, such as TensorFlow, BigQuery, or Vertex AI. The other options are less optimal for the following reasons:
Option A: Removing the data transformation step from the pipeline eliminates the parametrization of the model training, as the data processing and the model training are decoupled and independent. This option requires running the PySpark transformation separately from the Kubeflow Pipeline, which can introduce inconsistency and unreproducibility in the data processing and the model training. Moreover, this option requires managing the data in the Hive table or the Parquet files, which can be cumbersome and inefficient.
Option B: Containerizing the PySpark transformation step, and adding it to the pipeline introduces additional complexity and overhead. This option requires creating and maintaining a Docker image that can run the PySpark transformation, which can be challenging and time-consuming. Moreover, this option requires running the PySpark transformation on a single container, which can be slow and inefficient, as it does not leverage the parallelism and performance of Spark.
Option D: Deploying Apache Spark at a separate node pool in a Google Kubernetes Engine cluster, and adding a ContainerOp to the pipeline that invokes a corresponding transformation job for this Spark instance introduces additional complexity and cost. This option requires creating and managing a separate node pool in a Google Kubernetes Engine cluster, which is a fully managed service that runs Kubernetes clusters on Google Cloud. Moreover, this option requires deploying and running Apache Spark on the node pool, which can be tedious and costly, as it requires configuring and maintaining the Spark cluster, and paying for the node pool usage.
C
Explanation:
The best option for parametrizing the model training in Kubeflow Pipelines is to add a ContainerOp to the pipeline that spins a Dataproc cluster, runs a transformation, and then saves the transformed data in Cloud Storage.
This option has the following advantages:
It allows the data transformation to be performed as part of the Kubeflow Pipeline, which can ensure the consistency and reproducibility of the data processing and the model training. By adding a ContainerOp to the pipeline, you can define the parameters and the logic of the data transformation step, and integrate it with the other steps of the pipeline, such as the model training and evaluation. It leverages the scalability and performance of Dataproc, which is a fully managed service that runs Apache Spark and Apache Hadoop clusters on Google Cloud. By spinning a Dataproc cluster, you can run the PySpark transformation on the Parquet files stored in the Hive table, and take advantage of the parallelism and speed of Spark. Dataproc also supports various features and integrations, such as autoscaling, preemptible VMs, and connectors to other Google Cloud services, that can optimize the data processing and reduce the cost.
It simplifies the data storage and access, as the transformed data is saved in Cloud Storage, which is a scalable, durable, and secure object storage service. By saving the transformed data in Cloud Storage, you can avoid the overhead and complexity of managing the data in the Hive table or the Parquet files. Moreover, you can easily access the transformed data from Cloud Storage, using various tools and frameworks, such as TensorFlow, BigQuery, or Vertex AI. The other options are less optimal for the following reasons:
Option A: Removing the data transformation step from the pipeline eliminates the parametrization of the model training, as the data processing and the model training are decoupled and independent. This option requires running the PySpark transformation separately from the Kubeflow Pipeline, which can introduce inconsistency and unreproducibility in the data processing and the model training. Moreover, this option requires managing the data in the Hive table or the Parquet files, which can be cumbersome and inefficient.
Option B: Containerizing the PySpark transformation step, and adding it to the pipeline introduces additional complexity and overhead. This option requires creating and maintaining a Docker image that can run the PySpark transformation, which can be challenging and time-consuming. Moreover, this option requires running the PySpark transformation on a single container, which can be slow and inefficient, as it does not leverage the parallelism and performance of Spark.
Option D: Deploying Apache Spark at a separate node pool in a Google Kubernetes Engine cluster, and adding a ContainerOp to the pipeline that invokes a corresponding transformation job for this Spark instance introduces additional complexity and cost. This option requires creating and managing a separate node pool in a Google Kubernetes Engine cluster, which is a fully managed service that runs Kubernetes clusters on Google Cloud. Moreover, this option requires deploying and running Apache Spark on the node pool, which can be tedious and costly, as it requires configuring and maintaining the Spark cluster, and paying for the node pool usage.
Question #82
You are developing a model to identify traffic signs in images extracted from videos taken from the dashboard of a vehicle. You have a dataset of 100 000 images that were cropped to show one out of ten different traffic signs. The images have been labeled accordingly for model training and are stored in a Cloud Storage bucket You need to be able to tune the model during each training run.
How should you train the model?
- A . Train a model for object detection by using Vertex Al AutoML.
- B . Train a model for image classification by using Vertex Al AutoML.
- C . Develop the model training code for object detection and tram a model by using Vertex Al custom training.
- D . Develop the model training code for image classification and train a model by using Vertex Al custom training.
Correct Answer: D
D
Explanation:
Image classification is a task where the model assigns a label to an image based on its content, such as “stop sign” or "speed limit"1. Object detection is a task where the model locates and identifies multiple objects in an image, and draws bounding boxes around them2. Since your dataset consists of images that were cropped to show one out of ten different traffic signs, you are dealing with an image classification problem, not an object detection problem. Therefore, you need to train a model for image classification, not object detection.
Vertex AI AutoML is a service that allows you to train and deploy high-quality ML models with minimal effort and machine learning expertise3. You can use Vertex AI AutoML to train a model for image classification by uploading your images and labels to a Vertex AI dataset, and then launching an AutoML training job4. However, Vertex AI AutoML does not allow you to tune the model during each training run, as it automatically selects the best model architecture and hyperparameters for your data4.
Vertex AI custom training is a service that allows you to train and deploy your own custom ML models using your own code and frameworks5. You can use Vertex AI custom training to train a model for image classification by writing your own model training code, such as using TensorFlow or PyTorch, and then creating and running a custom training job. Vertex AI custom training allows you to tune the model during each training run, as you can specify the model architecture and hyperparameters in your code, and use Vertex AI Hyperparameter Tuning to optimize them. Therefore, the best option for your scenario is to develop the model training code for image classification and train a model by using Vertex AI custom training.
Reference: Image classification | TensorFlow Core
Object detection | TensorFlow Core
Introduction to Vertex AI AutoML | Google Cloud
AutoML Vision | Google Cloud
Introduction to Vertex AI custom training | Google Cloud [Custom training with TensorFlow | Vertex AI | Google Cloud] [Hyperparameter tuning overview | Vertex AI | Google Cloud]
D
Explanation:
Image classification is a task where the model assigns a label to an image based on its content, such as “stop sign” or "speed limit"1. Object detection is a task where the model locates and identifies multiple objects in an image, and draws bounding boxes around them2. Since your dataset consists of images that were cropped to show one out of ten different traffic signs, you are dealing with an image classification problem, not an object detection problem. Therefore, you need to train a model for image classification, not object detection.
Vertex AI AutoML is a service that allows you to train and deploy high-quality ML models with minimal effort and machine learning expertise3. You can use Vertex AI AutoML to train a model for image classification by uploading your images and labels to a Vertex AI dataset, and then launching an AutoML training job4. However, Vertex AI AutoML does not allow you to tune the model during each training run, as it automatically selects the best model architecture and hyperparameters for your data4.
Vertex AI custom training is a service that allows you to train and deploy your own custom ML models using your own code and frameworks5. You can use Vertex AI custom training to train a model for image classification by writing your own model training code, such as using TensorFlow or PyTorch, and then creating and running a custom training job. Vertex AI custom training allows you to tune the model during each training run, as you can specify the model architecture and hyperparameters in your code, and use Vertex AI Hyperparameter Tuning to optimize them. Therefore, the best option for your scenario is to develop the model training code for image classification and train a model by using Vertex AI custom training.
Reference: Image classification | TensorFlow Core
Object detection | TensorFlow Core
Introduction to Vertex AI AutoML | Google Cloud
AutoML Vision | Google Cloud
Introduction to Vertex AI custom training | Google Cloud [Custom training with TensorFlow | Vertex AI | Google Cloud] [Hyperparameter tuning overview | Vertex AI | Google Cloud]
Question #82
You are developing a model to identify traffic signs in images extracted from videos taken from the dashboard of a vehicle. You have a dataset of 100 000 images that were cropped to show one out of ten different traffic signs. The images have been labeled accordingly for model training and are stored in a Cloud Storage bucket You need to be able to tune the model during each training run.
How should you train the model?
- A . Train a model for object detection by using Vertex Al AutoML.
- B . Train a model for image classification by using Vertex Al AutoML.
- C . Develop the model training code for object detection and tram a model by using Vertex Al custom training.
- D . Develop the model training code for image classification and train a model by using Vertex Al custom training.
Correct Answer: D
D
Explanation:
Image classification is a task where the model assigns a label to an image based on its content, such as “stop sign” or "speed limit"1. Object detection is a task where the model locates and identifies multiple objects in an image, and draws bounding boxes around them2. Since your dataset consists of images that were cropped to show one out of ten different traffic signs, you are dealing with an image classification problem, not an object detection problem. Therefore, you need to train a model for image classification, not object detection.
Vertex AI AutoML is a service that allows you to train and deploy high-quality ML models with minimal effort and machine learning expertise3. You can use Vertex AI AutoML to train a model for image classification by uploading your images and labels to a Vertex AI dataset, and then launching an AutoML training job4. However, Vertex AI AutoML does not allow you to tune the model during each training run, as it automatically selects the best model architecture and hyperparameters for your data4.
Vertex AI custom training is a service that allows you to train and deploy your own custom ML models using your own code and frameworks5. You can use Vertex AI custom training to train a model for image classification by writing your own model training code, such as using TensorFlow or PyTorch, and then creating and running a custom training job. Vertex AI custom training allows you to tune the model during each training run, as you can specify the model architecture and hyperparameters in your code, and use Vertex AI Hyperparameter Tuning to optimize them. Therefore, the best option for your scenario is to develop the model training code for image classification and train a model by using Vertex AI custom training.
Reference: Image classification | TensorFlow Core
Object detection | TensorFlow Core
Introduction to Vertex AI AutoML | Google Cloud
AutoML Vision | Google Cloud
Introduction to Vertex AI custom training | Google Cloud [Custom training with TensorFlow | Vertex AI | Google Cloud] [Hyperparameter tuning overview | Vertex AI | Google Cloud]
D
Explanation:
Image classification is a task where the model assigns a label to an image based on its content, such as “stop sign” or "speed limit"1. Object detection is a task where the model locates and identifies multiple objects in an image, and draws bounding boxes around them2. Since your dataset consists of images that were cropped to show one out of ten different traffic signs, you are dealing with an image classification problem, not an object detection problem. Therefore, you need to train a model for image classification, not object detection.
Vertex AI AutoML is a service that allows you to train and deploy high-quality ML models with minimal effort and machine learning expertise3. You can use Vertex AI AutoML to train a model for image classification by uploading your images and labels to a Vertex AI dataset, and then launching an AutoML training job4. However, Vertex AI AutoML does not allow you to tune the model during each training run, as it automatically selects the best model architecture and hyperparameters for your data4.
Vertex AI custom training is a service that allows you to train and deploy your own custom ML models using your own code and frameworks5. You can use Vertex AI custom training to train a model for image classification by writing your own model training code, such as using TensorFlow or PyTorch, and then creating and running a custom training job. Vertex AI custom training allows you to tune the model during each training run, as you can specify the model architecture and hyperparameters in your code, and use Vertex AI Hyperparameter Tuning to optimize them. Therefore, the best option for your scenario is to develop the model training code for image classification and train a model by using Vertex AI custom training.
Reference: Image classification | TensorFlow Core
Object detection | TensorFlow Core
Introduction to Vertex AI AutoML | Google Cloud
AutoML Vision | Google Cloud
Introduction to Vertex AI custom training | Google Cloud [Custom training with TensorFlow | Vertex AI | Google Cloud] [Hyperparameter tuning overview | Vertex AI | Google Cloud]
Question #82
You are developing a model to identify traffic signs in images extracted from videos taken from the dashboard of a vehicle. You have a dataset of 100 000 images that were cropped to show one out of ten different traffic signs. The images have been labeled accordingly for model training and are stored in a Cloud Storage bucket You need to be able to tune the model during each training run.
How should you train the model?
- A . Train a model for object detection by using Vertex Al AutoML.
- B . Train a model for image classification by using Vertex Al AutoML.
- C . Develop the model training code for object detection and tram a model by using Vertex Al custom training.
- D . Develop the model training code for image classification and train a model by using Vertex Al custom training.
Correct Answer: D
D
Explanation:
Image classification is a task where the model assigns a label to an image based on its content, such as “stop sign” or "speed limit"1. Object detection is a task where the model locates and identifies multiple objects in an image, and draws bounding boxes around them2. Since your dataset consists of images that were cropped to show one out of ten different traffic signs, you are dealing with an image classification problem, not an object detection problem. Therefore, you need to train a model for image classification, not object detection.
Vertex AI AutoML is a service that allows you to train and deploy high-quality ML models with minimal effort and machine learning expertise3. You can use Vertex AI AutoML to train a model for image classification by uploading your images and labels to a Vertex AI dataset, and then launching an AutoML training job4. However, Vertex AI AutoML does not allow you to tune the model during each training run, as it automatically selects the best model architecture and hyperparameters for your data4.
Vertex AI custom training is a service that allows you to train and deploy your own custom ML models using your own code and frameworks5. You can use Vertex AI custom training to train a model for image classification by writing your own model training code, such as using TensorFlow or PyTorch, and then creating and running a custom training job. Vertex AI custom training allows you to tune the model during each training run, as you can specify the model architecture and hyperparameters in your code, and use Vertex AI Hyperparameter Tuning to optimize them. Therefore, the best option for your scenario is to develop the model training code for image classification and train a model by using Vertex AI custom training.
Reference: Image classification | TensorFlow Core
Object detection | TensorFlow Core
Introduction to Vertex AI AutoML | Google Cloud
AutoML Vision | Google Cloud
Introduction to Vertex AI custom training | Google Cloud [Custom training with TensorFlow | Vertex AI | Google Cloud] [Hyperparameter tuning overview | Vertex AI | Google Cloud]
D
Explanation:
Image classification is a task where the model assigns a label to an image based on its content, such as “stop sign” or "speed limit"1. Object detection is a task where the model locates and identifies multiple objects in an image, and draws bounding boxes around them2. Since your dataset consists of images that were cropped to show one out of ten different traffic signs, you are dealing with an image classification problem, not an object detection problem. Therefore, you need to train a model for image classification, not object detection.
Vertex AI AutoML is a service that allows you to train and deploy high-quality ML models with minimal effort and machine learning expertise3. You can use Vertex AI AutoML to train a model for image classification by uploading your images and labels to a Vertex AI dataset, and then launching an AutoML training job4. However, Vertex AI AutoML does not allow you to tune the model during each training run, as it automatically selects the best model architecture and hyperparameters for your data4.
Vertex AI custom training is a service that allows you to train and deploy your own custom ML models using your own code and frameworks5. You can use Vertex AI custom training to train a model for image classification by writing your own model training code, such as using TensorFlow or PyTorch, and then creating and running a custom training job. Vertex AI custom training allows you to tune the model during each training run, as you can specify the model architecture and hyperparameters in your code, and use Vertex AI Hyperparameter Tuning to optimize them. Therefore, the best option for your scenario is to develop the model training code for image classification and train a model by using Vertex AI custom training.
Reference: Image classification | TensorFlow Core
Object detection | TensorFlow Core
Introduction to Vertex AI AutoML | Google Cloud
AutoML Vision | Google Cloud
Introduction to Vertex AI custom training | Google Cloud [Custom training with TensorFlow | Vertex AI | Google Cloud] [Hyperparameter tuning overview | Vertex AI | Google Cloud]
Question #85
You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano, scikit-learn, and custom libraries.
What should you do?
- A . Use the Vertex AI Training to submit training jobs using any framework.
- B . Configure Kubeflow to run on Google Kubernetes Engine and submit training jobs through TFJob.
- C . Create a library of VM images on Compute Engine, and publish these images on a centralized repository.
- D . Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.
Correct Answer: A
A
Explanation:
The best option for using a managed service to submit training jobs with different frameworks is to use Vertex AI Training. Vertex AI Training is a fully managed service that allows you to train custom models on Google Cloud using any framework, such as TensorFlow, PyTorch, scikit-learn, XGBoost, etc. You can also use custom containers to run your own libraries and dependencies. Vertex AI Training handles the infrastructure provisioning, scaling, and monitoring for you, so you can focus on your model development and optimization. Vertex AI Training also integrates with other Vertex AI services, such as Vertex AI Pipelines, Vertex AI Experiments, and Vertex AI Prediction.
The other options are not as suitable for using a managed service to submit training jobs with different frameworks, because:
Configuring Kubeflow to run on Google Kubernetes Engine and submit training jobs through TFJob would require more infrastructure maintenance, as Kubeflow is not a fully managed service, and you would have to provision and manage your own Kubernetes cluster. This would also incur more costs, as you would have to pay for the cluster resources, regardless of the training job usage. TFJob is also mainly designed for TensorFlow models, and might not support other frameworks as well as Vertex AI Training.
Creating a library of VM images on Compute Engine, and publishing these images on a centralized repository would require more development time and effort, as you would have to create and maintain different VM images for different frameworks and libraries. You would also have to manually configure and launch the VMs for each training job, and handle the scaling and monitoring yourself. This would not leverage the benefits of a managed service, such as Vertex AI Training. Setting up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure would require more configuration and administration, as Slurm is not a native Google Cloud service, and you would have to install and manage it on your own VMs or clusters. Slurm is also a general-purpose workload manager, and might not have the same level of integration and optimization for ML frameworks and libraries as Vertex AI Training.
Reference: Vertex AI Training | Google Cloud
Kubeflow on Google Cloud | Google Cloud
TFJob for training TensorFlow models with Kubernetes | Kubeflow
Compute Engine | Google Cloud
Slurm Workload Manager
A
Explanation:
The best option for using a managed service to submit training jobs with different frameworks is to use Vertex AI Training. Vertex AI Training is a fully managed service that allows you to train custom models on Google Cloud using any framework, such as TensorFlow, PyTorch, scikit-learn, XGBoost, etc. You can also use custom containers to run your own libraries and dependencies. Vertex AI Training handles the infrastructure provisioning, scaling, and monitoring for you, so you can focus on your model development and optimization. Vertex AI Training also integrates with other Vertex AI services, such as Vertex AI Pipelines, Vertex AI Experiments, and Vertex AI Prediction.
The other options are not as suitable for using a managed service to submit training jobs with different frameworks, because:
Configuring Kubeflow to run on Google Kubernetes Engine and submit training jobs through TFJob would require more infrastructure maintenance, as Kubeflow is not a fully managed service, and you would have to provision and manage your own Kubernetes cluster. This would also incur more costs, as you would have to pay for the cluster resources, regardless of the training job usage. TFJob is also mainly designed for TensorFlow models, and might not support other frameworks as well as Vertex AI Training.
Creating a library of VM images on Compute Engine, and publishing these images on a centralized repository would require more development time and effort, as you would have to create and maintain different VM images for different frameworks and libraries. You would also have to manually configure and launch the VMs for each training job, and handle the scaling and monitoring yourself. This would not leverage the benefits of a managed service, such as Vertex AI Training. Setting up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure would require more configuration and administration, as Slurm is not a native Google Cloud service, and you would have to install and manage it on your own VMs or clusters. Slurm is also a general-purpose workload manager, and might not have the same level of integration and optimization for ML frameworks and libraries as Vertex AI Training.
Reference: Vertex AI Training | Google Cloud
Kubeflow on Google Cloud | Google Cloud
TFJob for training TensorFlow models with Kubernetes | Kubeflow
Compute Engine | Google Cloud
Slurm Workload Manager
Question #86
You have trained a text classification model in TensorFlow using Al Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead.
What should you do?
- A . Export the model to BigQuery ML.
- B . Deploy and version the model on Al Platform.
- C . Use Dataflow with the SavedModel to read the data from BigQuery
- D . Submit a batch prediction job on Al Platform that points to the model location in Cloud Storage.
Correct Answer: D
D
Explanation:
This answer is correct because it allows you to use the trained TensorFlow model for batch predictions on text data stored in BigQuery without any additional processing or overhead. Al Platform provides a service for running batch prediction jobs that can take input data from BigQuery or Cloud Storage and write the output to BigQuery or Cloud Storage. You can use the SavedModel format to export your TensorFlow model to Cloud Storage and then submit a batch prediction job that points to the model location and the input data location. Al Platform will handle the scaling and distribution of the prediction requests and return the results in the specified output location.
Reference: [Al Platform: Batch prediction overview]
[Al Platform: Exporting a SavedModel for prediction]
D
Explanation:
This answer is correct because it allows you to use the trained TensorFlow model for batch predictions on text data stored in BigQuery without any additional processing or overhead. Al Platform provides a service for running batch prediction jobs that can take input data from BigQuery or Cloud Storage and write the output to BigQuery or Cloud Storage. You can use the SavedModel format to export your TensorFlow model to Cloud Storage and then submit a batch prediction job that points to the model location and the input data location. Al Platform will handle the scaling and distribution of the prediction requests and return the results in the specified output location.
Reference: [Al Platform: Batch prediction overview]
[Al Platform: Exporting a SavedModel for prediction]
Question #87
Your team is training a large number of ML models that use different algorithms, parameters and datasets. Some models are trained in Vertex Ai Pipelines, and some are trained on Vertex Al Workbench notebook instances. Your team wants to compare the performance of the models across both services. You want to minimize the effort required to store the parameters and metrics.
What should you do?
- A . Implement an additional step for all the models running in pipelines and notebooks to export parameters and metrics to BigQuery.
- B . Create a Vertex Al experiment Submit all the pipelines as experiment runs. For models trained on notebooks log parameters and metrics by using the Vertex Al SDK.
- C . Implement all models in Vertex Al Pipelines Create a Vertex Al experiment, and associate all pipeline runs with that experiment.
- D . Store all model parameters and metrics as mode! metadata by using the Vertex Al Metadata API.
Correct Answer: B
B
Explanation:
Vertex AI Experiments is a service that allows you to track, compare, and manage experiments with Vertex AI. You can use Vertex AI Experiments to record the parameters, metrics, and artifacts of each model training run, and compare them in a graphical interface. Vertex AI Experiments supports models trained in Vertex AI Pipelines, Vertex AI Custom Training, and Vertex AI Workbench notebooks. To use Vertex AI Experiments, you need to create an experiment and submit your pipeline runs or custom training jobs as experiment runs. For models trained on notebooks, you need to use the Vertex AI SDK to log the parameters and metrics to the experiment. This way, you can minimize the effort required to store and compare the model performance across different services.
Reference: Track, compare, manage experiments with Vertex AI Experiments, Vertex AI
Pipelines: Metrics visualization and run comparison using the KFP SDK, [Vertex AI SDK for Python]
B
Explanation:
Vertex AI Experiments is a service that allows you to track, compare, and manage experiments with Vertex AI. You can use Vertex AI Experiments to record the parameters, metrics, and artifacts of each model training run, and compare them in a graphical interface. Vertex AI Experiments supports models trained in Vertex AI Pipelines, Vertex AI Custom Training, and Vertex AI Workbench notebooks. To use Vertex AI Experiments, you need to create an experiment and submit your pipeline runs or custom training jobs as experiment runs. For models trained on notebooks, you need to use the Vertex AI SDK to log the parameters and metrics to the experiment. This way, you can minimize the effort required to store and compare the model performance across different services.
Reference: Track, compare, manage experiments with Vertex AI Experiments, Vertex AI
Pipelines: Metrics visualization and run comparison using the KFP SDK, [Vertex AI SDK for Python]
Question #88
You work for an international manufacturing organization that ships scientific products all over the world Instruction manuals for these products need to be translated to 15 different languages Your organization’s leadership team wants to start using machine learning to reduce the cost of manual human translations and increase translation speed. You need to implement a scalable solution that maximizes accuracy and minimizes operational overhead. You also want to include a process to evaluate and fix incorrect translations.
What should you do?
- A . Create a workflow using Cloud Function Triggers Configure a Cloud Function that is triggered when documents are uploaded to an input Cloud Storage bucket Configure another Cloud Function that translates the documents using the Cloud Translation API and saves the translations to an output Cloud Storage bucket Use human reviewers to evaluate the incorrect translations.
- B . Create a Vertex Al pipeline that processes the documents1 launches an AutoML Translation training job evaluates the translations, and deploys the model to a Vertex Al endpoint with autoscaling and model monitoring When there is a predetermined skew between training and live data re-trigger the pipeline with the latest data.
- C . Use AutoML Translation to tram a model Configure a Translation Hub project and use the trained model to translate the documents Use human reviewers to evaluate the incorrect translations
- D . Use Vertex Al custom training jobs to fine-tune a state-of-the-art open source pretrained model with your data Deploy the model to a Vertex Al endpoint with autoscaling and model monitoring When there is a predetermined skew between the training and live data, configure a trigger to run another training job with the latest data.
Correct Answer: C
C
Explanation:
AutoML Translation is a service that allows you to create and train custom ML models for translating text between different languages. You can use AutoML Translation to train a model that can translate instruction manuals for scientific products to 15 different languages. You can also use Translation Hub to configure a project and use the trained model to translate the documents. Translation Hub is a service that allows you to manage and automate your translation workflows on Google Cloud. You can use Translation Hub to upload the documents to a Cloud Storage bucket, select the source and target languages, and apply the trained model to translate the documents. You can also use Translation Hub to download the translated documents or save them to another Cloud Storage bucket. You can also use human reviewers to evaluate the incorrect translations. Human reviewers are people who can review and correct the translations produced by the ML model. You can use human reviewers to improve the quality and accuracy of the translations, and provide feedback to the ML model. You can use Translation Hub to integrate with third-party human review services, such as Google Translate Community or Appen. By using AutoML Translation, Translation Hub, and human reviewers, you can implement a scalable solution that maximizes accuracy and minimizes operational overhead. You can also include a process to evaluate and fix incorrect translations.
Reference: [AutoML Translation documentation]
[Translation Hub documentation]
[Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate]
C
Explanation:
AutoML Translation is a service that allows you to create and train custom ML models for translating text between different languages. You can use AutoML Translation to train a model that can translate instruction manuals for scientific products to 15 different languages. You can also use Translation Hub to configure a project and use the trained model to translate the documents. Translation Hub is a service that allows you to manage and automate your translation workflows on Google Cloud. You can use Translation Hub to upload the documents to a Cloud Storage bucket, select the source and target languages, and apply the trained model to translate the documents. You can also use Translation Hub to download the translated documents or save them to another Cloud Storage bucket. You can also use human reviewers to evaluate the incorrect translations. Human reviewers are people who can review and correct the translations produced by the ML model. You can use human reviewers to improve the quality and accuracy of the translations, and provide feedback to the ML model. You can use Translation Hub to integrate with third-party human review services, such as Google Translate Community or Appen. By using AutoML Translation, Translation Hub, and human reviewers, you can implement a scalable solution that maximizes accuracy and minimizes operational overhead. You can also include a process to evaluate and fix incorrect translations.
Reference: [AutoML Translation documentation]
[Translation Hub documentation]
[Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate]
Question #88
You work for an international manufacturing organization that ships scientific products all over the world Instruction manuals for these products need to be translated to 15 different languages Your organization’s leadership team wants to start using machine learning to reduce the cost of manual human translations and increase translation speed. You need to implement a scalable solution that maximizes accuracy and minimizes operational overhead. You also want to include a process to evaluate and fix incorrect translations.
What should you do?
- A . Create a workflow using Cloud Function Triggers Configure a Cloud Function that is triggered when documents are uploaded to an input Cloud Storage bucket Configure another Cloud Function that translates the documents using the Cloud Translation API and saves the translations to an output Cloud Storage bucket Use human reviewers to evaluate the incorrect translations.
- B . Create a Vertex Al pipeline that processes the documents1 launches an AutoML Translation training job evaluates the translations, and deploys the model to a Vertex Al endpoint with autoscaling and model monitoring When there is a predetermined skew between training and live data re-trigger the pipeline with the latest data.
- C . Use AutoML Translation to tram a model Configure a Translation Hub project and use the trained model to translate the documents Use human reviewers to evaluate the incorrect translations
- D . Use Vertex Al custom training jobs to fine-tune a state-of-the-art open source pretrained model with your data Deploy the model to a Vertex Al endpoint with autoscaling and model monitoring When there is a predetermined skew between the training and live data, configure a trigger to run another training job with the latest data.
Correct Answer: C
C
Explanation:
AutoML Translation is a service that allows you to create and train custom ML models for translating text between different languages. You can use AutoML Translation to train a model that can translate instruction manuals for scientific products to 15 different languages. You can also use Translation Hub to configure a project and use the trained model to translate the documents. Translation Hub is a service that allows you to manage and automate your translation workflows on Google Cloud. You can use Translation Hub to upload the documents to a Cloud Storage bucket, select the source and target languages, and apply the trained model to translate the documents. You can also use Translation Hub to download the translated documents or save them to another Cloud Storage bucket. You can also use human reviewers to evaluate the incorrect translations. Human reviewers are people who can review and correct the translations produced by the ML model. You can use human reviewers to improve the quality and accuracy of the translations, and provide feedback to the ML model. You can use Translation Hub to integrate with third-party human review services, such as Google Translate Community or Appen. By using AutoML Translation, Translation Hub, and human reviewers, you can implement a scalable solution that maximizes accuracy and minimizes operational overhead. You can also include a process to evaluate and fix incorrect translations.
Reference: [AutoML Translation documentation]
[Translation Hub documentation]
[Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate]
C
Explanation:
AutoML Translation is a service that allows you to create and train custom ML models for translating text between different languages. You can use AutoML Translation to train a model that can translate instruction manuals for scientific products to 15 different languages. You can also use Translation Hub to configure a project and use the trained model to translate the documents. Translation Hub is a service that allows you to manage and automate your translation workflows on Google Cloud. You can use Translation Hub to upload the documents to a Cloud Storage bucket, select the source and target languages, and apply the trained model to translate the documents. You can also use Translation Hub to download the translated documents or save them to another Cloud Storage bucket. You can also use human reviewers to evaluate the incorrect translations. Human reviewers are people who can review and correct the translations produced by the ML model. You can use human reviewers to improve the quality and accuracy of the translations, and provide feedback to the ML model. You can use Translation Hub to integrate with third-party human review services, such as Google Translate Community or Appen. By using AutoML Translation, Translation Hub, and human reviewers, you can implement a scalable solution that maximizes accuracy and minimizes operational overhead. You can also include a process to evaluate and fix incorrect translations.
Reference: [AutoML Translation documentation]
[Translation Hub documentation]
[Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate]
Question #90
You need to train a computer vision model that predicts the type of government ID present in a given image using a GPU-powered virtual machine on Compute Engine.
You use the following parameters:
• Optimizer: SGD
• Image shape 224×224
• Batch size 64
• Epochs 10
• Verbose 2
During training you encounter the following error: ResourceExhaustedError: out of Memory (oom) when allocating tensor.
What should you do?
- A . Change the optimizer
- B . Reduce the batch size
- C . Change the learning rate
- D . Reduce the image shape
Correct Answer: B
B
Explanation:
A ResourceExhaustedError: out of memory (OOM) when allocating tensor is an error that occurs when the GPU runs out of memory while trying to allocate memory for a tensor. A tensor is a multi-dimensional array of numbers that represents the data or the parameters of a machine learning model. The size and shape of a tensor depend on various factors, such as the input data, the model architecture, the batch size, and the optimization algorithm1.
For the use case of training a computer vision model that predicts the type of government ID present in a given image using a GPU-powered virtual machine on Compute Engine, the best option to resolve the error is to reduce the batch size. The batch size is a parameter that determines how many input examples are processed at a time by the model. A larger batch size can improve the model’s accuracy and stability, but it also requires more memory and computation. A smaller batch size can reduce the memory and computation requirements, but it may also affect the model’s performance and convergence2.
By reducing the batch size, the GPU can allocate less memory for each tensor, and avoid running out of memory. Reducing the batch size can also speed up the training process, as the GPU can process more batches in parallel. However, reducing the batch size too much may also have some drawbacks, such as increasing the noise and variance of the gradient updates, and slowing down the convergence of the model. Therefore, the optimal batch size should be chosen based on the trade-off between memory, computation, and performance3.
The other options are not as effective as option B, because they are not directly related to the memory allocation of the GPU.
Option A, changing the optimizer, may affect the speed and quality of the optimization process, but it may not reduce the memory usage of the model.
Option C, changing the learning rate, may affect the convergence and stability of the model, but it may not reduce the memory usage of the model.
Option D, reducing the image shape, may reduce the size of the input tensor, but it may also reduce the quality and resolution of the image, and affect the model’s accuracy. Therefore, option B, reducing the batch size, is the best answer for this question.
Reference: ResourceExhaustedError: OOM when allocating tensor with shape – Stack Overflow
How does batch size affect model performance and training time? – Stack Overflow
How to choose an optimal batch size for training a neural network? – Stack Overflow
B
Explanation:
A ResourceExhaustedError: out of memory (OOM) when allocating tensor is an error that occurs when the GPU runs out of memory while trying to allocate memory for a tensor. A tensor is a multi-dimensional array of numbers that represents the data or the parameters of a machine learning model. The size and shape of a tensor depend on various factors, such as the input data, the model architecture, the batch size, and the optimization algorithm1.
For the use case of training a computer vision model that predicts the type of government ID present in a given image using a GPU-powered virtual machine on Compute Engine, the best option to resolve the error is to reduce the batch size. The batch size is a parameter that determines how many input examples are processed at a time by the model. A larger batch size can improve the model’s accuracy and stability, but it also requires more memory and computation. A smaller batch size can reduce the memory and computation requirements, but it may also affect the model’s performance and convergence2.
By reducing the batch size, the GPU can allocate less memory for each tensor, and avoid running out of memory. Reducing the batch size can also speed up the training process, as the GPU can process more batches in parallel. However, reducing the batch size too much may also have some drawbacks, such as increasing the noise and variance of the gradient updates, and slowing down the convergence of the model. Therefore, the optimal batch size should be chosen based on the trade-off between memory, computation, and performance3.
The other options are not as effective as option B, because they are not directly related to the memory allocation of the GPU.
Option A, changing the optimizer, may affect the speed and quality of the optimization process, but it may not reduce the memory usage of the model.
Option C, changing the learning rate, may affect the convergence and stability of the model, but it may not reduce the memory usage of the model.
Option D, reducing the image shape, may reduce the size of the input tensor, but it may also reduce the quality and resolution of the image, and affect the model’s accuracy. Therefore, option B, reducing the batch size, is the best answer for this question.
Reference: ResourceExhaustedError: OOM when allocating tensor with shape – Stack Overflow
How does batch size affect model performance and training time? – Stack Overflow
How to choose an optimal batch size for training a neural network? – Stack Overflow