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Question 191

You are working on a binary classification ML algorithm that detects whether an image of a classified scanned document contains a company's logo. In the dataset, 96% of examples don't have the logo, so the dataset is very skewed. Which metrics would give you the most confidence in your model?

A.F-score where recall is weighed more than precision
B.RMSE
C.F1 score
D.F-score where precision is weighed more than recall

Correct Answer: A

* Option A is correct because using F-score where recall is weighed more than precision is a suitable metric for binary classification with imbalanced data. F-score is a harmonic mean of precision and recall, which are two metrics that measure the accuracy and completeness of the positive class 1
. Precision is the fraction of true p ositives among all predicted positives, while recall is the fraction of true positives among all actual positives 1 . When the data is imbalanced, the positive class is the minority class, which is usually the class of interest. For example, in this case, the positive class is the images that contain the company's logo, which are rare but important to detect. By weighing recall more than precision, we can emphasize the importance of finding all the positive examples, even if some false positives are included 2 .
* Option B is incorrect because using RMSE (root mean squared error) is not a valid metric for binary classification with imbalanced data. RMSE is a metric that measures the average magnitude of the errors between the predicted and actual values 3 . RMSE is suitable for regression problems, where the target variable is continuous, not for classification problems, where the target variable is discrete 4 .
* Option C is incorrect because using F1 score is not the best metric for binary classification with imbalanced data. F1 score is a special case of F-score where precision and recall are equally weighted
1 . F1 score is suitable for balanced data, where the positive and negative classes are equally important and frequent 5 . However, for imbalanced data, the positive class is more important and less frequent than the negative class, so F1 sc ore may not reflect the performance of the model well 2 .
* Option D is incorrect because using F-score where precision is weighed more than recall is not a good metric for binary classification with imbalanced data. By weighing precision more than recall, we can emphasize the impo rtance of minimizing the false positives, even if some true positives are missed 2
. However, for imbalanced data, the true positives are more important and less frequent than the false positives, so this metric may not reflect the performance of the model well 2 .
References:
Precision, recall, an d F-measure
F-score for imbalanced data
RMSE
Regression vs classification
F1 score
[Imbalanced classification]
[Binary classification]

Question 192

Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this:

You followed the standard 80%-10%-10% data distribution across the training, testing, and evaluation subsets.
How should you distribute the training examples across the train-test-eval subsets while maintaining the
80-10-10 proportion?

Question 193

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

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. References:
* 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 194

Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this:

You followed the standard 80%-10%-10% data distribution across the training, testing, and evaluation subsets. How should you distribute the training examples across the train-test-eval subsets while maintaining the 80-10-10 proportion?

Question 195

You are designing an architecture with a serverless ML system to enrich customer support tickets with informative metadata before they are routed to a support agent. You need a set of models to predict ticket priority, predict ticket resolution time, and perform sentiment analysis to help agents make strategic decisions when they process support requests. Tickets are not expected to have any domain-specific terms or jargon.
The proposed architecture has the following flow:

Which endpoints should the Enrichment Cloud Functions call?

A.1 = Vertex Al. 2 = Vertex Al. 3 = AutoML Natural Language
B.1 = Vertex Al. 2 = Vertex Al. 3 = Cloud Natural Language API
C.1 = Vertex Al. 2 = Vertex Al. 3 = AutoML Vision
D.1 = Cloud Natural Language API. 2 = Vertex Al, 3 = Cloud Vision API