[PMLE CERTIFICATE - EXAMTOPIC] DUMPS Q9-Q12

EXAMTOPIC DUMPS Q9-Q12 ; CI/CD for Kubeflow pipelines, Proper metric for multi-class classification, Objective based on the Recommendation types, Architecture of Serverless ML model & Cloud ML API $vs.$ AutoML

Q 9.

You are developing ML models with AI Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?

CI/CD for Kubeflow pipelines

• ❌ A. Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job.
• ❌ B. Use the gcloud command-line tool to submit training jobs on AI Platform when you update your code.
• ⭕ C. Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository.
• ❌ D. Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.

Q 10.

Your team needs to build a model that predicts whether images contain a driver's license, passport, or credit card. The data engineering team already built the pipeline and generated a dataset composed of 10,000 images with driver's licenses, 1,000 images with passports, and 1,000 images with credit cards. You now have to train a model with the following label map: ['drivers_license', 'passport', 'credit_card']. Which loss function should you use?

Proper metric for multi-class (3) classification

• ❌ A. Categorical hinge
• ❌ B. Binary cross-entropy
• ⭕ C. Categorical cross-entropy
• ❌ D. Sparse categorical cross-entropy

Categorical Entropy & Sparse Categorical Entropy

• Categorical entropy is better to use when you want to prevent the model from giving more importance to a certain class. Or if the classes are very unbalanced you will get a better result by using Categorical entropy.

• Sparse Categorical Entropy is a more optimal coice if you have a huge amount of classes, enough to make a lot of memory usage, so since sparse categorical entropy uses less columns it uses less memory.

Q 11.

You are designing an ML recommendation model for shoppers on your company's ecommerce website. You will use Recommendations AI to build, test, and deploy your system. How should you develop recommendations that increase revenue while following best practices?

Objective based on the Recommendation types

• ❌ A. Use the Other Products You May Like recommendation type to increase the click-through rate.
• ⭕ B. Use the Frequently Bought Together recommendation type to increase the shopping cart size for each order.
• ❌ C. Import your user events and then your product catalog to make sure you have the highest quality event stream.
• ❌ D. Because it will take time to collect and record product data, use placeholder values for the product catalog to test the viability of the model.

• [EXAMTOPIC] Recommendation AI
• Google Cloud docs - Recommendation model types

Q 12.

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?

Architecture of Serverless ML model & Cloud ML API $vs.$ AutoML

• ❌ A. 1 = AI Platform, 2 = AI Platform, 3 = AutoML Vision
• ❌ B. 1 = AI Platform, 2 = AI Platform, 3 = AutoML Natural Language
  → Tickets are not expected to have any domain-specific terms or jargon : NOT INCLUDE SPECIFIC TERMS MEANING THAT YOU DONT NEED CUSTOM ML MODEL.
• ⭕ C. 1 = AI Platform, 2 = AI Platform, 3 = Cloud Natural Language API
  → SENTIMENT ANALYSIS PROVIDED BY Cloud Natural Language API
• ❌ D. 1 = Cloud Natural Language API, 2 = AI Platform, 3 = Cloud Vision API

Firebase, Cloud Functions, AI Platform, Cloud ML API for real time & serverless ML model system

No	Architecture of a Serverless Machine Learning Model
workflow	Architecture of a Serverless Machine Learning Model to enrich support tickets with metadata before they reach a support agent
1	A user writes a ticket to Firebase, which triggers a Cloud Function.
2	The Cloud Function calls 3 different endpoints to enrich the ticket
2-1	→ (1) An AI Platform endpoint, where the function can predict the priority.
2-2	→ (2) An AI Platform endpoint, where the function can predict the resolution time.
2-3	→ (3) The Natural Language API to do sentiment analysis and word salience.
3	For each reply, the Cloud Function updates the Firebase real-time database.
4	The Cloud Function then creates a ticket into the helpdesk platform using the RESTful API.

• Firebase

  • Real-time database that a client can update, and it displays real-time updates to other subscribed clients
  • Firebase can use Cloud Functions to call an external API, such as one that your helpdesk platform makes available. (Workflow에 e 표시)
  • Firebase works on desktop and mobile platforms and can be developed in various languages. When Firebase experiences unreliable internet connections, it can cache data locally.

• Sentiment analysis and autotagging : Cloud Natural Language API for Sentiment analysis, Entity analysis with salience calculation, Syntax analysis easily accessible from Cloud Functions as a RESTful API.

  • Sentiment analysis on the ticket description helps agents know how the customer feels.
  • Wild autotagging : By using a tool that identifies the most important words in the description, the agent can narrow down the subject matter. (⇒ Entity Analysis)