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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Automate and orchestrate ML pipelines | 18% | - Design end-to-end ML workflows - Implement CI/CD for ML systems - Automate retraining and model updates - Use Vertex AI Pipelines, TFX, and other orchestration tools |
| Topic 2: Scale prototypes into AI models | 18% | - Optimize model performance and generalization - Design and run experiments - Select appropriate model architectures and frameworks - Work with foundation models and generative AI techniques |
| Topic 3: Monitor and optimize AI solutions | 16% | - Monitor data quality and pipeline health - Troubleshoot and maintain production systems - Optimize cost, latency, and resource usage - Monitor model performance, fairness, and drift |
| Topic 4: Collaborate to manage data and models | 16% | - Organize and prepare enterprise data
- Manage datasets and features in Vertex AI |
| Topic 5: Architect low-code AI solutions | 12% | - Identify use cases for low-code/no-code AI tools - Apply responsible AI principles to low-code designs - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder |
| Topic 6: Train and deploy models | 20% | - Configure training jobs and environments - Use Vertex AI deployment features and infrastructure - Implement generative AI deployment patterns - Deploy models for online, batch, and streaming prediction |
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NEW QUESTION # 80
You are building a real-time prediction engine that streams files which may contain Personally Identifiable Information (Pll) to Google Cloud. You want to use the Cloud Data Loss Prevention (DLP) API to scan the files. How should you ensure that the Pll is not accessible by unauthorized individuals?
Answer: C
Explanation:
The Cloud DLP API is a service that allows users to inspect, classify, and de-identify sensitive data. It can be used to scan data in Cloud Storage, BigQuery, Cloud Datastore, and Cloud Pub/Sub. The best way to ensure that the PII is not accessible by unauthorized individuals is to use a quarantine bucket to store the data before scanning it with the DLP API. This way, the data is isolated from other applications and users until it is classified and moved to the appropriate bucket. The other options are not as secure or efficient, as they either expose the data to BigQuery before scanning, or scan the data after writing it to a non-sensitive bucket.
References:
* Cloud DLP documentation
* Scanning and classifying Cloud Storage files
NEW QUESTION # 81
You are building a linear regression model on BigQuery ML to predict a customer ' s likelihood of purchasing your company ' s products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables. What should you do?
Answer: A
Explanation:
One-hot encoding is a technique that converts categorical variables into numerical variables by creating dummy variables for each possible category. Each dummy variable has a value of 1 if the original variable belongs to that category, and 0 otherwise 1 . One-hot encoding can help linear regression models to capture the effect of different categories on the targe t variable without imposing any ordinal relationship among them
2 . Dataprep is a service that allows you to explore, clean, and transform your data for analysis and machine learning. You can use Dataprep to apply one-hot encoding to your city name variable and make each city a column with binary values 3 . This way, you can prepare your data using the least amount of coding while maintaining the predictive variables. Therefore, using Dataprep to transform the state column using a one-hot encoding method is the best option for this use case.
References:
One Hot Encoding: A Beginner's Guide
One-Hot Encoding in Linear Regression Models
Dataprep documentation
NEW QUESTION # 82
Your company manages an application that aggregates news articles from many different online sources and sends them to users. You need to build a recommendation model that will suggest articles to readers that are similar to the articles they are currently reading. Which approach should you use?
Answer: A
Explanation:
Option A is incorrect because creating a collaborative filtering system that recommends articles to a user based on the user's past behavior is not the best approach to suggest articles that are similar to the articles they are currently reading. Collaborative filtering is a method of recommendation that uses the ratings or preferences of other users to predict the preferences of a target user1. However, this method does not consider the content or features of the articles, and may not be able to find articles that are similar in terms of topic, style, or sentiment.
Option B is correct because encoding all articles into vectors using word2vec, and building a model that returns articles based on vector similarity is a suitable approach to suggest articles that are similar to the articles they are currently reading. Word2vec is a technique that learns low-dimensional and dense representations of words from a large corpus of text, such that words that are semantically similar have similar vectors2. By applying word2vec to the articles, we can obtain vector representations of the articles that capture their meaning and usage. Then, we can use a similarity measure, such as cosine similarity, to find articles that have similar vectors to the current article3.
Option C is incorrect because building a logistic regression model for each user that predicts whether an article should be recommended to a user is not a feasible approach to suggest articles that are similar to the articles they are currently reading. Logistic regression is a supervised learning method that models the probability of a binary outcome (such as recommend or not) based on some input features (such as user profile or article content)4. However, this method requires a large amount of labeled data for each user, which may not be available or scalable. Moreover, this method does not directly measure the similarity between articles, but rather the likelihood of a user's preference.
Option D is incorrect because manually labeling a few hundred articles, and then training an SVM classifier based on the manually classified articles that categorizes additional articles into their respective categories is not an effective approach to suggest articles that are similar to the articles they are currently reading. SVM (support vector machine) is a supervised learning method that finds a hyperplane that separates the data into different classes (such as news categories) with the maximum margin5. However, this method also requires a large amount of labeled data, which may be costly and time-consuming to obtain. Moreover, this method does not account for the fine-grained similarity between articles within the same category, or the cross-category similarity between articles from different categories.
Reference:
Collaborative filtering
Word2vec
Cosine similarity
Logistic regression
SVM
NEW QUESTION # 83
You deployed an ML model into production a year ago. Every month, you collect all raw requests that were sent to your model prediction service during the previous month. You send a subset of these requests to a human labeling service to evaluate your model's performance. After a year, you notice that your model's performance sometimes degrades significantly after a month, while other times it takes several months to notice any decrease in performance. The labeling service is costly, but you also need to avoid large performance degradations. You want to determine how often you should retrain your model to maintain a high level of performance while minimizing cost.
What should you do?
Answer: C
Explanation:
https://cloud.google.com/blog/topics/developers-practitioners/monitor-models-training-serving-skew-vertex-ai
https://developers.google.com/machine-learning/guides/rules-of-ml
NEW QUESTION # 84
You work with a data engineering team that has developed a pipeline to clean your dataset and save it in a Cloud Storage bucket. You have created an ML model and want to use the data to refresh your model as soon as new data is available. As part of your CI/CD workflow, you want to automatically run a Kubeflow Pipelines training job on Google Kubernetes Engine (GKE). How should you architect this workflow?
Answer: B
Explanation:
https://cloud.google.com/architecture/architecture-for-mlops-using-tfx-kubeflow-pipelines-and-cloud-build#triggering-and-scheduling-kubeflow-pipelines
NEW QUESTION # 85
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