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| Section | Weight | Objectives |
|---|---|---|
| Collaborate to manage data and models | 16% | - Organize and prepare enterprise data
- Manage datasets and features in Vertex AI |
| Train and deploy models | 20% | - Implement generative AI deployment patterns - Configure training jobs and environments - Deploy models for online, batch, and streaming prediction - Use Vertex AI deployment features and infrastructure |
| 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 |
| Automate and orchestrate ML pipelines | 18% | - Design end-to-end ML workflows - Automate retraining and model updates - Use Vertex AI Pipelines, TFX, and other orchestration tools - Implement CI/CD for ML systems |
| Monitor and optimize AI solutions | 16% | - Troubleshoot and maintain production systems - Optimize cost, latency, and resource usage - Monitor data quality and pipeline health - Monitor model performance, fairness, and drift |
| Scale prototypes into AI models | 18% | - Optimize model performance and generalization - Select appropriate model architectures and frameworks - Work with foundation models and generative AI techniques - Design and run experiments |
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NEW QUESTION # 64
You have developed a fraud detection model for a large financial institution using Vertex AI. The model achieves high accuracy, but stakeholders are concerned about potential bias based on customer demographics.
You have been asked to provide insights into the model's decision-making process and identify any fairness issues. What should you do?
Answer: A
Explanation:
Feature attribution helps to determine how each feature influences predictions, essential for identifying bias.
Vertex AI's built-in explainability tools provide insights without altering the model's feature space. Model monitoring (Option A) detects distributional drift rather than feature influence. Options B and D do not directly address the request to explain model decisions or provide fairness insights.
NEW QUESTION # 65
You are developing a recommendation engine for an online clothing store. The historical customer transaction data is stored in BigQuery and Cloud Storage. You need to perform exploratory data analysis (EDA), preprocessing and model training. You plan to rerun these EDA, preprocessing, and training steps as you experiment with different types of algorithms. You want to minimize the cost and development effort of running these steps as you experiment. How should you configure the environment?
Answer: C
Explanation:
* Cost-effectiveness: User-managed notebooks in Vertex AI Workbench allow you to leverage pre-configured virtual machines with reasonable resource allocation, keeping costs lower compared to options involving managed notebooks or Dataproc clusters.
* Development flexibility: User-managed notebooks offer full control over the environment, allowing you to install additional libraries or dependencies needed for your specific EDA, preprocessing, and model training tasks. This flexibility is crucial while experimenting with different algorithms.
* BigQuery integration: The %%bigquery magic commands provide seamless integration with BigQuery
* within the Jupyter Notebook environment. This enables efficient querying and exploration of customer transaction data stored in BigQuery directly from the notebook, streamlining the workflow.
Other options and why they are not the best fit:
* B. Managed notebook: While managed notebooks offer an easier setup, they might have limited customization options, potentially hindering your ability to install specific libraries or tools.
* C. Dataproc Hub: Dataproc Hub focuses on running large-scale distributed workloads, and it might be overkill for your scenario involving exploratory analysis and experimentation with different algorithms.
Additionally, it could incur higher costs compared to a user-managed notebook.
* D. Dataproc cluster with spark-bigquery-connector: Similar to option C, using a Dataproc cluster with the spark-bigquery-connector would be more complex and potentially more expensive than using
%%bigquery magic commands within a user-managed notebook for accessing BigQuery data.
References:
* https://cloud.google.com/vertex-ai/docs/workbench/instances/bigquery
* https://cloud.google.com/vertex-ai-notebooks
NEW QUESTION # 66
You are a data scientist at an industrial equipment manufacturing company. You are developing a regression model to estimate the power consumption in the company's manufacturing plants based on sensor data collected from all of the plants. The sensors collect tens of millions of records every day. You need to schedule daily training runs for your model that use all the data collected up to the current date. You want your model to scale smoothly and require minimal development work. What should you do?
Answer: D
Explanation:
The key is to understand the amount of data that needs to be used for training - the sensor collects tens of millions of records every day and the model needs to use all the data up to the current date.
There is a limitation for AutoML is 100M rows -> https://cloud.google.com/vertex-ai/docs/tabular- data/classification-regression/prepare-data
NEW QUESTION # 67
You are developing a Kubeflow pipeline on Google Kubernetes Engine. The first step in the pipeline is to issue a query against BigQuery. You plan to use the results of that query as the input to the next step in your pipeline. You want to achieve this in the easiest way possible. What should you do?
Answer: D
Explanation:
Kubeflow is an open source platform for developing, orchestrating, deploying, and running scalable and portable machine learning workflows on Kubernetes. Kubeflow Pipelines is a component of Kubeflow that allows you to build and manage end-to-end machine learning pipelines using a graphical user interface or a Python-based domain-specific language (DSL). Kubeflow Pipelines can help you automate and orchestrate your machine learning workflows, and integrate with various Google Cloud services and tools1 One of the Google Cloud services that you can use with Kubeflow Pipelines is BigQuery, which is a serverless, scalable, and cost-effective data warehouse that allows you to run fast and complex queries on large-scale data. BigQuery can help you analyze and prepare your data for machine learning, and store and manage your machine learning models2 To execute a query against BigQuery as the first step in your Kubeflow pipeline, and use the results of that query as the input to the next step in your pipeline, the easiest way to do that is to use the BigQuery Query Component, which is a pre-built component that you can find in the Kubeflow Pipelines repository on GitHub. The BigQuery Query Component allows you to run a SQL query on BigQuery, and output the results as a table or a file. You can use the component's URL to load the component into your pipeline, and specify the query and the output parameters. You can then use the output of the component as the input to the next step in your pipeline, such as a data processing or a model training step3 The other options are not as easy or feasible. Using the BigQuery console to execute your query and then save the query results into a new BigQuery table is not a good idea, as it does not integrate with your Kubeflow pipeline, and requires manual intervention and duplication of data. Writing a Python script that uses the BigQuery API to execute queries against BigQuery is not ideal, as it requires writing custom code and handling authentication and error handling. Using the Kubeflow Pipelines DSL to create a custom component that uses the Python BigQuery client library to execute queries is not optimal, as it requires creating and packaging a Docker container image for the component, and testing and debugging the component.
References: 1: Kubeflow Pipelines overview 2: BigQuery overview 3: BigQuery Query Component
NEW QUESTION # 68
You work for a retailer that sells clothes to customers around the world. You have been tasked with ensuring that ML models are built in a secure manner. Specifically, you need to protect sensitive customer data that might be used in the models. You have identified four fields containing sensitive data that are being used by your data science team: AGE, IS_EXISTING_CUSTOMER, LATITUDE_LONGITUDE, and SHIRT_SIZE.
What should you do with the data before it is made available to the data science team for training purposes?
Answer: D
Explanation:
The best option for protecting sensitive customer data that might be used in the ML models is to coarsen the data by putting AGE into quantiles and rounding LATITUDE_LONGITUDE into single precision. This option has the following advantages:
* It preserves the utility and relevance of the data for the ML models, as the coarsened data still captures the essential information and patterns that the models need to learn. For example, putting AGE into quantiles can group the customers into different age ranges, which can be useful for predicting their preferences or behavior. Rounding LATITUDE_LONGITUDE into single precision can reduce the precision of the location data, but still retain the general geographic region of the customers, which can be useful for personalizing the recommendations or offers.
* It reduces the risk of exposing the personal or private information of the customers, as the coarsened data makes it harder to identify or re-identify the individual customers from the data. For example, putting AGE into quantiles can hide the exact age of the customers, which can be considered sensitive or confidential. Rounding LATITUDE_LONGITUDE into single precision can obscure the exact location of the customers, which can be considered sensitive or confidential.
The other options are less optimal for the following reasons:
* Option A: Tokenizing all of the fields using hashed dummy values to replace the real values eliminates the utility and relevance of the data for the ML models, as the tokenized data loses all the information and patterns that the models need to learn. For example, tokenizing AGE using hashed dummy values can make the data meaningless and irrelevant, as the models cannot learn anything from the random tokens. Tokenizing LATITUDE_LONGITUDE using hashed dummy values can make the data meaningless and irrelevant, as the models cannot learn anything from the random tokens.
* Option B: Using principal component analysis (PCA) to reduce the four sensitive fields to one PCA vector reduces the utility and relevance of the data for the ML models, as the PCA vector may not capture all the information and patterns that the models need to learn. For example, using PCA to reduce AGE, IS_EXISTING_CUSTOMER, LATITUDE_LONGITUDE, and SHIRT_SIZE to one PCA vector can lose some information or introduce noise in the data, as the PCA vector is a linear combination of the original features, which may not reflect their true relationship or importance.
Moreover, using PCA to reduce the four sensitive fields to one PCA vector may not reduce the risk of exposing the personal or private information of the customers, as the PCA vector may still be reversible or linkable to the original data, depending on the amount of variance explained by the PCA vector and the availability of the PCA transformation matrix.
* Option D: Removing all sensitive data fields, and asking the data science team to build their models using non-sensitive data reduces the utility and relevance of the data for the ML models, as the non- sensitive data may not contain enough information and patterns that the models need to learn. For example, removing AGE, IS_EXISTING_CUSTOMER, LATITUDE_LONGITUDE, and SHIRT_SIZE from the data can make the data insufficient and unrepresentative, as the models may not be able to learn the factors that influence the customers' preferences or behavior. Moreover, removing all sensitive data fields from the data may not be necessary or feasible, as the data protection legislation may allow the use of sensitive data for the ML models, as long as the data is processed in a secure and ethical manner, and the customers' consent and rights are respected.
References:
* Protecting Sensitive Data and AI Models with Confidential Computing | NVIDIA Technical Blog
* Training machine learning models from sensitive data | Fast Data Science
* Securing ML applications. Model security and protection - Medium
* Security of AI/ML systems, ML model security | Cossack Labs
* Vulnerabilities, security and privacy for machine learning models
NEW QUESTION # 69
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