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| Section | Weight | Objectives |
|---|---|---|
| Collaborate to manage data and models | 16% | - Address data privacy, compliance, and governance - Manage datasets and features in Vertex AI - Organize and prepare enterprise data
|
| Automate and orchestrate ML pipelines | 18% | - Use Vertex AI Pipelines, TFX, and other orchestration tools - Design end-to-end ML workflows - Automate retraining and model updates - Implement CI/CD for ML systems |
| Monitor and optimize AI solutions | 16% | - Monitor data quality and pipeline health - Monitor model performance, fairness, and drift - Troubleshoot and maintain production systems - Optimize cost, latency, and resource usage |
| Architect low-code AI solutions | 12% | - Apply responsible AI principles to low-code designs - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Identify use cases for low-code/no-code AI tools |
| Scale prototypes into AI models | 18% | - Work with foundation models and generative AI techniques - Optimize model performance and generalization - Design and run experiments - Select appropriate model architectures and frameworks |
| Train and deploy models | 20% | - Implement generative AI deployment patterns - Deploy models for online, batch, and streaming prediction - Use Vertex AI deployment features and infrastructure - Configure training jobs and environments |
>> Professional-Machine-Learning-Engineer Reliable Exam Questions <<
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NEW QUESTION # 32
You are analyzing customer data for a healthcare organization that is stored in Cloud Storage. The data contains personally identifiable information (PII) You need to perform data exploration and preprocessing while ensuring the security and privacy of sensitive fields What should you do?
Answer: A
Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to "design, build, and productionalize ML models to solve business challenges using Google Cloud technologies". Cloud Data Loss Prevention (DLP) API2 is a service that provides programmatic access to a powerful detection engine for personally identifiable information and other privacy-sensitive data in unstructured data streams, such as text blocks and images. Cloud DLP API helps you discover, classify, and protect your sensitive data by using techniques such as de-identification, masking, tokenization, and bucketing. You can use Cloud DLP API to de-identify the PII data before performing data exploration and preprocessing, and retain the data utility for ML purposes. Therefore, option A is the best way to perform data exploration and preprocessing while ensuring the security and privacy of sensitive fields. The other options are not relevant or optimal for this scenario. Reference:
Professional ML Engineer Exam Guide
Cloud Data Loss Prevention (DLP) API
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
NEW QUESTION # 33
You work at a bank You have a custom tabular ML model that was provided by the bank's vendor. The training data is not available due to its sensitivity. The model is packaged as a Vertex Al Model serving container which accepts a string as input for each prediction instance. In each string the feature values are separated by commas. You want to deploy this model to production for online predictions, and monitor the feature distribution over time with minimal effort What should you do?
Answer: A
NEW QUESTION # 34
You have deployed a model on Vertex AI for real-time inference. During an online prediction request, you get an "Out of Memory" error. What should you do?
Answer: D
Explanation:
* Option A is incorrect because using batch prediction mode instead of online mode does not solve the
"Out of Memory" error, but rather changes the latency and throughput of the prediction service. Batch prediction mode is suitable for large-scale, asynchronous, and non-urgent predictions, while online prediction mode is suitable for low-latency, synchronous, and real-time predictions1.
* Option B is correct because sending the request again with a smaller batch of instances can reduce the memory consumption of the prediction service and avoid the "Out of Memory" error. The batch size is the number of instances that are processed together in one request. A smaller batch size means less data to load into memory at once2.
* Option C is incorrect because using base64 to encode your data before using it for prediction does not reduce the memory consumption of the prediction service, but rather increases it. Base64 encoding is a way of representing binary data as ASCII characters, which increases the size of the data by about 33%
3. Base64 encoding is only required for certain data types, such as images and audio, that cannot be represented as JSON or CSV4.
* Option D is incorrect because applying for a quota increase for the number of prediction requests does not solve the "Out of Memory" error, but rather increases the number of requests that can be sent to the prediction service per day. Quotas are limits on the usage of Google Cloud resources, such as CPU, memory, disk, and network5. Quotas do not affect the performance of the prediction service, but rather the availability and cost of the service.
References:
* Choosing between online and batch prediction
* Online prediction input data
* Base64 encoding
* Preparing data for prediction
* Quotas and limits
NEW QUESTION # 35
You work for a food product company. Your company's historical sales data is stored in BigQuery You need to use Vertex Al's custom training service to train multiple TensorFlow models that read the data from BigQuery and predict future sales You plan to implement a data preprocessing algorithm that performs min-max scaling and bucketing on a large number of features before you start experimenting with the models. You want to minimize preprocessing time, cost and development effort How should you configure this workflow?
Answer: C
NEW QUESTION # 36
You have created multiple versions of an ML model and have imported them to Vertex AI Model Registry.
You want to perform A/B testing to identify the best-performing model using the simplest approach. What should you do?
Answer: A
Explanation:
Vertex AI Model Registry supports traffic splitting and built-in monitoring, making A/B testing seamless.
This approach eliminates the need for additional monitoring tools and infrastructure overhead. Cloud Run and GKE solutions (Options A and C) add unnecessary complexity, while Looker Studio (Option B) requires additional configuration for monitoring.
NEW QUESTION # 37
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