2026 DumpTOP 최신 Professional-Machine-Learning-Engineer PDF 버전 시험 문제집과 Professional-Machine-Learning-Engineer 시험 문제 및 답변 무료 공유: https://drive.google.com/open?id=1ku8rHIaQNwvQgmdcyzbHpnUnEyG55t24
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Monitor and optimize AI solutions | 16% | - Monitor data quality and pipeline health - Monitor model performance, fairness, and drift - Optimize cost, latency, and resource usage - Troubleshoot and maintain production systems |
| Topic 2: Collaborate to manage data and models | 16% | - Manage datasets and features in Vertex AI - Address data privacy, compliance, and governance - Organize and prepare enterprise data
|
| Topic 3: 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 |
| Topic 4: Train and deploy models | 20% | - Deploy models for online, batch, and streaming prediction - Configure training jobs and environments - Implement generative AI deployment patterns - Use Vertex AI deployment features and infrastructure |
| Topic 5: Architect low-code AI solutions | 12% | - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Identify use cases for low-code/no-code AI tools - Apply responsible AI principles to low-code designs |
| Topic 6: Automate and orchestrate ML pipelines | 18% | - Use Vertex AI Pipelines, TFX, and other orchestration tools - Implement CI/CD for ML systems - Automate retraining and model updates - Design end-to-end ML workflows |
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질문 # 194
You are responsible for building a unified analytics environment across a variety of on-premises data marts. Your company is experiencing data quality and security challenges when integrating data across the servers, caused by the use of a wide range of disconnected tools and temporary solutions. You need a fully managed, cloud-native data integration service that will lower the total cost of work and reduce repetitive work. Some members on your team prefer a codeless interface for building Extract, Transform, Load (ETL) process. Which service should you use?
정답：A
설명：
https://cloud.google.com/data-fusion/docs/concepts/overview#using_the_code-free_web_ui
질문 # 195
You are training an ML model using data stored in BigQuery that contains several values that are considered Personally Identifiable Information (Pll). You need to reduce the sensitivity of the dataset before training your model. Every column is critical to your model. How should you proceed?
정답：B
설명：
The best option for reducing the sensitivity of the dataset before training the model is to use the Cloud Data Loss Prevention (DLP) API to scan for sensitive data, and use Dataflow with the DLP API to encrypt sensitive values with Format Preserving Encryption. This option allows you to keep every column in the dataset, while protecting the sensitive data from unauthorized access or exposure. The Cloud DLP API can detect and classify various types of sensitive data, such as names, email addresses, phone numbers, credit card numbers, and more1. Dataflow can create scalable and reliable pipelines to process large volumes of data from BigQuery and other sources2. Format Preserving Encryption (FPE) is a technique that encrypts sensitive data while preserving its original format and length, which can help maintain the utility and validity of the data3. By using Dataflow with the DLP API, you can apply FPE to the sensitive values in the dataset, and store the encrypted data in BigQuery or another destination. You can also use the same pipeline to decrypt the data when needed, by using the same encryption key and method4.
The other options are not as suitable as option B, for the following reasons:
Option A: Using Dataflow to ingest the columns with sensitive data from BigQuery, and then randomize the values in each sensitive column, would reduce the sensitivity of the data, but also the utility and accuracy of the data. Randomization is a technique that replaces sensitive data with random values, which can prevent re-identification of the data, but also distort the distribution and relationships of the data3. This can affect the performance and quality of the ML model, especially if every column is critical to the model.
Option C: Using the Cloud DLP API to scan for sensitive data, and use Dataflow to replace all sensitive data by using the encryption algorithm AES-256 with a salt, would reduce the sensitivity of the data, but also the utility and validity of the data. AES-256 is a symmetric encryption algorithm that uses a 256-bit key to encrypt and decrypt data. A salt is a random value that is added to the data before encryption, to increase the randomness and security of the encrypted data. However, AES-256 does not preserve the format or length of the original data, which can cause problems when storing or processing the data. For example, if the original data is a 10-digit phone number, AES-256 would produce a much longer and different string, which can break the schema or logic of the dataset3.
Option D: Before training, using BigQuery to select only the columns that do not contain sensitive data, and creating an authorized view of the data so that sensitive values cannot be accessed by unauthorized individuals, would reduce the exposure of the sensitive data, but also the completeness and relevance of the data. An authorized view is a BigQuery view that allows you to share query results with particular users or groups, without giving them access to the underlying tables. However, this option assumes that you can identify the columns that do not contain sensitive data, which may not be easy or accurate. Moreover, this option would remove some columns from the dataset, which can affect the performance and quality of the ML model, especially if every column is critical to the model.
Reference:
Preparing for Google Cloud Certification: Machine Learning Engineer, Course 5: Responsible AI, Week 2: Privacy Google Cloud Professional Machine Learning Engineer Exam Guide, Section 5: Developing responsible AI solutions, 5.2 Implementing privacy techniques Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 9: Responsible AI, Section 9.4: Privacy De-identification techniques Cloud Data Loss Prevention (DLP) API Dataflow Using Dataflow and Sensitive Data Protection to securely tokenize and import data from a relational database to BigQuery
[AES encryption]
[Salt (cryptography)]
[Authorized views]
질문 # 196
You have trained a text classification model in TensorFlow using Al Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead. What should you do?
정답：D
설명：
This answer is correct because it allows you to use the trained TensorFlow model for batch predictions on text data stored in BigQuery without any additional processing or overhead. Al Platform provides a service for running batch prediction jobs that can take input data from BigQuery or Cloud Storage and write the output to BigQuery or Cloud Storage. You can use the SavedModel format to export your TensorFlow model to Cloud Storage and then submit a batch prediction job that points to the model location and the input data location. Al Platform will handle the scaling and distribution of the prediction requests and return the results in the specified output location. Reference:
[Al Platform: Batch prediction overview]
[Al Platform: Exporting a SavedModel for prediction]
질문 # 197
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?
정답：A
질문 # 198
You work at a large organization that recently decided to move their ML and data workloads to Google Cloud. The data engineering team has exported the structured data to a Cloud Storage bucket in Avro format. You need to propose a workflow that performs analytics, creates features, and hosts the features that your ML models use for online prediction. How should you configure the pipeline?
정답：C
질문 # 199
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