완벽한Professional-Machine-Learning-Engineer최고품질인증시험자료시험패스의강력한무기

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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:

Section	Objectives
Designing ML solutions	- ML architecture design 1. Design scalable ML systems on GCP 2. Select appropriate ML models and approaches - Framing ML problems 1. Translate business problems into ML tasks 2. Define success metrics and evaluation criteria
ML model development	- Model training and tuning 1. Train models using TensorFlow / Vertex AI 2. Hyperparameter tuning and optimization - Evaluation 1. Model validation strategies 2. Evaluate model performance metrics
Deployment and operations	- Monitoring and maintenance 1. Retraining and lifecycle management 2. Monitor model drift and performance - Model deployment 1. Deploy models using Vertex AI endpoints 2. Batch and online prediction systems
Data preparation and processing	- Data ingestion and pipelines 1. Build data pipelines for training and serving 2. Use BigQuery and data processing services - Feature engineering 1. Feature selection and representation techniques 2. Transform and preprocess datasets
ML pipeline automation and orchestration	- Pipeline design 1. Build end-to-end ML pipelines 2. Use Vertex AI Pipelines

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Professional-Machine-Learning-Engineer시험대비 덤프 최신버전 - Professional-Machine-Learning-Engineer자격증덤프

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최신 Google Cloud Certified Professional-Machine-Learning-Engineer 무료샘플문제 (Q23-Q28):

질문 # 23
You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 30 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables. This data has a time signal that is spread across multiple columns. How should you ensure that AutoML fits the best model to your data?

A. Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column Allow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets
B. Submit the data for training without performing any manual transformations Use the columns that have a time signal to manually split your data Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing set is from 30 days after your validation set
C. Manually combine all columns that contain a time signal into an array Allow AutoML to interpret this array appropriately Choose an automatic data split across the training, validation, and testing sets
D. Submit the data for training without performing any manual transformations Allow AutoML to handle the appropriate transformations Choose an automatic data split across the training, validation, and testing sets

정답：B

질문 # 24
You work for a social media company. You want to create a no-code image classification model for an iOS mobile application to identify fashion accessories You have a labeled dataset in Cloud Storage You need to configure a training workflow that minimizes cost and serves predictions with the lowest possible latency What should you do?

A. Train the model by using AutoML, and register the model in Vertex Al Model Registry Configure your mobile application to send batch requests during prediction.
B. Train the model by using AutoML Edge and export the model as a TFLite model Configure your mobile application to use the tflite file directly
C. Train the model by using AutoML, and expose the model as a Vertex Al endpoint Configure your mobile application to invoke the endpoint during prediction.
D. Train the model by using AutoML Edge and export it as a Core ML model Configure your mobile application to use the mlmodel file directly.

정답：D

질문 # 25
You are developing ML models with Al 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?

A. Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code
B. Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job
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.

정답：A

질문 # 26
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?

A. 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
B. Before training, use BigQuery to select only the columns that do not contain sensitive data Create an authorized view of the data so that sensitive values cannot be accessed by unauthorized individuals.
C. Use the Cloud Data Loss Prevention (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.
D. Using Dataflow, ingest the columns with sensitive data from BigQuery, and then randomize the values in each sensitive column.

정답：A

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 tothe 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.
References:
* 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 # 27
You built a deep learning-based image classification model by using on-premises dat a. You want to use Vertex Al to deploy the model to production Due to security concerns you cannot move your data to the cloud. You are aware that the input data distribution might change over time You need to detect model performance changes in production. What should you do?

A. Create a Vertex Al Model Monitoring job. Enable training-serving skew detection for your model.
B. Use Vertex Explainable Al for model explainability Configure feature-based explanations.
C. Use Vertex Explainable Al for model explainability Configure example-based explanations.
D. Create a Vertex Al Model Monitoring job. Enable feature attribution skew and dnft detection for your model.

정답：D

질문 # 28
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