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Google Professional Machine Learning Engineer Sample Questions (Q165-Q170):

NEW QUESTION # 165
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?

A. Create two buckets of data Sensitive and Non-sensitive Write all data to the Non-sensitive bucket Periodically conduct a bulk scan of that bucket using the DLP API, and move the sensitive data to the Sensitive bucket
B. Stream all files to Google CloudT and then write the data to BigQuery Periodically conduct a bulk scan of the table using the DLP API.
C. Periodically conduct a bulk scan of that bucket using the DLP API, and move the data to either the Sensitive or Non-Sensitive bucket
D. Create three buckets of data: Quarantine, Sensitive, and Non-sensitive Write all data to the Quarantine bucket.
E. Stream all files to Google Cloud, and write batches of the data to BigQuery While the data is being written to BigQuery conduct a bulk scan of the data using the DLP API.

Answer: D

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. Reference:
Cloud DLP documentation
Scanning and classifying Cloud Storage files

NEW QUESTION # 166
Your team has a model deployed to a Vertex Al endpoint You have created a Vertex Al pipeline that automates the model training process and is triggered by a Cloud Function. You need to prioritize keeping the model up-to-date, but also minimize retraining costs. How should you configure retraining'?

A. Enable model monitoring on the Vertex Al endpoint Configure Pub/Sub to call the Cloud Function when anomalies are detected.
B. Configure a Cloud Scheduler job that calls the Cloud Function at a predetermined frequency that fits your team's budget.
C. Configure Pub/Sub to call the Cloud Function when a sufficient amount of new data becomes available.
D. Enable model monitoring on the Vertex Al endpoint Configure Pub/Sub to call the Cloud Function when feature drift is detected.

Answer: D

NEW QUESTION # 167
A Machine Learning Specialist wants to determine the appropriate
SageMakerVariantInvocationsPerInstancesetting for an endpoint automatic scaling configuration.
The Specialist has performed a load test on a single instance and determined that peak requests per second (RPS) without service degradation is about 20 RPS. As this is the first deployment, the Specialist intends to set the invocation safety factor to 0.5.
Based on the stated parameters and given that the invocations per instance setting is measured on a per- minute basis, what should the Specialist set as the SageMakerVariantInvocationsPerInstance setting?

A. 0
B. 1
C. 2
D. 2,400

Answer: C

NEW QUESTION # 168
Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?

A. Cloud Composer, BigQuery ML , and Al Platform Prediction
B. Cloud Composer, Al Platform Training with custom containers, and App Engine
C. Vertex AI Pipelines and App Engine
D. Vertex AI Pipelines and Al Platform Prediction

Answer: D

Explanation:
Vertex AI Pipelines and AI Platform Prediction are the platform components that best suit the requirements of the data science team. Vertex AI Pipelines is a service that allows you to orchestrate and automate your machine learning workflows using pipelines. Pipelines are portable and scalable ML workflows that are based on containers. You can use Vertex AI Pipelines to schedule model retraining, use custom containers, and integrate with other Google Cloud services. AI Platform Prediction is a service that allows you to host your trained models and serve online predictions. You can use AI Platform Prediction to deploy models trained on Vertex AI or elsewhere, and benefit from features such as autoscaling, monitoring, logging, and explainability. References:
* Vertex AI Pipelines
* AI Platform Prediction

NEW QUESTION # 169
You are pre-training a large language model on Google Cloud. This model includes custom TensorFlow operations in the training loop Model training will use a large batch size, and you expect training to take several weeks You need to configure a training architecture that minimizes both training time and compute costs What should you do?

Answer: D

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". TPUs2 are Google's custom-developed application-specific integrated circuits (ASICs) used to accelerate machine learning workloads. TPUs are designed to handle large batch sizes, high dimensional data, and complex computations. TPUs can significantly reduce the training time and compute costs of large language models, especially when used with distributed training strategies, such as MultiWorkerMirroredStrategy3. Therefore, option D is the best way to configure a training architecture that minimizes both training time and compute costs for the given use case. The other options are not relevant or optimal for this scenario. References:
* Professional ML Engineer Exam Guide
* TPUs
* MultiWorkerMirroredStrategy
* Google Professional Machine Learning Certification Exam 2023
* Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

NEW QUESTION # 170
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100% Pass Unparalleled Google - Professional-Machine-Learning-Engineer - Well Google Professional Machine Learning Engineer Prep

Professional-Machine-Learning-Engineer Exam Simulator Fee - Professional-Machine-Learning-Engineer Authentic Exam Hub

Google Professional Machine Learning Engineer Sample Questions (Q165-Q170):

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