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Professional-Machine-Learning-Engineer Exam Dumps : Google Professional Machine Learning Engineer

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Google Professional-Machine-Learning-Engineer Exam Dumps FAQs

Q. # 1: What is the Google Professional-Machine-Learning-Engineer Exam?

The Google Professional-Machine-Learning-Engineer Exam is a certification test designed to assess an individuals ability to design, build, and deploy machine learning models using Google Cloud technologies. It evaluates skills in model architecture, data pipeline creation, and metrics interpretation.

Q. # 2: Who should take the Google Professional Machine Learning Engineer Exam?

The Google Professional-Machine-Learning-Engineer Exam is ideal for experienced machine learning engineers who design, build, and productionize ML models on Google Cloud Platform (GCP). It validates your ability to solve real-world business problems using Google's cutting-edge machine learning tools and workflows.

Q. # 3: What topics are covered in the Google Professional-Machine-Learning-Engineer Exam?

The Google Professional-Machine-Learning-Engineer Exam covers topics such as ML model architecture, data engineering, MLOps, responsible AI, and the use of Google Cloud tools like BigQuery ML and Vertex AI.

Q. # 4: How many questions are on the Google Professional-Machine-Learning-Engineer Exam?

The Google Professional-Machine-Learning-Engineer Exam consists of 50-60 multiple-choice and multiple-select questions.

Q. # 5: What is the duration of the Google Professional-Machine-Learning-Engineer Exam?

The Google Professional-Machine-Learning-Engineer Exam duration is two hours.

Q. # 6: What is the passing score for the Google Professional-Machine-Learning-Engineer Exam?

The passing score for the Google Professional-Machine-Learning-Engineer Exam is 70%.

Q. # 7: Is there a success guarantee with CertsTopics Professional-Machine-Learning-Engineer study materials?

CertsTopics offers a success guarantee, meaning that if you do not pass the Machine Learning Engineer certification exam after using Professional-Machine-Learning-Engineer study materials, you may be eligible for a refund or additional support.

Q. # 8: Are there any discounts available for CertsTopics Professional-Machine-Learning-Engineer study materials?

CertsTopics occasionally offers promotions and discounts. Check our website for the latest deals and offers.

Q. # 9: Are the Professional-Machine-Learning-Engineer exam questions from CertsTopics updated regularly?

Yes, CertsTopics regularly updates its Professional-Machine-Learning-Engineer exam questions to reflect the latest exam changes and industry trends, ensuring that you have access to the most current information.

Google Professional Machine Learning Engineer Questions and Answers

Question 1

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?

Options:

A.

Use the Cloud Data Loss Prevention (DLP) API to de-identify the PI! before performing data exploration and preprocessing.

B.

Use customer-managed encryption keys (CMEK) to encrypt the Pll data at rest and decrypt the Pll data during data exploration and preprocessing.

C.

Use a VM inside a VPC Service Controls security perimeter to perform data exploration and preprocessing.

D.

Use Google-managed encryption keys to encrypt the Pll data at rest, and decrypt the Pll data during data exploration and preprocessing.

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Question 2

You work as an ML researcher at an investment bank and are experimenting with the Gemini large language model (LLM). You plan to deploy the model for an internal use case and need full control of the model’s underlying infrastructure while minimizing inference time. Which serving configuration should you use for this task?

Options:

A.

Deploy the model on a Vertex AI endpoint using one-click deployment in Model Garden.

B.

Deploy the model on a Google Kubernetes Engine (GKE) cluster manually by creating a custom YAML manifest.

C.

Deploy the model on a Vertex AI endpoint manually by creating a custom inference container.

D.

Deploy the model on a Google Kubernetes Engine (GKE) cluster using the deployment options in Model Garden.

Question 3

You are designing an ML recommendation model for shoppers on your company's ecommerce website. You will use Recommendations Al to build, test, and deploy your system. How should you develop recommendations that increase revenue while following best practices?

Options:

A.

Use the "Other Products You May Like" recommendation type to increase the click-through rate

B.

Use the "Frequently Bought Together' recommendation type to increase the shopping cart size for each order.

C.

Import your user events and then your product catalog to make sure you have the highest quality event stream

D.

Because it will take time to collect and record product data, use placeholder values for the product catalog to test the viability of the model.