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Google Professional Machine Learning Engineer Certification Exam is an industry-leading certification that validates the skills and knowledge required to design, build, and deploy scalable and reliable machine learning models on Google Cloud Platform. Google Professional Machine Learning Engineer certification is designed for professionals who have experience in developing and deploying machine learning models in a production environment and are looking to advance their career in machine learning.

The Professional Machine Learning Engineer exam is a performance-based assessment that evaluates the candidate's ability to solve real-world problems using machine learning techniques. Professional-Machine-Learning-Engineer exam consists of a series of hands-on tasks that require the candidate to demonstrate their understanding of various machine learning concepts and their ability to apply them in practical scenarios. Professional-Machine-Learning-Engineer exam is conducted online and can be taken from anywhere in the world.

Google Professional Machine Learning Engineer Sample Questions (Q139-Q144):

NEW QUESTION # 139
Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this:

A)

B)

C)

D)

A. Option D
B. Option A
C. Option B
D. Option C

Answer: A

NEW QUESTION # 140
You work for a gaming company that develops massively multiplayer online (MMO) games. You built a TensorFlow model that predicts whether players will make in-app purchases of more than $10 in the next two weeks. The model's predictions will be used to adapt each user's game experience. User data is stored in BigQuery. How should you serve your model while optimizing cost, user experience, and ease of management?

A. Deploy the model to Vertex AI Prediction. Make predictions using batch reading data from Cloud Bigtable, and push the data to Cloud SQL.
B. Embed the model in the mobile application. Make predictions after every in-app purchase event is published in Pub/Sub, and push the data to Cloud SQL.
C. Embed the model in the streaming Dataflow pipeline. Make predictions after every in-app purchase event is published in Pub/Sub, and push the data to Cloud SQL.
D. Import the model into BigQuery ML. Make predictions using batch reading data from BigQuery, and push the data to Cloud SQL

Answer: D

NEW QUESTION # 141
A data scientist uses an Amazon SageMaker notebook instance to conduct data exploration and analysis. This requires certain Python packages that are not natively available on Amazon SageMaker to be installed on the notebook instance.
How can a machine learning specialist ensure that required packages are automatically available on the notebook instance for the data scientist to use?

A. Create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.
B. Use the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook.
C. Create a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and place the file under the /etc/init directory of each Amazon SageMaker notebook instance.
D. Install AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands.

Answer: C

Explanation:
Explanation
Explanation/Reference: https://towardsdatascience.com/automating-aws-sagemaker-notebooks-2dec62bc2c84

NEW QUESTION # 142
A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains Personally Identifiable Information (PII).
The dataset:
* Must be accessible from a VPC only.
* Must not traverse the public internet.
How can these requirements be satisfied?

A. Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.
B. Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.
C. Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance
D. Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.

Answer: B

NEW QUESTION # 143
You lead a data science team at a large international corporation. Most of the models your team trains are large-scale models using high-level TensorFlow APIs on AI Platform with GPUs. Your team usually takes a few weeks or months to iterate on a new version of a model. You were recently asked to review your team's spending. How should you reduce your Google Cloud compute costs without impacting the model's performance?

A. Use AI Platform to run distributed training jobs with checkpoints.
B. Migrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs with checkpoints.
C. Use AI Platform to run distributed training jobs without checkpoints.
D. Migrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs without checkpoints.

Answer: D

NEW QUESTION # 144
......

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