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Google Professional-Machine-Learning-Engineer Exam Overview:

Certification Vendor:	Google Cloud
Exam Name:	Google Cloud Professional Machine Learning Engineer Certification Exam
Exam Number:	Professional-Machine-Learning-Engineer
Available Languages:	English, Japanese
Exam Price:	$200 USD (plus tax where applicable)
Real Exam Qty:	50-60
Exam Format:	Multiple select, Multiple choice
Passing Score:	Not officially published, approximately 70%
Exam Duration:	120 minutes
Certificate Validity Period:	2 years
Related Certifications:	Google Cloud Professional Cloud Architect Google Cloud Professional Data Engineer
Recommended Training:	Official Exam Guide Google Cloud Skills Boost - Professional Machine Learning Engineer Learning Path
Exam Registration:	Google Cloud Certification Registration
Sample Questions:	Google Professional-Machine-Learning-Engineer Sample Questions
Exam Way:	Online-proctored remote exam or onsite-proctored exam at authorized test centers
Pre Condition:	No mandatory prerequisites; recommended 3+ years industry experience including 1+ year designing/managing Google Cloud solutions
Official Syllabus URL:	https://cloud.google.com/learn/certification/machine-learning-engineer

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Google Professional Machine Learning Engineer certification is a highly respected and sought-after certification in the field of machine learning. Google Professional Machine Learning Engineer certification is designed to validate the skills and expertise of professionals who are responsible for designing, building, managing, and deploying machine learning models at scale using Google Cloud technologies. Google Professional Machine Learning Engineer certification exam covers a wide range of topics related to machine learning, and candidates must have a minimum of three years of experience in the field of machine learning to be eligible for the exam.

Google Professional Machine Learning Engineer Sample Questions (Q256-Q261):

NEW QUESTION # 256
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 Edge and export it as a Core ML model Configure your mobile application to use the mlmodel file directly.
B. 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.
C. 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.
D. 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

Answer: A

NEW QUESTION # 257
You need to create a working environment in Vertex AI Workbench for a team of data scientists. Each data scientist has different VM CPU and RAM and package requirements, and will be assigned a personal notebook instance. You want each instance to have a custom set of packages pre-installed. Your company wants to minimize the running cost of notebook instances. How should you create the environment?

A. Create Vertex AI Workbench instances based on a Deep Learning Containers container image. Use n1- standard-4 as the default machine type. Verify idle shutdown of each instance.
B. Create Vertex AI Workbench instances based on a custom container image derived from a Deep Learning Containers image. Use n1-standard-4 as the default machine type. Verify idle shutdown of each instance.
C. Create Vertex AI Workbench instances based on a Deep Learning Containers container image. Disable idle shutdown of each instance.
D. Create Vertex AI Workbench instances based on a custom container image derived from a Deep Learning Containers image. Verify idle shutdown of each instance.

Answer: D

Explanation:
This scenario requires balancing customization with cost-efficiency using Vertex AI Workbench.
* Custom Container Images: Since the team requires specific, pre-installed packages that are not part of the standard Google Deep Learning Containers (DLC), creating a custom image derived from a DLC is the most efficient way to ensure environment consistency across instances.
* Cost Management: To minimize costs, " Idle Shutdown " is a critical feature. It automatically stops the notebook instance when it is not in use, ensuring you only pay for compute time actually utilized.
* Why Option C is better than D: The prompt explicitly states that each data scientist has different VM CPU and RAM requirements. Setting a fixed " default " machine type like n1-standard-4 (Option D) contradicts the requirement to cater to different hardware needs and could lead to over-provisioning (wasted cost) or under-provisioning for specific users. Option C focuses on the custom image and the cost-saving mechanism (Idle Shutdown) while allowing for hardware flexibility.

NEW QUESTION # 258
You work for a bank You have been asked to develop an ML model that will support loan application decisions. You need to determine which Vertex Al services to include in the workflow You want to track the model's training parameters and the metrics per training epoch. You plan to compare the performance of each version of the model to determine the best model based on your chosen metrics. Which Vertex Al services should you use?

A. Vertex Al Pipelines. Vertex Al Feature Store, and Vertex Al TensorBoard
B. Vertex ML Metadata Vertex Al Feature Store, and Vertex Al Vizier
C. Vertex ML Metadata Vertex Al Experiments, and Vertex Al TensorBoard
D. Vertex Al Pipelines. Vertex Al Experiments, and Vertex Al Vizier

Answer: C

Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to "track the lineage of pipeline artifacts". Vertex ML Metadata2 is a service that allows you to store, query, and visualize metadata associated with your ML workflows, such as datasets, models, metrics, and executions. Vertex ML Metadata helps you track the provenance and lineage of your ML artifacts and understand the relationships between them. Vertex AI Experiments3 is a service that allows you to track and compare the results of your model training runs. Vertex AI Experiments automatically logs metadata such as hyperparameters, metrics, and artifacts for each training run. You can use Vertex AI Experiments to train your custom model using TensorFlow, PyTorch, XGBoost, or scikit-learn. Vertex AI TensorBoard4 is a service that allows you to visualize and monitor your ML experiments using TensorBoard, an open source tool for ML visualization.
Vertex AI TensorBoard helps you track the model's training parameters and the metrics per training epoch, and compare the performance of each version of the model. Therefore, option C is the best way to determine which Vertex AI services to include in the workflow for the given use case. The other options are not relevant or optimal for this scenario. References:
* Professional ML Engineer Exam Guide
* Vertex ML Metadata
* Vertex AI Experiments
* Vertex AI TensorBoard
* Google Professional Machine Learning Certification Exam 2023
* Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

NEW QUESTION # 259
You work with a data engineering team that has developed a pipeline to clean your dataset and save it in a Cloud Storage bucket. You have created an ML model and want to use the data to refresh your model as soon as new data is available. As part of your CI/CD workflow, you want to automatically run a Kubeflow Pipelines training job on Google Kubernetes Engine (GKE). How should you architect this workflow?

A. Configure your pipeline with Dataflow, which saves the files in Cloud Storage After the file is saved, start the training job on a GKE cluster
B. Configure a Cloud Storage trigger to send a message to a Pub/Sub topic when a new file is available in a storage bucket. Use a Pub/Sub-triggered Cloud Function to start the training job on a GKE cluster
C. Use App Engine to create a lightweight python client that continuously polls Cloud Storage for new files As soon as a file arrives, initiate the training job
D. Use Cloud Scheduler to schedule jobs at a regular interval. For the first step of the job. check the timestamp of objects in your Cloud Storage bucket If there are no new files since the last run, abort the job.

Answer: A

NEW QUESTION # 260
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Choose three.)

A. The validation channel identifying the location of validation data on an Amazon S3 bucket.
B. The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users.
C. Hyperparameters in a JSON array as documented for the algorithm used.
D. The training channel identifying the location of training data on an Amazon S3 bucket.
E. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.
F. The output path specifying where on an Amazon S3 bucket the trained model will persist.

Answer: D,E,F

Explanation:
Explanation

NEW QUESTION # 261
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