GOOGLE PROFESSIONAL-MACHINE-LEARNING-ENGINEER TEST CRAM REVIEW - PROFESSIONAL-MACHINE-LEARNING-ENGINEER LATEST STUDY MATERIALS

Google Professional-Machine-Learning-Engineer Test Cram Review - Professional-Machine-Learning-Engineer Latest Study Materials

Google Professional-Machine-Learning-Engineer Test Cram Review - Professional-Machine-Learning-Engineer Latest Study Materials

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If you want to pass the exam in the shortest time, our study materials can help you achieve this dream. Professional-Machine-Learning-Engineer learning quiz according to your specific circumstances, for you to develop a suitable schedule and learning materials, so that you can prepare in the shortest possible time to pass the exam needs everything. If you use our Professional-Machine-Learning-Engineer training prep, you only need to spend twenty to thirty hours to practice our Professional-Machine-Learning-Engineer study materials and you are ready to take the exam.

The Google Professional Machine Learning Engineer certification exam consists of multiple-choice questions that assess the candidate's knowledge of machine learning concepts, data preprocessing, model selection, hyperparameter tuning, model evaluation, and deployment. Professional-Machine-Learning-Engineer Exam is conducted online and is proctored by a third-party vendor. Candidates are required to pass the exam within two hours and thirty minutes and must score at least 80% to pass.

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Professional-Machine-Learning-Engineer Latest Study Materials | Professional-Machine-Learning-Engineer Exam Cram Pdf

Like other Google examinations, the Professional-Machine-Learning-Engineer exam preparation calls for a strong preparation and precise Professional-Machine-Learning-Engineer practice material. Finding original and latest 121 exam questions however, is a difficult process. Candidates require assistance finding the Professional-Machine-Learning-Engineer updated questions. It will be hard for applicants to pass the Google Professional-Machine-Learning-Engineer exam on their first try if Google Professional Machine Learning Engineer questions they have are not real and updated.

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 (Q229-Q234):

NEW QUESTION # 229
You work for an online grocery store. You recently developed a custom ML model that recommends a recipe when a user arrives at the website. You chose the machine type on the Vertex Al endpoint to optimize costs by using the queries per second (QPS) that the model can serve, and you deployed it on a single machine with 8 vCPUs and no accelerators.
A holiday season is approaching and you anticipate four times more traffic during this time than the typical daily traffic You need to ensure that the model can scale efficiently to the increased demand. What should you do?

  • A. 1 Change the machine type on the endpoint to have 32 vCPUs
    2. Set up a monitoring job and an alert for CPU usage
    3 If you receive an alert, scale the vCPUs further as needed
  • B. 1, Maintain the same machine type on the endpoint.
    2 Set up a monitoring job and an alert for CPU usage
    3 If you receive an alert add a compute node to the endpoint
  • C. 1 Maintain the same machine type on the endpoint Configure the endpoint to enable autoscalling based on vCPU usage.
    2 Set up a monitoring job and an alert for CPU usage
    3 If you receive an alert investigate the cause
  • D. 1 Change the machine type on the endpoint to have a GPU_ Configure the endpoint to enable autoscaling based on the GPU usage.
    2 Set up a monitoring job and an alert for GPU usage.
    3 If you receive an alert investigate the cause.

Answer: C

Explanation:
Vertex AI Endpoint is a service that allows you to serve your ML models online and scale them automatically.
You can use Vertex AI Endpoint to deploy the custom ML model that you developed for recommending recipes to the users. You can maintain the same machine type on the endpoint, which is a single machine with
8 vCPUs and no accelerators. This machine type can optimize the costs by using the queries per second (QPS) that the model can serve. You can also configure the endpoint to enable autoscaling based on vCPU usage.
Autoscaling is a feature that allows the endpoint to adjust the number of compute nodes based on the traffic demand. By enabling autoscaling based on vCPU usage, you can ensure that the endpoint can scale efficiently to the increased demand during the holiday season, without overprovisioning or underprovisioning the resources. You can also set up a monitoring job and an alert for CPU usage. Monitoring is a service that allows you to collect and analyze the metrics and logs from your Google Cloud resources. You can use Monitoring to monitor the CPU usage of your endpoint, which is an indicator of the load and performance of your model.
You can also set up an alert for CPU usage, which is a feature that allows you to receive notifications when the CPU usage exceeds a certain threshold. By setting up a monitoring job and an alert for CPU usage, you can keep track of the health and status of your endpoint, and detect any issues or anomalies. If you receive an alert, you can investigate the cause by using the Monitoring dashboard, which provides a graphical interface for viewing and analyzing the metrics and logs from your endpoint. You can also use the Monitoring dashboard to troubleshoot and resolve the issues, such as adjusting the autoscaling parameters, optimizing the model, or updating the machine type. By using Vertex AI Endpoint, autoscaling, and Monitoring, you can ensure that the model can scale efficiently to the increased demand during the holiday season, and handle any issues or alerts that might arise. References:
* [Vertex AI Endpoint documentation]
* [Autoscaling documentation]
* [Monitoring documentation]
* [Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate]


NEW QUESTION # 230
You work for an online travel agency that also sells advertising placements on its website to other companies.
You have been asked to predict the most relevant web banner that a user should see next. Security is important to your company. The model latency requirements are 300ms@p99, the inventory is thousands of web banners, and your exploratory analysis has shown that navigation context is a good predictor.
You want to Implement the simplest solution. How should you configure the prediction pipeline?

  • A. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Memorystore for writing and for reading the user's navigation context, and then deploy the model on Google Kubernetes Engine.
  • B. Embed the client on the website, deploy the gateway on App Engine, and then deploy the model on AI Platform Prediction.
  • C. Embed the client on the website, and then deploy the model on AI Platform Prediction.
  • D. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Cloud Bigtable for writing and for reading the user's navigation context, and then deploy the model on AI Platform Prediction.

Answer: B


NEW QUESTION # 231
You work at a mobile gaming startup that creates online multiplayer games Recently, your company observed an increase in players cheating in the games, leading to a loss of revenue and a poor user experience. You built a binary classification model to determine whether a player cheated after a completed game session, and then send a message to other downstream systems to ban the player that cheated Your model has performed well during testing, and you now need to deploy the model to production You want your serving solution to provide immediate classifications after a completed game session to avoid further loss of revenue. What should you do?

  • A. Save the model files in a Cloud Storage Bucket Create a Cloud Function to read the model files and make online inference requests on the Cloud Function.
  • B. Import the model into Vertex Al Model Registry. Use the Vertex Batch Prediction service to run batch inference jobs.
  • C. Import the model into Vertex Al Model Registry Create a Vertex Al endpoint that hosts the model and make online inference requests.
  • D. Save the model files in a VM Load the model files each time there is a prediction request and run an inference job on the VM.

Answer: C

Explanation:
Online inference is a process where you send a single or a small number of prediction requests to a model and get immediate responses1. Online inference is suitable for scenarios where you need timely predictions, such as detecting cheating in online games. Online inference requires that the model is deployed to an endpoint, which is a resource that provides a service URL for prediction requests2.
Vertex AI Model Registry is a central repository where you can manage the lifecycle of your ML models3. You can import models from various sources, such as custom models or AutoML models, and assign them to different versions and aliases3. You can also deploy models to endpoints, which are resources that provide a service URL for online prediction2.
By importing the model into Vertex AI Model Registry, you can leverage the Vertex AI features to monitor and update the model3. You can use Vertex AI Experiments to track and compare the metrics of different model versions, such as accuracy, precision, recall, and AUC. You can also use Vertex AI Explainable AI to generate feature attributions that show how much each input feature contributed to the model's prediction.
By creating a Vertex AI endpoint that hosts the model, you can use the Vertex AI Prediction service to serve online inference requests2. Vertex AI Prediction provides various benefits, such as scalability, reliability, security, and logging2. You can use the Vertex AI API or the Google Cloud console to send online inference requests to the endpoint and get immediate classifications4.
Therefore, the best option for your scenario is to import the model into Vertex AI Model Registry, create a Vertex AI endpoint that hosts the model, and make online inference requests.
The other options are not suitable for your scenario, because they either do not provide immediate classifications, such as using batch prediction or loading the model files each time, or they do not use Vertex AI Prediction, which would require more development and maintenance effort, such as creating a Cloud Function or a VM.
Reference:
Online versus batch prediction | Vertex AI | Google Cloud
Deploy a model to an endpoint | Vertex AI | Google Cloud
Introduction to Vertex AI Model Registry | Google Cloud
Get online predictions | Vertex AI | Google Cloud


NEW QUESTION # 232
You are developing a custom image classification model in Python. You plan to run your training application on Vertex Al Your input dataset contains several hundred thousand small images You need to determine how to store and access the images for training. You want to maximize data throughput and minimize training time while reducing the amount of additional code. What should you do?

  • A. Store image files in Cloud Storage and access them by using serialized records.
  • B. Store image files in Cloud Storage and access them directly.
  • C. Store image files in Cloud Filestore and access them directly by using an NFS mount point.
  • D. Store image files in Cloud Filestore, and access them by using serialized records.

Answer: A

Explanation:
Cloud Storage is a scalable and cost-effective storage service for any type of data. By storing image files in Cloud Storage, you can access them from anywhere and avoid the overhead of managing your own storage infrastructure. However, accessing image files directly from Cloud Storage can be slow and inefficient, especially for large-scale training. A better option is to use serialized records, such as TFRecord or Apache Avro, which are binary formats that store multiple images and their labels in a single file. Serialized records can improve the data throughput and reduce the network latency, as well as enable data compression and sharding. You can use TensorFlow or Apache Beam APIs to create and read serialized records from Cloud Storage. This solution requires minimal code changes and can speed up your training time significantly. Reference:
Cloud Storage | Google Cloud
TFRecord and tf.Example | TensorFlow Core
Apache Avro 1.10.2 Specification
Using Apache Beam with Cloud Storage | Cloud Storage


NEW QUESTION # 233
You are deploying a new version of a model to a production Vertex Al endpoint that is serving traffic You plan to direct all user traffic to the new model You need to deploy the model with minimal disruption to your application What should you do?

  • A. 1, Create a new model Set it as the default version Upload the model to Vertex Al Model Registry
    2 Deploy the new model to the existing endpoint
  • B. 1 Create a new endpoint.
    2 Create a new model Set it as the default version Upload the model to Vertex Al Model Registry.
    3. Deploy the new model to the new endpoint.
    4 Update Cloud DNS to point to the new endpoint
  • C. 1. Create a new endpoint.
    2. Create a new model Set the parentModel parameter to the model ID of the currently deployed model and set it as the default version Upload the model to Vertex Al Model Registry
    3. Deploy the new model to the new endpoint and set the new model to 100% of the traffic
  • D. 1 Create a new model Set the parentModel parameter to the model ID of the currently deployed model Upload the model to Vertex Al Model Registry.
    2 Deploy the new model to the existing endpoint and set the new model to 100% of the traffic.

Answer: D


NEW QUESTION # 234
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