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Google Professional Machine Learning Engineer exam is designed to test the expertise of individuals in the field of machine learning. Google Professional Machine Learning Engineer certification provides a strong foundation in machine learning concepts and tools, as well as the ability to develop and deploy sophisticated machine learning models using Google Cloud technologies. Professional-Machine-Learning-Engineer Exam assesses one's ability to use Google’s machine learning tools and services to build and deploy robust, scalable, and efficient machine learning models.

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Google Professional Machine Learning Engineer Exam is a comprehensive program that covers a wide range of topics related to machine learning. Professional-Machine-Learning-Engineer Exam consists of multiple-choice questions, coding challenges, and hands-on tasks that evaluate the candidate's practical skills and knowledge. By earning this certification, candidates can demonstrate their proficiency in machine learning and stand out in a competitive job market.

Google Professional Machine Learning Engineer Sample Questions (Q281-Q286):

NEW QUESTION # 281
You developed an ML model with Al Platform, and you want to move it to production. You serve a few thousand queries per second and are experiencing latency issues. Incoming requests are served by a load balancer that distributes them across multiple Kubeflow CPU-only pods running on Google Kubernetes Engine (GKE). Your goal is to improve the serving latency without changing the underlying infrastructure. What should you do?

A. Significantly increase the max_enqueued_batches TensorFlow Serving parameter
B. Switch to the tensorflow-model-server-universal version of TensorFlow Serving
C. Recompile TensorFlow Serving using the source to support CPU-specific optimizations Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes
D. Significantly increase the max_batch_size TensorFlow Serving parameter

Answer: C

Explanation:
https://www.tensorflow.org/tfx/serving/performance

NEW QUESTION # 282
You are an ML engineer in the contact center of a large enterprise. You need to build a sentiment analysis tool that predicts customer sentiment from recorded phone conversations. You need to identify the best approach to building a model while ensuring that the gender, age, and cultural differences of the customers who called the contact center do not impact any stage of the model development pipeline and results. What should you do?

A. Extract sentiment directly from the voice recordings
B. Convert the speech to text and build a model based on the words
C. Convert the speech to text and extract sentiment using syntactical analysis
D. Convert the speech to text and extract sentiments based on the sentences

Answer: D

NEW QUESTION # 283
You are a data scientist at an industrial equipment manufacturing company. You are developing a regression model to estimate the power consumption in the company's manufacturing plants based on sensor data collected from all of the plants. The sensors collect tens of millions of records every day. You need to schedule daily training runs for your model that use all the data collected up to the current date. You want your model to scale smoothly and require minimal development work. What should you do?

A. Develop a custom PyTorch regression model, and optimize it using Vertex Al Training
B. Develop a custom TensorFlow regression model, and optimize it using Vertex Al Training.
C. Develop a custom scikit-learn regression model, and optimize it using Vertex Al Training
D. Develop a regression model using BigQuery ML.

Answer: D

Explanation:
BigQuery ML is a powerful tool that allows you to build and deploy machine learning models directly within BigQuery, Google's fully-managed, serverless data warehouse. It allows you to create regression models using SQL, which is a familiar and easy-to-use language for many data scientists. It also allows you to scale smoothly and require minimal development work since you don't have to worry about cluster management and it's fully-managed by Google.
BigQuery ML also allows you to run your training on the same data where it's stored, this will minimize data movement, and thus minimize cost and time.
Reference:
BigQuery ML
BigQuery ML for regression
BigQuery ML for scalability

NEW QUESTION # 284
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. 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.
C. Import the model into Vertex Al Model Registry Create a Vertex Al endpoint that hosts the model and make online inference requests.
D. Import the model into Vertex Al Model Registry. Use the Vertex Batch Prediction service to run batch inference jobs.

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.
References:
* 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 # 285
Your company manages an application that aggregates news articles from many different online sources and sends them to users. You need to build a recommendation model that will suggest articles to readers that are similar to the articles they are currently reading. Which approach should you use?

A. Encode all articles into vectors using word2vec, and build a model that returns articles based on vector similarity.
B. Build a logistic regression model for each user that predicts whether an article should be recommended to a user.
C. Manually label a few hundred articles, and then train an SVM classifier based on the manually classified articles that categorizes additional articles into their respective categories.
D. Create a collaborative filtering system that recommends articles to a user based on the user's past behavior.

Answer: A

Explanation:
https://cloud.google.com/blog/topics/developers-practitioners/meet-ais-multitool-vector-embeddings

NEW QUESTION # 286
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