Instant Access Google.Professional-Machine-Learning-Engineer.v2026-07-28.q219 Actual Practice Test Engine for Free (Page 13)

40%off

Professional-Machine-Learning-Engineer Premium Dumps

Latest Professional-Machine-Learning-Engineer Exam Premium Dumps provide by TrainingQuiz.com to help you Passing Professional-Machine-Learning-Engineer Exam! TrainingQuiz.com offers the updated Professional-Machine-Learning-Engineer exam dumps, the TrainingQuiz.com Professional-Machine-Learning-Engineer exam questions has been updated to correct Answer. Get the latest TrainingQuiz.com Professional-Machine-Learning-Engineer pdf dumps with Exam Engine here:

(350 Q&As Dumps, 40%OFF Special Discount: DumpsDB)

Question 56

You developed a Vertex Al pipeline that trains a classification model on data stored in a large BigQuery table.
The pipeline has four steps, where each step is created by a Python function that uses the KubeFlow v2 API The components have the following names:
You launch your Vertex Al pipeline as the following:
You perform many model iterations by adjusting the code and parameters of the training step. You observe high costs associated with the development, particularly the data export and preprocessing steps. You need to reduce model development costs. What should you do?

A.Set the parameter to .PipelineJobenable_cachingTrue
B.Change the YAML file name of the component to , , and .export.yamlpreprocess.yamltrain-{dt}.yamlcalibrate-{dt}.yaml
C.Move the first step of the pipeline into a separate pipeline and provide the cached Cloud Storage path as input to the main pipeline.
D.Change the name of the pipeline to .f"my-awesome-pipeline-{dt}"

Question 57

You are developing an ML pipeline using Vertex AI Pipelines. You want your pipeline to upload a new version of the XGBoost model to Vertex AI Model Registry and deploy it to Vertex AI Endpoints for online inference. You want to use the simplest approach. What should you do?

A.Use the Vertex AI ModelEvaluationOp component to evaluate the model
B.Use the Vertex AI REST API within a custom component based on a vertex-ai/prediction/xgboost- cpu image
C.Chain the Vertex AI ModelUploadOp and ModelDeployOp components together
D.Use the Vertex AI SDK for Python within a custom component based on a python:3.10 image

Question 58

Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers1 account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?

A.1. Create a Pub/Sub topic for each user
2 Deploy a Cloud Function that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.
B.1 Build a notification system on Firebase
2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user's account balance will drop below the $25 threshold
C.1. Create a Pub/Sub topic for each user
2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold
D.1. Build a notification system on Firebase
2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold

Question 59

You have written unit tests for a Kubeflow Pipeline that require custom libraries. You want to automate the execution of unit tests with each new push to your development branch in Cloud Source Repositories. What should you do?

A.Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run
B.Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.
C.Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories Configure a Pub/Sub trigger for Cloud Run, and execute the unit tests on Cloud Run.
D.Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories. Execute the unit tests using a Cloud Function that is triggered when messages are sent to the Pub/Sub topic

Correct Answer: B

Cloud Build is a service that executes your builds on Google Cloud Platform infrastructure. Cloud Build can import source code from Cloud Source Repositories, Cloud Storage, GitHub, or Bitbucket, execute a build to your specifications, and produce artifacts such as Docker containers or Java archives1 Cloud Build allows you to set up automated triggers that start a build when changes are pushed to a source code repository. You can configure triggers to filter the changes based on the branch, tag, or file path2 To automate the execution of unit tests for a Kubeflow Pipeline that require custom libraries, you can use Cloud Build to set an automated trigger to execute the unit tests when changes are pushed to your development branch in Cloud Source Repositories. You can specify the steps of the build in a YAML or JSON file, such as installing the custom libraries, running the unit tests, and reporting the results. You can also use Cloud Build to build and deploy the Kubeflow Pipeline components if the unit tests pass3 The other options are not recommended or feasible. Writing a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run is not a good practice, as it does not leverage the benefits of Cloud Build and its integration with Cloud Source Repositories. Setting up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories and using a Pub
/Sub trigger for Cloud Run or Cloud Function to execute the unit tests is unnecessarily complex and inefficient, as it adds extra steps and latency to the process. Cloud Run and Cloud Function are also not designed for executing unit tests, as they have limitations on the memory, CPU, and execution time45 References: 1: Cloud Build overview 2: Creating and managing build triggers 3: Building and deploying Kubeflow Pipelines using Cloud Build 4: Cloud Run documentation 5: Cloud Functions documentation

Question 60

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.Create a collaborative filtering system that recommends articles to a user based on the user's past behavior.
B.Encode all articles into vectors using word2vec, and build a model that returns articles based on vector similarity.
C.Build a logistic regression model for each user that predicts whether an article should be recommended to a user.
D.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.

Correct Answer: B

Option A is incorrect because creating a collaborative filtering system that recommends articles to a user based on the user's past behavior is not the best approach to suggest articles that are similar to the articles they are currently reading. Collaborative filtering is a method of recommendation that uses the ratings or preferences of other users to predict the preferences of a target user1. However, this method does not consider the content or features of the articles, and may not be able to find articles that are similar in terms of topic, style, or sentiment.
Option B is correct because encoding all articles into vectors using word2vec, and building a model that returns articles based on vector similarity is a suitable approach to suggest articles that are similar to the articles they are currently reading. Word2vec is a technique that learns low-dimensional and dense representations of words from a large corpus of text, such that words that are semantically similar have similar vectors2. By applying word2vec to the articles, we can obtain vector representations of the articles that capture their meaning and usage. Then, we can use a similarity measure, such as cosine similarity, to find articles that have similar vectors to the current article3.
Option C is incorrect because building a logistic regression model for each user that predicts whether an article should be recommended to a user is not a feasible approach to suggest articles that are similar to the articles they are currently reading. Logistic regression is a supervised learning method that models the probability of a binary outcome (such as recommend or not) based on some input features (such as user profile or article content)4. However, this method requires a large amount of labeled data for each user, which may not be available or scalable. Moreover, this method does not directly measure the similarity between articles, but rather the likelihood of a user's preference.
Option D is incorrect because manually labeling a few hundred articles, and then training an SVM classifier based on the manually classified articles that categorizes additional articles into their respective categories is not an effective approach to suggest articles that are similar to the articles they are currently reading. SVM (support vector machine) is a supervised learning method that finds a hyperplane that separates the data into different classes (such as news categories) with the maximum margin5. However, this method also requires a large amount of labeled data, which may be costly and time-consuming to obtain. Moreover, this method does not account for the fine-grained similarity between articles within the same category, or the cross-category similarity between articles from different categories.
Reference:
Collaborative filtering
Word2vec
Cosine similarity
Logistic regression
SVM

Professional-Machine-Learning-Engineer Premium Dumps

Question 56

Question 57

Question 58

Question 59

Question 60

Download PDF File