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Google Professional Machine Learning Engineer Sample Questions (Q117-Q122):

NEW QUESTION # 117
You are developing an ML model to predict house prices. While preparing the data, you discover that an important predictor variable, distance from the closest school, is often missing and does not have high variance. Every instance (row) in your data is important. How should you handle the missing data?

A. Predict the missing values using linear regression.
B. Delete the rows that have missing values.
C. Apply feature crossing with another column that does not have missing values.
D. Replace the missing values with zeros.

Answer: A

Explanation:
The best option for handling missing data in this case is to predict the missing values using linear regression. Linear regression is a supervised learning technique that can be used to estimate the relationship between a continuous target variable and one or more predictor variables. In this case, the target variable is the distance from the closest school, and the predictor variables are the other features in the dataset, such as house size, location, number of rooms, etc. By fitting a linear regression model on the data that has no missing values, we can then use the model to predict the missing values for the distance from the closest school feature. This way, we can preserve all the instances in the dataset and avoid introducing bias or reducing variance. The other options are not suitable for handling missing data in this case, because:
Deleting the rows that have missing values would reduce the size of the dataset and potentially lose important information. Since every instance is important, we want to keep as much data as possible.
Applying feature crossing with another column that does not have missing values would create a new feature that combines the values of two existing features. This might increase the complexity of the model and introduce noise or multicollinearity. It would not solve the problem of missing values, as the new feature would still have missing values whenever the distance from the closest school feature is missing.
Replacing the missing values with zeros would distort the distribution of the feature and introduce bias. It would also imply that the houses with missing values are located at the same distance from the closest school, which is unlikely to be true. A zero value might also be outside the range of the feature, as the distance from the closest school is unlikely to be exactly zero for any house. Reference:
Linear Regression
Imputation of missing values
Google Cloud launches machine learning engineer certification
Google Professional Machine Learning Engineer Certification
Professional ML Engineer Exam Guide
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

NEW QUESTION # 118
You are using Kubeflow Pipelines to develop an end-to-end PyTorch-based MLOps pipeline. The pipeline reads data from BigQuery, processes the data, conducts feature engineering, model training, model evaluation, and deploys the model as a binary file to Cloud Storage. You are writing code for several different versions of the feature engineering and model training steps, and running each new version in Vertex Al Pipelines.
Each pipeline run is taking over an hour to complete. You want to speed up the pipeline execution to reduce your development time, and you want to avoid additional costs. What should you do?

A. Delegate feature engineering to BigQuery and remove it from the pipeline.
B. Add a GPU to the model training step.
C. Comment out the part of the pipeline that you are not currently updating.
D. Enable caching in all the steps of the Kubeflow pipeline.

Answer: D

Explanation:
Kubeflow Pipelines allows for efficient use of compute resources through parallel task execution and caching, which eliminates redundant executions1. By enabling caching in all the steps of the Kubeflow pipeline, you can avoid re-running the same steps when you execute the pipeline multiple times. This can significantly speed up the pipeline execution and reduce your development time without incurring additional costs

NEW QUESTION # 119
You need to analyze user activity data from your company's mobile applications. Your team will use BigQuery for data analysis, transformation, and experimentation with ML algorithms. You need to ensure real-time ingestion of the user activity data into BigQuery. What should you do?

A. Run an Apache Spark streaming job on Dataproc to ingest the data into BigQuery.
B. Configure Pub/Sub and a Dataflow streaming job to ingest the data into BigQuery,
C. Run a Dataflow streaming job to ingest the data into BigQuery.
D. Configure Pub/Sub to stream the data into BigQuery.

Answer: D

NEW QUESTION # 120
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. 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
B. 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.
C. 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
D. 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

Answer: B

NEW QUESTION # 121
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. 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.
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. Execute the unit tests using a Cloud Function that is triggered when messages are sent to the Pub/Sub topic
D. Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run

Answer: B

Explanation:
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

NEW QUESTION # 122
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FREE PDF PROFESSIONAL-MACHINE-LEARNING-ENGINEER EXAM REVIEWS–THE BEST TEST SCORE REPORT FOR YOUR GOOGLE PROFESSIONAL-MACHINE-LEARNING-ENGINEER

Free PDF Professional-Machine-Learning-Engineer Exam Reviews–The Best Test Score Report for your Google Professional-Machine-Learning-Engineer