Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam braindumps download (PROFESSIONAL MACHINE LEARNING ENGINEER exam dumps Free)

QUESTION: 151

You work at a retail company, and are tasked with developing an ML model to predict product sales. Your company’s historical sales data is stored in BigQuery and includes features such as date, store location, product category, and promotion details. You need to choose the most effective combination of a BigQuery ML model and feature engineering to maximize prediction accuracy.
What should you do?

Use a linear regression model. Perform one-hot encoding on categorical features, and create additional features based on the date, such as day of the week or month.
Use a boosted tree model. Perform label encoding on categorical features, and transform the date column into numeric values.
Use an autoencoder model. Perform label encoding on categorical features, and normalize the date column.
Use a matrix factorization model. Perform one-hot encoding on categorical features, and create interaction features between the store location and product category variables.

Answer(s): A

Explanation:

Linear regression model: This is a suitable choice for predicting continuous variables like product sales. BigQuery ML supports linear regression, making it a practical option for this use case.
One-hot encoding on categorical features: This encoding method is effective for handling categorical variables (e.g., store location, product category) without imposing an ordinal relationship that doesn't exist.
Feature engineering for date-related features: Creating features like "day of the week" or "month" helps the model capture temporal patterns in the sales data, which are often significant predictors in retail.

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QUESTION: 152

You recently deployed a model to a Vertex AI endpoint and set up online serving in Vertex AI Feature Store. You have configured a daily batch ingestion job to update your featurestore. During the batch ingestion jobs, you discover that CPU utilization is high in your featurestore’s online serving nodes and that feature retrieval latency is high. You need to improve online serving performance during the daily batch ingestion.
What should you do?

Schedule an increase in the number of online serving nodes in your featurestore prior to the batch ingestion jobs
Enable autoscaling of the online serving nodes in your featurestore
Enable autoscaling for the prediction nodes of your DeployedModel in the Vertex AI endpoint
Increase the worker_count in the ImportFeatureValues request of your batch ingestion job

Answer(s): B

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QUESTION: 153

You have created multiple versions of an ML model and have imported them to Vertex AI Model Registry. You want to perform A/B testing to identify the best performing model using the simplest approach.
What should you do?

Split incoming traffic to distribute prediction requests among the versions. Monitor the performance of each version using Vertex AI's built-in monitoring tools.
Split incoming traffic among Google Kubernetes Engine (GKE) clusters, and use Traffic Director to distribute prediction requests to different versions. Monitor the performance of each version using Cloud Monitoring.
Split incoming traffic to distribute prediction requests among the versions. Monitor the performance of each version using Looker Studio dashboards that compare logged data for each version.
Split incoming traffic among separate Cloud Run instances of deployed models. Monitor the performance of each version using Cloud Monitoring.

Answer(s): A

Explanation:

Vertex AI Model Registry supports traffic splitting across multiple model versions natively, allowing you to distribute prediction requests simply and monitor each version’s performance using built-in Vertex AI monitoring tools. This approach is the simplest and most integrated for A/B testing within Vertex AI.

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QUESTION: 154

You are deploying a generative AI chatbot for an online gaming community using Gemini Enterprise Agent Platform. During beta testing, you discover that users are able to input malicious prompts that cause the model to generate hate speech and violent content. You need to implement a guardrail to block this content in production with minimal development overhead.
What should you do?

Update the system instruction (system prompt) of the model to explicitly state: “You are a helpful assistant. Do not generate hate speech or violent content.”
Implement and configure Model Armor to sanitize responses if hateful content is detected.
Create a supervised fine-tuning (SFT) job in Agent Platform, and tune the model using a curated dataset of safe, policy-compliant gaming conversations.
Integrate the Cloud Natural Language API’s moderation capabilities to analyze and block toxic user prompts before they reach the chatbot model.

Answer(s): B

Explanation:

Model Armor provides a managed guardrail that inspects prompts and responses for unsafe content, including hate speech and violence, and can block or sanitize detected content. It reduces development and maintenance effort compared with building custom moderation logic or relying only on prompting.

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QUESTION: 155

While performing exploratory data analysis on a dataset, you find that an important categorical feature has 5% null values. You want to minimize the bias that could result from the missing values. How should you handle the missing values?

Remove the rows with missing values, and upsample your dataset by 5%.
Replace the missing values with the feature’s mean.
Replace the missing values with a placeholder category indicating a missing value.
Move the rows with missing values to your validation dataset.

Answer(s): C

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Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps Professional Machine Learning Engineer Questions 151–155 (page: 32)

QUESTION: 151

Explanation:

QUESTION: 152

QUESTION: 153

Explanation:

QUESTION: 154

Explanation:

QUESTION: 155

Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 151–155 (page: 32)