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

QUESTION: 211

You need to train a natural language model to perform text classification on product descriptions that contain millions of examples and 100,000 unique words. You want to preprocess the words individually so that they can be fed into a recurrent neural network.
What should you do?

Create a hot-encoding of words, and feed the encodings into your model.
Identify word embeddings from a pre-trained model, and use the embeddings in your model.
Sort the words by frequency of occurrence, and use the frequencies as the encodings in your model.
Assign a numerical value to each word from 1 to 100,000 and feed the values as inputs in your model.

Answer(s): B

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QUESTION: 212

You need to develop an image classification model by using a large dataset that contains labeled images in a Cloud Storage bucket.
What should you do?

Use Vertex AI Pipelines with the Kubeflow Pipelines SDK to create a pipeline that reads the images from Cloud Storage and trains the model.
Use Vertex AI Pipelines with TensorFlow Extended (TFX) to create a pipeline that reads the images from Cloud Storage and trains the model.
Import the labeled images as a managed dataset in Vertex AI, and use AutoML to train the model.
Convert the image dataset to a tabular format using Dataflow. Load the data into BigQuery and use BigQuery ML to train the model.

Answer(s): C

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QUESTION: 213

You work at a manufacturing company. You are building an assistant to help engineers write scripts for a proprietary, legacy machine control language called “MechScript.” MechScript is highly specialized, uses unique syntax not found in public datasets, and is strictly internal to your company.
You built the base Gemini model using Agent Platform RAG Engine and grounded the model with the MechScript reference manual. The model correctly answers questions about the language, but it consistently fails to generate syntactically correct code snippets, often hallucinating Python-like syntax instead.
What should you do?

Increase the temperature parameter in the Gemini API call.
Use the Agent Platform supervised fine-tuning (SFT) service with a dataset of valid MechScript examples.
Increase the number of retrieved chunks in the RAG Engine call.
Copy the MechScript manual into the system prompt using Gemini’s context window.

Answer(s): B

Explanation:

Supervised fine-tuning with high-quality MechScript input-output examples teaches the model the language’s proprietary syntax and code-generation patterns. RAG supplies reference knowledge at inference time, but fine-tuning is more appropriate when the model must consistently produce outputs in a specialized format that is absent from its original training data.

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QUESTION: 214

You work for a media company. You are developing a solution that requires transcription and diarizing (speaker identification) of live audio streams in near real-time. You are using an optimized WhisperX model built on the PyTorch framework that uses several libraries and weights sourced from the Hugging Face Hub. The deployment must include proprietary Python post-processing logic including speaker diarization and alignment correction that executes immediately after model inference.
You need your solution to be efficient, scalable, handle high throughput, and minimize serving latency. How should you deploy this model and its required logic?

Deploy the WhisperX model artifacts to an Agent Platform endpoint using a pre-built PyTorch serving container configured with a GPU. Host the custom post-processing scripts in a Cloud Run function that receives the endpoint’s prediction output and performs the final diarization.
Use a self-managed Google Kubernetes Engine (GKE) cluster for deployment. Package the model and logic into a container, and configure the cluster’s horizontal pod autoscaler (HPA) to manage throughput.
Package the WhisperX model artifacts, the specific Python post-processing scripts, and the necessary dependencies (including Hugging Face libraries) into a single, custom serving container image. Deploy this custom image to a GPU-enabled Agent Platform endpoint.
Locate the WhisperX model in the Model Garden and use the “1-click deploy” feature to deploy it to an Agent Platform endpoint. Route the output to a Dataflow pipeline that uses a custom DoFn function to handle the post-processing and logging.

Answer(s): C

Explanation:

A custom serving container can bundle the optimized WhisperX model, Hugging Face dependencies, model weights, and proprietary diarization and alignment logic into one inference service. Deploying that container to a GPU-enabled Agent Platform endpoint provides managed autoscaling and high-throughput serving while keeping post-processing in the same request path, which minimizes data transfer and end-to-end latency.

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QUESTION: 215

You work for a social media company. You need to detect whether posted images contain cars. Each training example is a member of exactly one class. You have trained an object detection neural network and deployed the model version to AI Platform Prediction for evaluation. Before deployment, you created an evaluation job and attached it to the AI Platform Prediction model version. You notice that the precision is lower than your business requirements allow. How should you adjust the model’s final layer softmax threshold to increase precision?

Increase the recall.
Decrease the recall.
Increase the number of false positives.
Decrease the number of false negatives.

Answer(s): B

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Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps Professional Machine Learning Engineer Questions 211–215 (page: 44)

QUESTION: 211

QUESTION: 212

QUESTION: 213

Explanation:

QUESTION: 214

Explanation:

QUESTION: 215

Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 211–215 (page: 44)