support@Free-Braindumps.com
Register Login
Cisco
CompTIA
ISC
EC-Council
EXIN
Fortinet
ITIL
Juniper
Microsoft
VMware
Avaya
SAP
Google
NVIDIA
Salesforce
Amazon
Palo Alto Networks
ISC2
CrowdStrike
Anthropic
All Vendors
  1. Home
  2. Exams
  3. Google
  4. PROFESSIONAL MACHINE LEARNING ENGINEER

Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 46–50 (page: 11)

Updated On: 25-Oct-2026
Page 11 of 82
PDF with 402 Questions

You are an ML engineer at a regulated insurance company. You are asked to develop an insurance approval model that accepts or rejects insurance applications from potential customers.
What factors should you consider before building the model?

  1. Redaction, reproducibility, and explainability
  2. Traceability, reproducibility, and explainability
  3. Federated learning, reproducibility, and explainability
  4. Differential privacy, federated learning, and explainability

Answer(s): B



As the lead ML Engineer for your company, you are responsible for building ML models to digitize scanned customer forms. You have developed a TensorFlow model that converts the scanned images into text and stores them in Cloud Storage. You need to use your ML model on the aggregated data collected at the end of each day with minimal manual intervention.
What should you do?

  1. Use the batch prediction functionality of Vertex AI.
  2. Create a serving pipeline in Compute Engine for prediction.
  3. Use Cloud Functions for prediction each time a new data point is ingested.
  4. Deploy the model on Vertex AI and create a version of it for online inference.

Answer(s): A



You are using Gemini Enterprise Agent Platform with TPU v5e (16 GB HBM) Pods to train two different machine learning models at FP16. Model A is a computer vision model with 25 million parameters. Model B is an LLM with 70 billion parameters. You need to configure the distributed training strategy for both models to resolve memory constraints and minimize training latency.
What should you do?

  1. Use pipeline parallelism for Model Use data parallelism for Model B.
  2. Use data parallelism for Model A. Use model parallelism for Model
  3. Use data parallelism for Model A. Use data parallelism with gradient accumulation for Model B.
  4. Use model parallelism for Model A. Use data parallelism for Model B.

Answer(s): B

Explanation:

Data parallelism is appropriate for Model A because its 25 million parameters require approximately 50 MB of memory at FP16 precision, allowing the model to fit comfortably within a TPU v5e chip's 16 GB HBM. Replicating the model across multiple TPU chips enables parallel processing of training batches, increasing throughput and reducing training time.
Model parallelism is appropriate for Model B because its 70 billion parameters require approximately 140 GB of memory for FP16 weights alone, exceeding the memory capacity of a single TPU chip. Distributing model parameters and computations across multiple TPU chips allows the model to fit into their combined memory, including the additional memory required for gradients, optimizer states, and activations. This strategy enables efficient training of the large model while leveraging the high-speed interconnects available in TPU Pods.



You are developing an ML model on Gemini Enterprise Agent Platform that needs to meet specific interpretability requirements for regulatory compliance. You want to use a combination of model architectures and modeling techniques to maximize accuracy and interpretability. How should you create the model?

  1. Use a convolutional neural network (CNN)-based deep learning model architecture, and use local interpretable model-agnostic explanations (LIME) for interpretability.
  2. Use a recurrent neural network (RNN)-based deep learning model architecture, and use integrated gradients for interpretability.
  3. Use a boosted decision tree-based model architecture, and use SHAP values for interpretability.
  4. Use a long short-term memory (LSTM)-based model architecture, and use local interpretable model-agnostic explanations (LIME) for interpretability.

Answer(s): C

Explanation:

Boosted decision tree models (e.g., XGBoost) offer strong predictive performance while being more interpretable than deep learning models. SHAP values provide consistent, theoretically grounded explanations for model predictions, making this combination well-suited for meeting interpretability requirements in regulated environments.



You work at an ecommerce startup. You need to create a customer churn prediction model. Your company’s recent sales records are stored in a BigQuery table. You want to understand how your initial model is making predictions. You also want to iterate on the model as quickly as possible while minimizing cost. How should you build your first model?

  1. Export the data to a Cloud Storage bucket. Load the data into a pandas DataFrame on Vertex AI Workbench and train a logistic regression model with scikit-learn.
  2. Create a tf.data.Dataset by using the TensorFlow BigQueryClient. Implement a deep neural network in TensorFlow.
  3. Prepare the data in BigQuery and associate the data with a Vertex AI dataset. Create an AutoMLTabularTrainingJob to tram a classification model.
  4. Export the data to a Cloud Storage bucket. Create a tf.data.Dataset to read the data from Cloud Storage. Implement a deep neural network in TensorFlow.

Answer(s): C



  PDF
Viewing page 11 of 82
Viewing questions 51 - 55 out of 402 questions


Post your Comments and Discuss Google PROFESSIONAL MACHINE LEARNING ENGINEER exam prep with other Community members:

AI Tutor AI Tutor 👋 I’m here to help!