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Interactive Professional-Machine-Learning-Engineer Practice Exam | Interactive Professional-Machine-Learning-Engineer Questions
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Obtaining the Google Professional Machine Learning Engineer certification can be a significant advantage for individuals seeking employment opportunities in the field of machine learning. Google Professional Machine Learning Engineer certification demonstrates to employers that an individual has a strong understanding of machine learning concepts and is capable of designing and implementing machine learning models on the Google Cloud Platform. Additionally, this certification can lead to higher salaries, increased job opportunities, and advancement within an organization.
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Google Professional Machine Learning Engineer Sample Questions (Q160-Q165):
NEW QUESTION # 160
You work for a rapidly growing social media company. Your team builds TensorFlow recommender models in an on-premises CPU cluster. The data contains billions of historical user events and 100 000 categorical features. You notice that as the data increases the model training time increases. You plan to move the models to Google Cloud You want to use the most scalable approach that also minimizes training time. What should you do?
- A. Deploy the training jobs by using TPU VMs with TPUv3 Pod slices, and use the TPUEmbedding API.
- B. Deploy the training jobs in an autoscaling Google Kubernetes Engine cluster with CPUs
- C. Deploy the training jobs by using Compute Engine instances with A100 GPUs and use the t f. nn. embedding_lookup API.
- D. Deploy a matrix factorization model training job by using BigQuery ML.
Answer: A
Explanation:
TPU VMs with TPUv3 Pod slices are the most scalable and performant option for training large-scale recommender models on Google Cloud. TPUv3 Pods can provide up to 2048 cores and 32 TB of memory, and can process billions of examples and features in minutes. The TPUEmbedding API is designed to efficiently handle large-scale categorical features and embeddings, and can reduce the memory footprint and communication overhead of the model. The other options are either less scalable (B and C) or less efficient (D) for this use case.
NEW QUESTION # 161
You need to design a customized deep neural network in Keras that will predict customer purchases based on their purchase history. You want to explore model performance using multiple model architectures, store training data, and be able to compare the evaluation metrics in the same dashboard. What should you do?
- A. Create an experiment in Kubeflow Pipelines to organize multiple runs
- B. Automate multiple training runs using Cloud Composer
- C. Create multiple models using AutoML Tables
- D. Run multiple training jobs on Al Platform with similar job names
Answer: A
Explanation:
https://www.kubeflow.org/docs/components/pipelines/concepts/experiment/ https://www.kubeflow.org/docs/components/pipelines/concepts/run/
NEW QUESTION # 162
You are analyzing customer data for a healthcare organization that is stored in Cloud Storage. The data contains personally identifiable information (PII) You need to perform data exploration and preprocessing while ensuring the security and privacy of sensitive fields What should you do?
- A. Use Google-managed encryption keys to encrypt the Pll data at rest, and decrypt the Pll data during data exploration and preprocessing.
- B. Use the Cloud Data Loss Prevention (DLP) API to de-identify the PI! before performing data exploration and preprocessing.
- C. Use a VM inside a VPC Service Controls security perimeter to perform data exploration and preprocessing.
- D. Use customer-managed encryption keys (CMEK) to encrypt the Pll data at rest and decrypt the Pll data during data exploration and preprocessing.
Answer: B
Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to "design, build, and productionalize ML models to solve business challenges using Google Cloud technologies". Cloud Data Loss Prevention (DLP) API2 is a service that provides programmatic access to a powerful detection engine for personally identifiable information and other privacy-sensitive data in unstructured data streams, such as text blocks and images. Cloud DLP API helps you discover, classify, and protect your sensitive data by using techniques such as de-identification, masking, tokenization, and bucketing. You can use Cloud DLP API to de-identify the PII data before performing data exploration andpreprocessing, and retain the data utility for ML purposes. Therefore, option A is the best way to perform data exploration and preprocessing while ensuring the security and privacy of sensitive fields. The other options are not relevant or optimal for this scenario.
References:
* Professional ML Engineer Exam Guide
* Cloud Data Loss Prevention (DLP) API
* Google Professional Machine Learning Certification Exam 2023
* Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
NEW QUESTION # 163
You recently trained a XGBoost model that you plan to deploy to production for online inference Before sending a predict request to your model's binary you need to perform a simple data preprocessing step This step exposes a REST API that accepts requests in your internal VPC Service Controls and returns predictions You want to configure this preprocessing step while minimizing cost and effort What should you do?
- A. Build a custom predictor class based on XGBoost Predictor from the Vertex Al SDK. package it and a pickled model in a custom container image based on a Vertex built-in image, and deploy the model to Vertex Al Endpoints.
- B. Build a Flask-based app. package the app and a pickled model in a custom container image, and deploy the model to Vertex Al Endpoints.
- C. Store a pickled model in Cloud Storage Build a Flask-based app packages the app in a custom container image, and deploy the model to Vertex Al Endpoints.
- D. Build a custom predictor class based on XGBoost Predictor from the Vertex Al SDK and package the handler in a custom container image based on a Vertex built-in container image Store a pickled model in Cloud Storage and deploy the model to Vertex Al Endpoints.
Answer: D
Explanation:
* Option A is not the best answer because it requires storing the pickled model in Cloud Storage, which may incur additional cost and latency for loading the model. It also requires building a Flask-based app, which may not be necessary for a simple data preprocessing step.
* Option B is not the best answer because it requires building a Flask-based app, which may not be necessary for a simple data preprocessing step. It also requires packaging the app andthe pickled model
* in a custom container image, which may increase the size and complexity of the image.
* Option C is not the best answer because it requires packaging the pickled model in a custom container image, which may increase the size and complexity of the image. It also does not leverage the Vertex built-in container image, which may provide some optimizations and integrations for XGBoost models.
* Option D is the best answer because it leverages the Vertex built-in container image, which may provide some optimizations and integrations for XGBoost models. It also allows storing the pickled model in Cloud Storage, which may reduce the size and complexity of the image. It also allows building a custom predictor class based on XGBoost Predictor from the Vertex AI SDK, which may simplify the data preprocessing step and the prediction logic.
NEW QUESTION # 164
You lead a data science team at a large international corporation. Most of the models your team trains are large-scale models using high-level TensorFlow APIs on AI Platform with GPUs. Your team usually takes a few weeks or months to iterate on a new version of a model. You were recently asked to review your team's spending. How should you reduce your Google Cloud compute costs without impacting the model's performance?
- A. Use AI Platform to run distributed training jobs without checkpoints.
- B. Migrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs without checkpoints.
- C. Use AI Platform to run distributed training jobs with checkpoints.
- D. Migrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs with checkpoints.
Answer: B
NEW QUESTION # 165
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