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To be eligible for the exam, candidates must have experience in developing and deploying machine learning models using Google Cloud Platform. They should also have experience with programming languages such as Python and SQL, and knowledge of machine learning concepts such as supervised and unsupervised learning, reinforcement learning, and deep learning.

Google Professional Machine Learning Engineer Certification Exam is a challenging exam that requires extensive preparation and study. Professional-Machine-Learning-Engineer Exam consists of multiple-choice questions, coding exercises, and scenario-based questions. Professional-Machine-Learning-Engineer exam is designed to test the practical knowledge and skills of candidates in real-world scenarios. Candidates who pass the exam demonstrate their expertise in the field of machine learning and are recognized as certified professionals by Google Cloud.

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Google Professional Machine Learning Engineer exam is a certification test that is designed to validate the skills and knowledge of individuals in the field of machine learning. Professional-Machine-Learning-Engineer Exam is intended for individuals who have a strong understanding of machine learning concepts, including supervised learning, unsupervised learning, and deep learning. Additionally, this certification exam assesses an individual's ability to design and implement machine learning models on the Google Cloud Platform.

Google Professional Machine Learning Engineer Sample Questions (Q347-Q352):

NEW QUESTION # 347
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 the Cloud Data Loss Prevention (DLP) API to de-identify the PI! before performing data exploration and preprocessing.
B. Use a VM inside a VPC Service Controls security perimeter to perform data exploration and preprocessing.
C. Use customer-managed encryption keys (CMEK) to encrypt the Pll data at rest and decrypt the Pll data during data exploration and preprocessing.
D. Use Google-managed encryption keys to encrypt the Pll data at rest, and decrypt the Pll data during data exploration and preprocessing.

Answer: A

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 and preprocessing, 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 # 348
You are building a model to predict daily temperatures. You split the data randomly and then transformed the training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model performed with 97% accuracy; however, after deploying to production, the model ' s accuracy dropped to
66%. How can you make your production model more accurate?

A. Split the training and test data based on time rather than a random split to avoid leakage
B. Normalize the data for the training, and test datasets as two separate steps.
C. Add more data to your test set to ensure that you have a fair distribution and sample for testing
D. Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.

Answer: A

Explanation:
When building a model to predict daily temperatures, it is important to split the training and test data based on time rather than a random split. This is because temperature data is likely to have temporal dependencies and patterns, such as seasonality, trends, and cycles. If the data is split randomly, there is a risk of data leakage, which occurs when information from the future is used to train or validate the model. Data leakage can lead to overfitting and unrealistic performance estimates, as the model may learn from data that it should not have access to. By splitting the data based on time, such as using the most recent data as the test set and the older data as the training set, the model can be evaluated on how well it can forecast future temperatures based on past data, which is the realistic scenario in production. Therefore, splitting the data based on time rather than a random split is the best way to make the production model more accurate.

NEW QUESTION # 349
You work for a company that is developing an application to help users with meal planning You want to use machine learning to scan a corpus of recipes and extract each ingredient (e g carrot, rice pasta) and each kitchen cookware (e.g. bowl, pot spoon) mentioned Each recipe is saved in an unstructured text file What should you do?

A. Create a text dataset on Vertex Al for entity extraction Create as many entities as there are different ingredients and cookware Train an AutoML entity extraction model to extract those entities Evaluate the models performance on a holdout dataset.
B. Create a text dataset on Vertex Al for entity extraction Create two entities called ingredient" and cookware" and label at least 200 examples of each entity Train an AutoML entity extraction model to extract occurrences of these entity types Evaluate performance on a holdout dataset.
C. Use the Entity Analysis method of the Natural Language API to extract the ingredients and cookware from each recipe Evaluate the model's performance on a prelabeled dataset.
D. Create a multi-label text classification dataset on Vertex Al Create a test dataset and label each recipe that corresponds to its ingredients and cookware Train a multi-class classification model Evaluate the model's performance on a holdout dataset.

Answer: B

Explanation:
Entity extraction is a natural language processing (NLP) task that involves identifying and extracting specific types of information from text, such as names, dates, locations, etc. Entity extraction can help you analyze a corpus of recipes and extract each ingredient and cookware mentioned in them. Vertex AI is a unified platform for building and managing machine learning solutions on Google Cloud. It provides a service for AutoML entity extraction, which allows you to create and train custom entity extraction models without writing any code. You can use Vertex AI to create a text dataset for entity extraction, and label your data with two entities: "ingredient" and "cookware". You need to label at least 200 examples of each entity type to train an AutoML entity extraction model. You can also use a holdout dataset to evaluate the performance of your model, such as precision, recall, and F1-score. This solution can help you build a machine learning model to scan a corpus of recipes and extract each ingredient and cookware mentioned in them, and use the results to help users with meal planning. References:
* AutoML Entity Extraction | Vertex AI
* Preparing data for AutoML Entity Extraction | Vertex AI

NEW QUESTION # 350
You are developing ML models with Al Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?

A. Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.
B. Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job
C. Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository
D. Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code

Answer: C

Explanation:
CI/CD for Kubeflow pipelines. At the heart of this architecture is Cloud Build, infrastructure. Cloud Build can import source from Cloud Source Repositories, GitHub, or Bitbucket, and then execute a build to your specifications, and produce artifacts such as Docker containers or Python tar files.
https://cloud.google.com/architecture/architecture-for-mlops-using-tfx-kubeflow-pipelines-and-cloud-build#cicd_architecture

NEW QUESTION # 351
You work as an ML engineer at a social media company, and you are developing a visual filter for users' profile photos. This requires you to train an ML model to detect bounding boxes around human faces. You want to use this filter in your company's iOS-based mobile phone application. You want to minimize code development and want the model to be optimized for inference on mobile phones. What should you do?

A. Train a custom TensorFlow model and convert it to TensorFlow Lite (TFLite).
B. Train a model using AutoML Vision and use the "export for Core ML" option.
C. Train a model using AutoML Vision and use the "export for TensorFlow.js" option.
D. Train a model using AutoML Vision and use the "export for Coral" option.

Answer: B

NEW QUESTION # 352
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