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The Google Professional Machine Learning Engineer certification exam covers various topics related to machine learning, such as data preprocessing, feature engineering, model selection, hyperparameter tuning, and deployment. Professionals who pass the exam demonstrate their ability to design and develop machine learning models that meet specific business requirements. Google Professional Machine Learning Engineer certification exam also covers various machine learning techniques such as deep learning, supervised and unsupervised learning, and reinforcement learning.

To obtain the Google Professional Machine Learning Engineer certification, candidates must pass a 2-hour exam that consists of multiple-choice and scenario-based questions. Professional-Machine-Learning-Engineer Exam evaluates the candidates on their ability to design and develop scalable, efficient, and secure machine learning models using Google Cloud Platform. Google Professional Machine Learning Engineer certification is recognized globally and is highly valued by employers as it demonstrates a professional's expertise and ability to work with cutting-edge technologies in the field of machine learning. Google Professional Machine Learning Engineer certification also provides access to Google's resources and community of machine learning professionals, making it a valuable asset for anyone looking to advance their career in this field.

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Google Professional Machine Learning Engineer certification exam is a comprehensive exam that covers a wide range of topics related to machine learning. Professional-Machine-Learning-Engineer exam is designed to test the knowledge and skills of professionals in areas such as data preprocessing, model training, model tuning, model deployment, and monitoring. Professional-Machine-Learning-Engineer Exam also covers topics such as machine learning frameworks, data analysis, and data visualization.

Google Professional Machine Learning Engineer Sample Questions (Q47-Q52):

NEW QUESTION # 47
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 model using AutoML Vision and use the "export for TensorFlow.js" option.
B. Train a model using AutoML Vision and use the "export for Core ML" option.
C. Train a custom TensorFlow model and convert it to TensorFlow Lite (TFLite).
D. Train a model using AutoML Vision and use the "export for Coral" option.

Answer: B

NEW QUESTION # 48
You were asked to investigate failures of a production line component based on sensor readings.
After receiving the dataset, you discover that less than 1% of the readings are positive examples representing failure incidents.
You have tried to train several classification models, but none of them converge. How should you resolve the class imbalance problem?

A. Use a convolutional neural network with max pooling and softmax activation.
B. Use the class distribution to generate 10% positive examples.
C. Downsample the data with upweighting to create a sample with 10% positive examples.
D. Remove negative examples until the numbers of positive and negative examples are equal.

Answer: C

Explanation:
- less than 1% of the readings are positive
- none of them converge.
Downsampling (in this context) means training on a disproportionately low subset of the majority class examples.
https://developers.google.com/machine-learning/data-prep/construct/sampling-splitting/imbalanced-data#downsampling-and-upweighting

NEW QUESTION # 49
A Machine Learning Specialist is developing a custom video recommendation model for an application. The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket.
The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance.
Which approach allows the Specialist to use all the data to train the model?

A. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.
B. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to the instance. Train on a small amount of the data to verify the training code and hyperparameters. Go back to Amazon SageMaker and train using the full dataset
C. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
D. Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.

Answer: C

NEW QUESTION # 50
You are developing a demand forecasting model for a large online retailer. The company has
20,000 SKUs that are sold through a single distribution center. You have three years of historical data. You need to provide a daily forecast at the SKU level for the next two weeks. You need to develop the first version quickly and minimize the development effort while delivering high forecasting accuracy. What should you do?

A. Write a Python script in Agent Platform Workbench that trains a BigQuery ML ARIMA model for each SKU, and use a corresponding model for each SKU forecast.
B. For each SKU, send the last month's history of sales and the SKU's description to Gemini Pro to generate a prediction for the next two weeks. Instruct the LLM to act as a category specialist in retail and to focus on accuracy.
C. Train a multivariate BigQuery ML ARIMA_PLUS_XREG model on all SKUs by using the TIME_SERIES_ID_COL option.
D. Fine-tune the Gemini Pro model on the historical data to predict the daily sales for each SKU. Use the fine-tuned model without prompt engineering.

Answer: C

Explanation:
BigQuery ML can train and manage separate time-series forecasts for all 20,000 SKUs in a single SQL model by using the SKU identifier as TIME_SERIES_ID_COL. ARIMA_PLUS_XREG automates model selection, trend and seasonality handling, and supports additional explanatory variables, providing a scalable, accurate solution with minimal development effort.
Reference:
https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-multivariate-time-series
https://docs.cloud.google.com/bigquery/docs/arima-plus-xreg-multiple-time-series-forecasting-tutorial

NEW QUESTION # 51
You are working on a binary classification ML algorithm that detects whether an image of a classified scanned document contains a company's logo. In the dataset, 96% of examples don't have the logo, so the dataset is very skewed. Which metrics would give you the most confidence in your model?

A. F1 score
B. F-score where recall is weighed more than precision
C. F-score where precision is weighed more than recall
D. RMSE

Answer: B

Explanation:
* Option A is correct because using F-score where recall is weighed more than precision is a suitable metric for binary classification with imbalanced data. F-score is a harmonic mean of precision and recall, which are two metrics that measure the accuracy and completeness of the positive class1. Precision is the fraction of true positives among all predicted positives, while recall is the fraction of true positives among all actual positives1. When the data is imbalanced, the positive class is the minority class, which is usually the class of interest. For example, in this case, the positive class is the images that contain the company's logo, which are rare but important to detect. By weighing recall more than precision, we can emphasize the importance of finding all the positive examples, even if some false positives are included2.
* Option B is incorrect because using RMSE (root mean squared error) is not a valid metric for binary classification with imbalanced data. RMSE is a metric that measures the average magnitude of the errors between the predicted and actual values3. RMSE is suitable for regression problems, where the target variable is continuous, not for classification problems, where the target variable is discrete4.
* Option C is incorrect because using F1 score is not the best metric for binary classification with imbalanced data. F1 score is a special case of F-score where precision and recall are equally weighted1. F1 score is suitable for balanced data, where the positive and negative classes are equally important and frequent5. However, for imbalanced data, the positive class is more important and less frequent than the negative class, so F1 score may not reflect the performance of the model well2.
* Option D is incorrect because using F-score where precision is weighed more than recall is not a good metric for binary classification with imbalanced data. By weighing precision more than recall, we can
* emphasize the importance of minimizing the false positives, even if some true positives are missed2. However, for imbalanced data, the true positives are more important and less frequent than the false positives, so this metric may not reflect the performance of the model well2.
References:
* Precision, recall, and F-measure
* F-score for imbalanced data
* RMSE
* Regression vs classification
* F1 score
* [Imbalanced classification]
* [Binary classification]

NEW QUESTION # 52
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