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The Google Professional-Machine-Learning-Engineer Exam consists of multiple-choice and multiple-select questions, as well as case studies and hands-on labs. Professional-Machine-Learning-Engineer exam duration is two hours, and the passing score is 70%. Professional-Machine-Learning-Engineer exam fee is $200, and it can be taken remotely or at a testing center.
Google Professional Machine Learning Engineer Exam is a certification program that focuses on the skills and knowledge required to design, build, and deploy machine learning models on the Google Cloud Platform. Google Professional Machine Learning Engineer certification is awarded to individuals who have demonstrated their proficiency in developing and implementing machine learning solutions on the Google Cloud Platform.
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The Google Professional-Machine-Learning-Engineer Exam covers a wide range of topics related to machine learning, including data preparation, model design and implementation, model training and evaluation, and deployment and monitoring of machine learning models. Successful candidates will be able to demonstrate their ability to design and implement machine learning models using Google Cloud Platform tools and services, as well as their ability to optimize performance and ensure reliability and scalability of machine learning systems. Google Professional Machine Learning Engineer certification is recognized as a valuable credential for professionals working in the field of machine learning, and it can help to enhance career opportunities and earning potential.
NEW QUESTION # 283
You recently used BigQuery ML to train an AutoML regression model. You shared results with your team and received positive feedback. You need to deploy your model for online prediction as quickly as possible. What should you do?
Answer: D
NEW QUESTION # 284
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?
Answer: D
NEW QUESTION # 285
You are training an LSTM-based model on AI Platform to summarize text using the following job submission script:
gcloud ai-platform jobs submit training $JOB_NAME \
--package-path $TRAINER_PACKAGE_PATH \
--module-name $MAIN_TRAINER_MODULE \
--job-dir $JOB_DIR \
--region $REGION \
--scale-tier basic \
-- \
--epochs 20 \
--batch_size=32 \
--learning_rate=0.001 \
You want to ensure that training time is minimized without significantly compromising the accuracy of your model. What should you do?
Answer: B
Explanation:
Changing the scale tier does not impact performance-only speeds up training time. Epochs, Batch size, and learning rate all are hyperparameters that might impact model accuracy.
NEW QUESTION # 286
You work for a company that is developing a new video streaming platform. You have been asked to create a recommendation system that will suggest the next video for a user to watch. After a review by an AI Ethics team, you are approved to start development. Each video asset in your company's catalog has useful metadata (e.g., content type, release date, country), but you do not have any historical user event dat a. How should you build the recommendation system for the first version of the product?
Answer: D
Explanation:
The best option for building a recommendation system without any user event data is to use simple heuristics based on content metadata. This is a type of content-based filtering, which recommends items that are similar to the ones that the user has interacted with or selected, based on their attributes. For example, if a user selects a comedy movie from the US released in 2020, the system can recommend other comedy movies from the US released in 2020 or nearby years. This approach does not require any machine learning, but it can leverage the existing metadata of the videos to provide relevant recommendations. It also allows the system to start collecting user event data, such as views, likes, ratings, etc., which can be used to train a more sophisticated machine learning model in the future, such as a collaborative filtering model or a hybrid model that combines content and collaborative information. Reference:
Recommendation Systems
Content-Based Filtering
Collaborative Filtering
Hybrid Recommender Systems: A Systematic Literature Review
NEW QUESTION # 287
You are an ML engineer at an ecommerce company and have been tasked with building a model that predicts how much inventory the logistics team should order each month. Which approach should you take?
Answer: A
Explanation:
The best approach to build a model that predicts how much inventory the logistics team should order each month is to use a time series forecasting model to predict each item's monthly sales. This approach can capture the temporal patterns and trends in the sales data, such as seasonality, cyclicality, and autocorrelation. It can also account for the variability and uncertainty in the demand, and provide confidence intervals and error metrics for the predictions. By using a time series forecasting model, you can provide the logistics team with accurate and reliable estimates of the future sales for each item, which can help them optimize the inventory levels and avoid overstocking or understocking. You can use various methods and tools to build a time series forecasting model, such as ARIMA, LSTM, Prophet, or BigQuery ML.
The other options are not optimal for the following reasons:
* A. Using a clustering algorithm to group popular items together is not a good approach, as it does not provide any quantitative or temporal information about the sales or the inventory. It only provides a qualitative and static categorization of the items based on their similarity or dissimilarity. Moreover, clustering is an unsupervised learning technique, which does not use any target variable or feedback to guide the learning process. This can result in arbitrary and inconsistent clusters, which may not reflect the true demand or preferences of the customers.
* B. Using a regression model to predict how much additional inventory should be purchased each month is not a good approach, as it does not account for the individual differences and dynamics of each item.
It only provides a single aggregated value for the whole inventory, which can be misleading and inaccurate. Moreover, a regression model is not well-suited for handling time series data, as it assumes that the data points are independent and identically distributed, which is not the case for sales data. A regression model can also suffer from overfitting or underfitting, depending on the choice and complexity of the features and the model.
* D. Using a classification model to classify inventory levels as UNDER_STOCKED, OVER_STOCKED, and CORRECTLY_STOCKED is not a good approach, as it does not provide any numerical or predictive information about the sales or the inventory. It only provides a discrete and subjective label for the inventory levels, which can be vague and ambiguous. Moreover, a classification model is not well-suited for handling time series data, as it assumes that the data points are independent and identically distributed, which is not the case for sales data. A classification model can also suffer from class imbalance, misclassification, or overfitting, depending on the choice and complexity of the features, the model, and the threshold.
References:
* Professional ML Engineer Exam Guide
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
* Google Cloud launches machine learning engineer certification
* Time Series Forecasting: Principles and Practice
* BigQuery ML: Time series analysis
NEW QUESTION # 288
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