Free Practice Questions for Google Professional-Machine-Learning-Engineer Exam

The best option for handling missing values in a categorical feature is to replace them with a placeholder category indicating a missing value. This is a type of imputation, which is a method of estimating the missing values based on the observed data. Imputing the missing values with a placeholder category preserves the information that the data is missing, and avoids introducing bias or distortion in the feature distribution. It also allows the machine learning model to learn from the missingness pattern, and potentially use it as a predictor for the target variable. The other options are not suitable for handling missing values in a categorical feature, because:

Removing the rows with missing values and upsampling the dataset by 5% would reduce the size of the dataset and potentially lose important information. It would also introduce sampling bias and overfitting, as the upsampling process would create duplicate or synthetic observations that do not reflect the true population.

Replacing the missing values with the feature’s mean would not make sense for a categorical feature, as the mean is a numerical measure that does not capture the mode or frequency of the categories. It would also create a new category that does not exist in the original data, and might confuse the machine learning model.

Moving the rows with missing values to the validation dataset would compromise the validity and reliability of the model evaluation, as the validation dataset would not be representative of the test or production data. It would also reduce the amount of data available for training the model, and might introduce leakage or inconsistency between the training and validation datasets.  References :

Imputation of missing values

Effective Strategies to Handle Missing Values in Data Analysis

How to Handle Missing Values of Categorical Variables?

Google Cloud launches machine learning engineer certification

Google Professional Machine L earning Engineer Certification

Professional ML Engineer Exam Guide

Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

TFRecord is a binary file format that stores your data as a sequence of binary strings 1 .  TFRecord files are efficient, scalable, and easy to process 1 .  Sharding is a technique that splits a large file into smaller files, which can improve parallelism and perfo rmance 2 .  Dataflow is a service that allows you to create and run data processing pipelines on G oogle Cloud 3 .  Dataflow can create sharded TFRecord files from your images in a Cloud Storage directory 4 .

tf.data.TFRecordDataset is a class that allows you to read and parse TFRecord files in TensorFlow. You can use this class to create a tf.data.Dataset object that represents your input data for training. tf.data.Dataset is a high-level API that provides various methods to transform, batch, shuffle, and prefetch your data.

Vertex AI Training is a service that allows you to train your custom models on Google Cloud using various hardware accelerators, such as GPUs. Vertex AI Training supports TensorFlow models and can read data from Cloud Storage. You can use Vertex AI Training to train your image classification model by using a V100 GPU, which is a powerful and fast GPU for deep learning.

 According to the official exam guide 1 , one of the skills assessed in the exam is to “automate and orchestrate ML pipelines using Cloud Composer”.  Cloud Composer 2  is a fully managed workflow orchestration service that uses Apache Airflow to create, schedule, monitor, and manage workflows. Cloud Composer allows you to build custom training pipelines for deep learning-based models and integrate them with other Google Cloud services.  You can also use Cloud Composer to implement model inte rpretability techniques, such as feature attributions, explainable AI, or model debugging 3 . The other options are not relevant or optimal for this scenario.  References :

Professional ML Engineer Exam Guide

Cloud Composer

Model interpretability with Cloud Composer

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

According to the official exam guide 1 , one of the skills assessed in the exam is to “design, build, and productionalize ML models to solve business challenges using Google Cloud technologies”.  The Vertex AI Vision Occupancy Analytics model 2  is a specialized pre-built vision model that lets you count people or vehicles given specific inputs you add in video frames. It provides advanced features such as active zones counting, line crossing counting, and dwelling detection. This model is suitable for the use case of detecting the number of customers waiting for service in near real time.  You can easily create and deploy an occupancy analytics application using Vertex AI Vision 3 . The other options are not relevant or optimal for this scenario.  References :

Professional ML Engineer Exam Guide

Occupancy analytics guide

Create an occupancy analytics app with BigQuery forecasting

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

 The best option for splitting the data between the training, validation, and test sets, using a managed tabular dataset in Vertex AI that contains sales data from three different stores, is to use Vertex AI default data split. This option allows you to leverage the power and simplicity of Vertex AI to automatically and randomly split your data into the three sets by percentage. Vertex AI is a unified platform for building and deploying machine learning solutions on Google Cloud. Vertex AI can support various types of models, such as linear regression, logistic regression, k-means clustering, matrix factorization, and deep neural networks. Vertex AI can also provide various tools and services for data analysis, model development, model deployment, model monitoring, and model governance. A default data split is a data split method that is provided by Vertex AI, and does not require any user input or configuration. A default data split can help you split your data into the training, validation, and test sets by using a random sampling method, and assign a fixed percentage of the data to each set. A default data split can help you simplify the data split process, and works well in most cases. A training set is a subset of the data that is used to train the model, and adjust the model parameters. A training set can help you learn the relationship between the input features and the target variable, and optimize the model performance. A validation set is a subset of the data that is used to validate the model, and tune the model hyperparameters. A validation set can help you evaluate the model performance on unseen data, and avoid overfitting or underfitting. A test set is a subset of the data that is used to test the model, and provide the final evaluation metrics. A test set can help you assess the model performance on new data, and measure the generalization ability of the model.  By using Vertex AI default data split, you can split your data into the training, validation, and test sets by using a random sampling method, and assign the following percentages of the data to each set 1 :

The other options are not as good as option B, for the following reasons:

Option A: Using Vertex AI manual split, using the store name feature to assign one store for each set would not allow you to split your data into representative and balanced sets, and could cause errors or poor performance. A manual split is a data split method that allows you to control how your data is split into sets, by using the ml_use label or the data filter expression. A manual split can help you customize the data split logic, and handle complex or non-standard data formats. A store name feature is a feature that indicates the name of the store where the sales data was collected. A store name feature can help you identify the source of the data, and group the data by store. However, using Vertex AI manual split, using the store name feature to assign one store for each set would not allow you to split your data into representative and balanced sets, and could cause errors or poor performance. You would need to write code, create and configure the ml_use label or the data filter expression, and assign one store for each set.  Moreover, this option would not ensure that the data in each set has the same distribution and characteristics as the data in the whole dataset, which could prevent you from learning the general pattern o f the data, and cause bias or variance in the model 2 .

Option C: Using Vertex AI chronological split and specifying the sales timestamp feature as the time variable would not allow you to split your data into representative and balanced sets, and could cause errors or poor performance. A chronological split is a data split method that allows you to split your data into sets based on the order of the data. A chronological split can help you preserve the temporal dependency and sequence of the data, and avoid data leakage. A sales timestamp feature is a feature that indicates the date and time when the sales data was collected. A sales timestamp feature can help you track the changes and trends of the data over time, and capture the seasonality and cyclicality of the data. However, using Vertex AI chronological split and specifying the sales timestamp feature as the time variable would not allow you to split your data into representative and balanced sets, and could cause errors or poor performance. You would need to write code, create and configure the time variable, and split the data by the order of the time variable.  Moreover, this option would not ensure that the data in each set has the same distribution and characteristics as the data in the whole dataset, which could prevent you from learning the general pattern of the data, and cause bias or variance in the model 3 .

Option D: Using Vertex AI random split, assigning 70% of the rows to the training set, 10% to the validation set, and 20% to the test set would not allow you to use the default data split method that is provided by Vertex AI, and could increase the complexity and cost of the data split process. A random split is a data split method that allows you to split your data into sets by using a random sampling method, and assign a custom percentage of the data to each set. A random split can help you split your data into representative and balanced sets, and avoid data leakage. However, using Vertex AI random split, assigning 70% of the rows to the training set, 10% to the validation set, and 20% to the test set would not allow you to use the default data split method that is provided by Vertex AI, and could increase the complexity and cost of the data split process. You would need to write code, create and configure the random split method, and assign the custom percentages to each set.  Moreover, this option would not use the default data split method that is provided by Vertex AI, which can simplify the data split process, and works well in most cases 1 .

One-hot encoding is a technique that converts categorical variables into numerical variables by creating dummy variables for each possible category.  Each dummy variable has a value of 1 if the original variable belongs to that category, and 0 otherwise 1 .  One-hot encoding can help linear regression models to capture the effect of different categories on the targe t variable without imposing any ordinal relationship among them 2 . Dataprep is a service that allows you to explore, clean, and transform your data for analysis and machine learning.  You can use Dataprep to apply one-hot encoding to your city name variable and make each city a column with binary values 3 . This way, you can prepare your data using the least amount of coding while maintaining the predictive variables. Therefore, using Dataprep to transform the state column using a one-hot encoding method is the best option for this use case.

Dynamic range quantization is a model optimization technique for reducing latency that reduces the numerical precision of the weights and activations of models. This technique can reduce the model size, memory usage, and inference time by up to 4x with negligible accuracy loss. Dynamic range quantization can be applied to a trained TensorFlow model without retraining, and it is suitable for mobile applications that require low latency and power consumption.

Weight pruning, model distillation, and dimensionality reduction are also model optimization techniques for reducing latency, but they have some limitations or drawbacks compared to dynamic range quantization:

Weight pruning works by removing parameters within a model that have only a minor impact on its predictions. Pruned models are the same size on disk, and have the same runtime latency, but can be compressed more effectively. This makes pruning a useful technique for reducing model download size, but not for reducing inference time.

Model distillation works by training a smaller and simpler model (student) to mimic the behavior of a larger and complex model (teacher). Distilled models can have lower latency and memory usage than the original models, but they require retraining and may not preserve the accuracy of the teacher model.

Dimensionality reduction works by reducing the number of features or dimensions in the input data or the model layers. Dimensionality reduction can improve the computational efficiency and generalization ability of models, but it may also lose some information or intro duce noise in the data or the model. Dimensionality reduction also requires retraining or modifying the model architecture.

The best option for deploying a scikit-learn classification model to production is to deploy an online Vertex AI prediction endpoint and set the max replica count to 100. This option allows you to leverage the power and scalability of Google Cloud to serve requests 24/7 and handle millions of requests per second. Vertex AI is a unified platform for building and deploying machine learning solutions on Google Cloud. Vertex AI can deploy a trained scikit-learn model to an online prediction endpoint, which can provide low-latency predictions for individual instances. An online prediction endpoint consists of one or more replicas, which are copies of the model that run on virtual machines. The max replica count is a parameter that determines the maximum number of replicas that can be created for the endpoint. By setting the max replica count to 100, you can enable the endpoint to scale up to 100 replicas when the traffic increases, and scale down to zero replicas when the traffic decreases. This can help minimize the cost of deployment, as you only pay for the resources that you use.  Moreover, you can use the autoscaling algorithm option to optimize the scaling behavior of the endpoint based on the latency and utilization metrics 1 .

The other options are not as good as option B, for the following reasons:

Option A: Deploying an online Vertex AI prediction endpoint and setting the max replica count to 1 would not be able to serve requests 24/7 and handle millions of requests per second. Setting the max replica count to 1 would limit the endpoint to only one replica, which can cause performance issues and service disruptions when the traffic increases.  Moreover, se tting the max replica count to 1 would prevent the endpoint from scaling down to zero replicas when the traffic decreases, which can increase the cost of deployment, as you pay for the resources that you do not use 1 .

Option C: Deploying an online Vertex AI prediction endpoint with one GPU per replica and setting the max replica count to 1 would not be able to serve requests 24/7 and handle millions of requests per second, and would increase the cost of deployment. Adding a GPU to each replica would increase the computational power of the endpoint, but it would also in crease the cost of deployment, as GPUs are more expensive than CPUs.  Moreover, setting the max replica count to 1 would limit the endpoint to only one replica, which can cause performance issues and service disruptions whe n the traffic increases, and prevent the endpoint from scaling down to zero replicas when the traffic decreases 1 .  Furthermore, scikit-learn models do not benefit from GPUs, as scikit-learn is not optimized for GPU acceleration 2 .

Option D: Deploying an online Vertex AI prediction endpoint with one GPU per replica and setting the max replica count to 100 would be able to serve requests 24/7 and handle millions of requests per second, but it would increase the cost of deployment. Adding a GPU to each replica would increase the computational power of the endpoint, but it would also increase the cost of deployment, as GPUs are more expensive than CPUs. Setting the max replica count to 100 would enable the endpoint to scale up to 100 replicas when the traffic increases, and scale down to zero replicas when the traffic decreases, which can help minimize the cost of deployment.  However , scikit-learn models do not benefit from GPUs, as scikit-learn is not optimized for GPU acceleration 2 . Therefore, using GPUs for scikit-learn models would be unnecessary and wasteful.