GOOGLE PROFESSIONAL DATA ENGINEER
COMPREHENSIVE TEST PAPER 2026 FULL
SOLUTION STUDY GUIDE
● Hyperparameters. Answer: Values that configure a machine learning
model
● Model Training. Answer: The process of using training data to create
a model that can be used to make predictions
● Feature Selection. Answer: The process of evaluating how a particular
attribute or feature contributes to the predictiveness of a model
● Regularization. Answer: The process used to compensate for the
impact of noise in training data and reduce the risk of overfitting by
introducing a penalty for data points that make a model more
complicated
● Validation Dataset. Answer: Dataset that is used to tune
hyperparameters
● Underfitting. Answer: Occurs when a model is unable to predict
values of training data correctly
, ● Overfitting. Answer: Occurs when a model fits the training data too
well due to noise in the data
● L1 Regularization (lasso). Answer: Type of regularization where the
penalty is based on the magnitude or absolute value of coefficients in a
model
● L2 Regularization (ridge regression). Answer: Type of regularization
where the penalty is based on the square of the magnitude of the
coefficient
● Tensor Processing Units (TPUs). Answer: Specialized accelerators
based on ASICs and created by Google to improve the training of deep
neural networks
● Von Neumann Bottleneck. Answer: The limited data rate between a
processor and memory
● TensorFlow. Answer: An open source deep learning framework
created by Google that allows developers to specify a distributed
computing strategy when training a model
● MirroredStrategy. Answer: A TensorFlow strategy that supports
synchronous distributed training on GPUs attached to a single machine
COMPREHENSIVE TEST PAPER 2026 FULL
SOLUTION STUDY GUIDE
● Hyperparameters. Answer: Values that configure a machine learning
model
● Model Training. Answer: The process of using training data to create
a model that can be used to make predictions
● Feature Selection. Answer: The process of evaluating how a particular
attribute or feature contributes to the predictiveness of a model
● Regularization. Answer: The process used to compensate for the
impact of noise in training data and reduce the risk of overfitting by
introducing a penalty for data points that make a model more
complicated
● Validation Dataset. Answer: Dataset that is used to tune
hyperparameters
● Underfitting. Answer: Occurs when a model is unable to predict
values of training data correctly
, ● Overfitting. Answer: Occurs when a model fits the training data too
well due to noise in the data
● L1 Regularization (lasso). Answer: Type of regularization where the
penalty is based on the magnitude or absolute value of coefficients in a
model
● L2 Regularization (ridge regression). Answer: Type of regularization
where the penalty is based on the square of the magnitude of the
coefficient
● Tensor Processing Units (TPUs). Answer: Specialized accelerators
based on ASICs and created by Google to improve the training of deep
neural networks
● Von Neumann Bottleneck. Answer: The limited data rate between a
processor and memory
● TensorFlow. Answer: An open source deep learning framework
created by Google that allows developers to specify a distributed
computing strategy when training a model
● MirroredStrategy. Answer: A TensorFlow strategy that supports
synchronous distributed training on GPUs attached to a single machine
