Google Professional Machine Learning Engineer Exam Questions

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Total 283 questions
Question 1

One of your models is trained using data provided by a third-party data broker. The data broker does not reliably notify you of formatting changes in the dat

a. You want to make your model training pipeline more robust to issues like this. What should you do?



Answer : A

TensorFlow Data Validation (TFDV) is a library that helps you understand, validate, and monitor your data for machine learning. It can automatically detect and report schema anomalies, such as missing features, new features, or different data types, in your data. It can also generate descriptive statistics and data visualizations to help you explore and debug your data. TFDV can be integrated with your model training pipeline to ensure data quality and consistency throughout the machine learning lifecycle.Reference:

TensorFlow Data Validation

Data Validation | TensorFlow

Data Validation | Machine Learning Crash Course | Google Developers


Question 2

You are a lead ML engineer at a retail company. You want to track and manage ML metadata in a centralized way so that your team can have reproducible experiments by generating artifacts. Which management solution should you recommend to your team?



Answer : D

Vertex ML Metadata is a service that lets you track and manage the metadata produced by your ML workflows in a centralized way. It helps you have reproducible experiments by generating artifacts that represent the data, parameters, and metrics used or produced by your ML system. You can also analyze the lineage and performance of your ML artifacts using Vertex ML Metadata.

Some of the benefits of using Vertex ML Metadata are:

It captures your ML system's metadata as a graph, where artifacts and executions are nodes, and events are edges that link them as inputs or outputs.

It allows you to create contexts to group sets of artifacts and executions together, such as experiments, runs, or projects.

It supports querying and filtering the metadata using the Vertex AI SDK for Python or REST commands.

It integrates with other Vertex AI services, such as Vertex AI Pipelines and Vertex AI Experiments, to automatically log metadata and artifacts.

The other options are not suitable for tracking and managing ML metadata in a centralized way.

Option A: Storing your tf.logging data in BigQuery is not enough to capture the full metadata of your ML system, such as the artifacts and their lineage. BigQuery is a data warehouse service that is mainly used for analytics and reporting, not for metadata management.

Option B: Managing all relational entities in the Hive Metastore is not a good solution for ML metadata, as it is designed for storing metadata of Hive tables and partitions, not for ML artifacts and executions. Hive Metastore is a component of the Apache Hive project, which is a data warehouse system for querying and analyzing large datasets stored in Hadoop.

Option C: Storing all ML metadata in Google Cloud's operations suite is not a feasible option, as it is a set of tools for monitoring, logging, tracing, and debugging your applications and infrastructure, not for ML metadata. Google Cloud's operations suite does not provide the features and integrations that Vertex ML Metadata offers for ML workflows.


Question 3

Your team trained and tested a DNN regression model with good results. Six months after deployment, the model is performing poorly due to a change in the distribution of the input dat

a. How should you address the input differences in production?



Answer : A

The performance of a DNN regression model can degrade over time due to a change in the distribution of the input data. This phenomenon is known as data drift or concept drift, and it can affect the accuracy and reliability of the model predictions.Data drift can be caused by various factors, such as seasonal changes, population shifts, market trends, or external events1

To address the input differences in production, one should create alerts to monitor for skew, and retrain the model. Skew is a measure of how much the input data in production differs from the input data used for training the model. Skew can be detected by comparing the statistics and distributions of the input features in the training and production data, such as mean, standard deviation, histogram, or quantiles.Alerts can be set up to notify the model developers or operators when the skew exceeds a certain threshold, indicating a significant change in the input data2

When an alert is triggered, the model should be retrained with the latest data that reflects the current distribution of the input features. Retraining the model can help the model adapt to the new data and improve its performance. Retraining the model can be done manually or automatically, depending on the frequency and severity of the data drift.Retraining the model can also involve updating the model architecture, hyperparameters, or optimization algorithm, if necessary3

The other options are not as effective or feasible. Performing feature selection on the model and retraining the model with fewer features is not a good idea, as it may reduce the expressiveness and complexity of the model, and ignore some important features that may affect the output. Retraining the model and selecting an L2 regularization parameter with a hyperparameter tuning service is not relevant, as L2 regularization is a technique to prevent overfitting, not data drift. Retraining the model on a monthly basis with fewer features is not optimal, as it may not capture the timely changes in the input data, and may compromise the model performance.


Question 4

You are an ML engineer at a regulated insurance company. You are asked to develop an insurance approval model that accepts or rejects insurance applications from potential customers. What factors should you consider before building the model?



Answer : B

Before building an insurance approval model, an ML engineer should consider the factors of traceability, reproducibility, and explainability, as these are important aspects of responsible AI and fairness in a regulated domain. Traceability is the ability to track the provenance and lineage of the data, models, and decisions throughout the ML lifecycle. It helps to ensure the quality, reliability, and accountability of the ML system, and to comply with the regulatory and ethical standards. Reproducibility is the ability to recreate the same results and outcomes using the same data, models, and parameters. It helps to verify the validity, consistency, and robustness of the ML system, and to debug and improve the performance. Explainability is the ability to understand and interpret the logic, behavior, and outcomes of the ML system. It helps to increase the transparency, trust, and confidence of the ML system, and to identify and mitigate any potential biases, errors, or risks. The other options are not as relevant or comprehensive as this option. Redaction is the process of removing sensitive or confidential information from the data or documents, but it is not a factor that the ML engineer should consider before building the model, as it is more related to the data preparation and protection. Federated learning is a technique that allows training ML models on decentralized data without transferring the data to a central server, but it is not a factor that the ML engineer should consider before building the model, as it is more related to the model architecture and privacy preservation. Differential privacy is a method that adds noise to the data or the model outputs to protect the individual privacy of the data subjects, but it is not a factor that the ML engineer should consider before building the model, as it is more related to the model evaluation and deployment.Reference:

Responsible AI documentation

Traceability documentation

Reproducibility documentation

Explainability documentation


Question 5

You need to design a customized deep neural network in Keras that will predict customer purchases based on their purchase history. You want to explore model performance using multiple model architectures, store training data, and be able to compare the evaluation metrics in the same dashboard. What should you do?



Answer : D

Kubeflow Pipelines is a service that allows you to create and run machine learning workflows on Google Cloud using various features, model architectures, and hyperparameters.You can use Kubeflow Pipelines to scale up your workflows, leverage distributed training, and access specialized hardware such as GPUs and TPUs1. An experiment in Kubeflow Pipelines is a workspace where you can try different configurations of your pipelines and organize your runs into logical groups.You can use experiments to compare the performance of different models and track the evaluation metrics in the same dashboard2.

For the use case of designing a customized deep neural network in Keras that will predict customer purchases based on their purchase history, the best option is to create an experiment in Kubeflow Pipelines to organize multiple runs. This option allows you to explore model performance using multiple model architectures, store training data, and compare the evaluation metrics in the same dashboard. You can use Keras to build and train your deep neural network models, and then package them as pipeline components that can be reused and combined with other components. You can also use Kubeflow Pipelines SDK to define and submit your pipelines programmatically, and use Kubeflow Pipelines UI to monitor and manage your experiments. Therefore, creating an experiment in Kubeflow Pipelines to organize multiple runs is the best option for this use case.


Kubeflow Pipelines documentation

Experiment | Kubeflow

Question 6

You created a model that uses BigQuery ML to perform linear regression. You need to retrain the model on the cumulative data collected every week. You want to minimize the development effort and the scheduling cost. What should you do?



Answer : B

BigQuery is a serverless data warehouse that allows you to perform SQL queries on large-scale data. BigQuery ML is a feature of BigQuery that enables you to create and execute machine learning models using standard SQL queries. You can use BigQuery ML to perform linear regression on your data and create a model. BigQuery also provides a scheduling service that allows you to create and manage recurring SQL queries. You can use BigQuery's scheduling service to run the model retraining query periodically, such as every week. You can specify the destination table for the query results, and the schedule options, such as start date, end date, frequency, and time zone. You can also monitor the status and history of your scheduled queries. This solution can help you retrain the model on the cumulative data collected every week, while minimizing the development effort and the scheduling cost.Reference:

BigQuery ML | Google Cloud

Scheduling queries | BigQuery


Question 7

You are building an ML model to predict trends in the stock market based on a wide range of factors. While exploring the data, you notice that some features have a large range. You want to ensure that the features with the largest magnitude don't overfit the model. What should you do?



Answer : D

The best option to ensure that the features with the largest magnitude don't overfit the model is to normalize the data by scaling it to have values between 0 and 1. This is also known as min-max scaling or feature scaling, and it can reduce the variance and skewness of the data, as well as improve the numerical stability and convergence of the model. Normalizing the data can also make the model less sensitive to the scale of the features, and more focused on the relative importance of each feature. Normalizing the data can be done using various methods, such as dividing each value by the maximum value, subtracting the minimum value and dividing by the range, or using the sklearn.preprocessing.MinMaxScaler function in Python.

The other options are not optimal for the following reasons:

A . Standardizing the data by transforming it with a logarithmic function is not a good option, as it can distort the distribution and relationship of the data, and introduce bias and errors. Moreover, the logarithmic function is not defined for negative or zero values, which can limit its applicability and cause problems for the model.

B . Applying a principal component analysis (PCA) to minimize the effect of any particular feature is not a good option, as it can reduce the interpretability and explainability of the data and the model. PCA is a dimensionality reduction technique that transforms the data into a new set of orthogonal features that capture the most variance in the data. However, these new features are not directly related to the original features, and can lose some information and meaning in the process. Moreover, PCA can be computationally expensive and complex, and may not be necessary for the problem at hand.

C . Using a binning strategy to replace the magnitude of each feature with the appropriate bin number is not a good option, as it can lose the granularity and precision of the data, and introduce noise and outliers. Binning is a discretization technique that groups the continuous values of a feature into a finite number of bins or categories. However, this can reduce the variability and diversity of the data, and create artificial boundaries and gaps that may not reflect the true nature of the data. Moreover, binning can be arbitrary and subjective, and depend on the choice of the bin size and number.


Professional ML Engineer Exam Guide

Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

Google Cloud launches machine learning engineer certification

Feature Scaling for Machine Learning: Understanding the Difference Between Normalization vs. Standardization

sklearn.preprocessing.MinMaxScaler documentation

Principal Component Analysis Explained Visually

Binning Data in Python

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