Google Professional Machine Learning Engineer Exam Questions

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Question 1

You are building a predictive maintenance model to preemptively detect part defects in bridges. You plan to use high definition images of the bridges as model inputs. You need to explain the output of the model to the relevant stakeholders so they can take appropriate action. How should you build the model?

AUse scikit-learn to build a tree-based model, and use SHAP values to explain the model output.

BUse scikit-lean to build a tree-based model, and use partial dependence plots (PDP) to explain the model output.

CUse TensorFlow to create a deep learning-based model and use Integrated Gradients to explain the model
output.

DUse TensorFlow to create a deep learning-based model and use the sampled Shapley method to explain the model output.

Answer : C

According to the official exam guide1, one of the skills assessed in the exam is to ''explain the predictions of a trained model''.TensorFlow2is an open source framework for developing and deploying machine learning and deep learning models.TensorFlow supports various model explainability methods, such as Integrated Gradients3, which is a technique that assigns an importance score to each input feature by approximating the integral of the gradients along the path from a baseline input to the actual input. Integrated Gradients can help explain the output of a deep learning-based model by highlighting the most influential features in the input images. Therefore, option C is the best way to build the model for the given use case. The other options are not relevant or optimal for this scenario.Reference:

Professional ML Engineer Exam Guide

TensorFlow

Integrated Gradients

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

Question 2

You are building a TensorFlow text-to-image generative model by using a dataset that contains billions of images with their respective captions. You want to create a low maintenance, automated workflow that reads the data from a Cloud Storage bucket collects statistics, splits the dataset into training/validation/test datasets performs data transformations, trains the model using the training/validation datasets. and validates the model by using the test dataset. What should you do?

AUse the Apache Airflow SDK to create multiple operators that use Dataflow and Vertex Al services Deploy the workflow on Cloud Composer.

BUse the MLFlow SDK and deploy it on a Google Kubernetes Engine Cluster Create multiple components that use Dataflow and Vertex Al services.

CUse the Kubeflow Pipelines (KFP) SDK to create multiple components that use Dataflow and Vertex Al services Deploy the workflow on Vertex Al Pipelines.

DUse the TensorFlow Extended (TFX) SDK to create multiple components that use Dataflow and Vertex Al services Deploy the workflow on Vertex Al Pipelines.

Answer : D

According to the web search results, TensorFlow Extended (TFX) is a platform for building end-to-end machine learning pipelines using TensorFlow1. TFX provides a set of components that can be orchestrated using either the TFX SDK or Kubeflow Pipelines. TFX components can handle different aspects of the pipeline, such as data ingestion, data validation, data transformation, model training, model evaluation, model serving, and more.TFX components can also leverage other Google Cloud services, such as Dataflow2and Vertex AI3. Dataflow is a fully managed service for running Apache Beam pipelines on Google Cloud. Dataflow handles the provisioning and management of the compute resources, as well as the optimization and execution of the pipelines. Vertex AI is a unified platform for machine learning development and deployment. Vertex AI offers various services and tools for building, managing, and serving machine learning models. Therefore, option D is the best way to create a low maintenance, automated workflow for the given use case, as it allows you to use the TFX SDK to define and execute your pipeline components, and use Dataflow and Vertex AI services to scale and optimize your pipeline. The other options are not relevant or optimal for this scenario.Reference:

TensorFlow Extended

Dataflow

Vertex AI

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

Question 3

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?

AUse TensorFlow Data Validation to detect and flag schema anomalies.

BUse TensorFlow Transform to create a preprocessing component that will normalize data to the expected distribution, and replace values that don't match the schema with 0.

CUse tf.math to analyze the data, compute summary statistics, and flag statistical anomalies.

DUse custom TensorFlow functions at the start of your model training to detect and flag known formatting errors.

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 4

You work on the data science team for a multinational beverage company. You need to develop an ML model to predict the company's profitability for a new line of naturally flavored bottled waters in different locations. You are provided with historical data that includes product types, product sales volumes, expenses, and profits for all regions. What should you use as the input and output for your model?

AUse latitude, longitude, and product type as features. Use profit as model output.

BUse latitude, longitude, and product type as features. Use revenue and expenses as model outputs.

CUse product type and the feature cross of latitude with longitude, followed by binning, as features. Use profit as model output.

DUse product type and the feature cross of latitude with longitude, followed by binning, as features. Use revenue and expenses as model outputs.

Answer : C

Option A is incorrect because using latitude, longitude, and product type as features, and using profit as model output is not the best way to develop an ML model to predict the company's profitability for a new line of naturally flavored bottled waters in different locations. This option does not capture the interaction between latitude and longitude, which may affect the profitability of the product. For example, the same product may have different profitability in different regions, depending on the climate, culture, or preferences of the customers. Moreover, this option does not account for the granularity of the location data, which may be too fine or too coarse for the model. For example, using the exact coordinates of a city may not be meaningful, as the profitability may vary within the city, or using the country name may not be informative, as the profitability may vary across the country.

Option B is incorrect because using latitude, longitude, and product type as features, and using revenue and expenses as model outputs is not a suitable way to develop an ML model to predict the company's profitability for a new line of naturally flavored bottled waters in different locations. This option has the same drawbacks as option A, as it does not capture the interaction between latitude and longitude, or account for the granularity of the location data. Moreover, this option does not directly predict the profitability of the product, which is the target variable of interest. Instead, it predicts the revenue and expenses of the product, which are intermediate variables that depend on other factors, such as the price, the cost, or the demand of the product. To obtain the profitability, we would need to subtract the expenses from the revenue, which may introduce errors or uncertainties in the prediction.

Option C is correct because using product type and the feature cross of latitude with longitude, followed by binning, as features, and using profit as model output is a good way to develop an ML model to predict the company's profitability for a new line of naturally flavored bottled waters in different locations. This option captures the interaction between latitude and longitude, which may affect the profitability of the product, by creating a feature cross of these two features.A feature cross is a synthetic feature that combines the values of two or more features into a single feature1. This option also accounts for the granularity of the location data, by binning the feature cross into discrete buckets.Binning is a technique that groups continuous values into intervals, which can reduce the noise and complexity of the data2. Moreover, this option directly predicts the profitability of the product, which is the target variable of interest, by using it as the model output.

Option D is incorrect because using product type and the feature cross of latitude with longitude, followed by binning, as features, and using revenue and expenses as model outputs is not a valid way to develop an ML model to predict the company's profitability for a new line of naturally flavored bottled waters in different locations. This option has the same advantages as option C, as it captures the interaction between latitude and longitude, and accounts for the granularity of the location data, by creating a feature cross and binning it. However, this option does not directly predict the profitability of the product, which is the target variable of interest, but rather predicts the revenue and expenses of the product, which are intermediate variables that depend on other factors, as explained in option B.

Feature cross

Binning

[Profitability]

[Revenue and expenses]

[Latitude and longitude]

[Product type]

Question 5

You want to train an AutoML model to predict house prices by using a small public dataset stored in BigQuery. You need to prepare the data and want to use the simplest most efficient approach. What should you do?

AWrite a query that preprocesses the data by using BigQuery and creates a new table Create a Vertex Al managed dataset with the new table as the data source.

BUse Dataflow to preprocess the data Write the output in TFRecord format to a Cloud Storage bucket.

CWrite a query that preprocesses the data by using BigQuery Export the query results as CSV files and use
those files to create a Vertex Al managed dataset.

DUse a Vertex Al Workbench notebook instance to preprocess the data by using the pandas library Export the data as CSV files, and use those files to create a Vertex Al managed dataset.

Answer : A

The simplest and most efficient approach for preparing the data for AutoML is to use BigQuery and Vertex AI. BigQuery is a serverless, scalable, and cost-effective data warehouse that can perform fast and interactive queries on large datasets. BigQuery can preprocess the data by using SQL functions such as filtering, aggregating, joining, transforming, and creating new features. The preprocessed data can be stored in a new table in BigQuery, which can be used as the data source for Vertex AI. Vertex AI is a unified platform for building and deploying machine learning solutions on Google Cloud. Vertex AI can create a managed dataset from a BigQuery table, which can be used to train an AutoML model. Vertex AI can also evaluate, deploy, and monitor the AutoML model, and provide online or batch predictions. By using BigQuery and Vertex AI, users can leverage the power and simplicity of Google Cloud to train an AutoML model to predict house prices.

The other options are not as simple or efficient as option A, for the following reasons:

Option B: Using Dataflow to preprocess the data and write the output in TFRecord format to a Cloud Storage bucket would require more steps and resources than using BigQuery and Vertex AI. Dataflow is a service that can create scalable and reliable pipelines to process large volumes of data from various sources. Dataflow can preprocess the data by using Apache Beam, a programming model for defining and executing data processing workflows. TFRecord is a binary file format that can store sequential data efficiently. However, using Dataflow and TFRecord would require writing code, setting up a pipeline, choosing a runner, and managing the output files. Moreover, TFRecord is not a supported format for Vertex AI managed datasets, so the data would need to be converted to CSV or JSONL files before creating a Vertex AI managed dataset.

Option C: Writing a query that preprocesses the data by using BigQuery and exporting the query results as CSV files would require more steps and storage than using BigQuery and Vertex AI. CSV is a text file format that can store tabular data in a comma-separated format. Exporting the query results as CSV files would require choosing a destination Cloud Storage bucket, specifying a file name or a wildcard, and setting the export options. Moreover, CSV files can have limitations such as size, schema, and encoding, which can affect the quality and validity of the data. Exporting the data as CSV files would also incur additional storage costs and reduce the performance of the queries.

Option D: Using a Vertex AI Workbench notebook instance to preprocess the data by using the pandas library and exporting the data as CSV files would require more steps and skills than using BigQuery and Vertex AI. Vertex AI Workbench is a service that provides an integrated development environment for data science and machine learning. Vertex AI Workbench allows users to create and run Jupyter notebooks on Google Cloud, and access various tools and libraries for data analysis and machine learning. Pandas is a popular Python library that can manipulate and analyze data in a tabular format. However, using Vertex AI Workbench and pandas would require creating a notebook instance, writing Python code, installing and importing pandas, connecting to BigQuery, loading and preprocessing the data, and exporting the data as CSV files. Moreover, pandas can have limitations such as memory usage, scalability, and compatibility, which can affect the efficiency and reliability of the data processing.

Preparing for Google Cloud Certification: Machine Learning Engineer, Course 2: Data Engineering for ML on Google Cloud, Week 1: Introduction to Data Engineering for ML

Google Cloud Professional Machine Learning Engineer Exam Guide, Section 1: Architecting low-code ML solutions, 1.3 Training models by using AutoML

Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 4: Low-code ML Solutions, Section 4.3: AutoML

BigQuery

Vertex AI

Dataflow

TFRecord

CSV

Vertex AI Workbench

Pandas

Question 6

You work at a subscription-based company. You have trained an ensemble of trees and neural networks to predict customer churn, which is the likelihood that customers will not renew their yearly subscription. The average prediction is a 15% churn rate, but for a particular customer the model predicts that they are 70% likely to churn. The customer has a product usage history of 30%, is located in New York City, and became a customer in 1997. You need to explain the difference between the actual prediction, a 70% churn rate, and the average prediction. You want to use Vertex Explainable AI. What should you do?

ATrain local surrogate models to explain individual predictions.

BConfigure sampled Shapley explanations on Vertex Explainable AI.

CConfigure integrated gradients explanations on Vertex Explainable AI.

DMeasure the effect of each feature as the weight of the feature multiplied by the feature value.

Answer : B

Option A is incorrect because training local surrogate models to explain individual predictions is not a feature of Vertex Explainable AI, but rather a general technique for interpreting black-box models.Local surrogate models are simpler models that approximate the behavior of the original model around a specific input1.

Option B is correct because configuring sampled Shapley explanations on Vertex Explainable AI is a way to explain the difference between the actual prediction and the average prediction for a given input.Sampled Shapley explanations are based on the Shapley value, which is a game-theoretic concept that measures how much each feature contributes to the prediction2.Vertex Explainable AI supports sampled Shapley explanations for tabular data, such as customer churn3.

Option C is incorrect because configuring integrated gradients explanations on Vertex Explainable AI is not suitable for explaining the difference between the actual prediction and the average prediction for a given input.Integrated gradients explanations are based on the idea of computing the gradients of the prediction with respect to the input features along a path from a baseline input to the actual input4.Vertex Explainable AI supports integrated gradients explanations for image and text data, but not for tabular data3.

Option D is incorrect because measuring the effect of each feature as the weight of the feature multiplied by the feature value is not a valid way to explain the difference between the actual prediction and the average prediction for a given input. This method assumes that the model is linear and additive, which is not the case for an ensemble of trees and neural networks.Moreover, this method does not account for the interactions between features or the non-linearity of the model5.

Local surrogate models

Shapley value

Vertex Explainable AI overview

Integrated gradients

Feature importance

Question 7

You are profiling the performance of your TensorFlow model training time and notice a performance issue caused by inefficiencies in the input data pipeline for a single 5 terabyte CSV file dataset on Cloud Storage. You need to optimize the input pipeline performance. Which action should you try first to increase the efficiency of your pipeline?

APreprocess the input CSV file into a TFRecord file.

BRandomly select a 10 gigabyte subset of the data to train your model.

CSplit into multiple CSV files and use a parallel interleave transformation.

DSet the reshuffle_each_iteration parameter to true in the tf.data.Dataset.shuffle method.

Answer : A

According to the web search results, the TFRecord format is a recommended way to store large amounts of data efficiently and improve the performance of the data input pipeline123.The TFRecord format is a binary format that can be compressed and serialized, which reduces the I/O overhead and the memory footprint of the data1.The tf.data API provides tools to create and read TFRecord files easily1.

The other options are not as effective as option A. Option B would reduce the amount of data available for training and might affect the model accuracy. Option C would still require reading from a single CSV file at a time, which might not utilize the full bandwidth of the remote storage. Option D would only affect the order of the data elements, not the speed of reading them.