Updated Google Professional Machine Learning Engineer Exam Dumps

Question No: 1

MultipleChoice

You are creating a model training pipeline to predict sentiment scores from text-based product reviews. You want to have control over how the model parameters are tuned, and you will deploy the model to an endpoint after it has been trained You will use Vertex Al Pipelines to run the pipeline You need to decide which Google Cloud pipeline components to use What components should you choose?

Options

AOption A

BOption B

COption C

DOption D

Question No: 2

MultipleChoice

You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?

Options

AEnsure that training is reproducible

BEnsure that all hyperparameters are tuned

CEnsure that model performance is monitored

DEnsure that feature expectations are captured in the schema

Question No: 3

MultipleChoice

You work for a gaming company that manages a popular online multiplayer game where teams with 6 players play against each other in 5-minute battles. There are many new players every day. You need to build a model that automatically assigns available players to teams in real time. User research indicates that the game is more enjoyable when battles have players with similar skill levels. Which business metrics should you track to measure your model's performance? (Select One Correct Answer)

Options

AAverage time players wait before being assigned to a team

BPrecision and recall of assigning players to teams based on their predicted versus actual ability

CUser engagement as measured by the number of battles played daily per user

DRate of return as measured by additional revenue generated minus the cost of developing a new model

Answer
CExplanation

The best business metric to track to measure the model's performance is user engagement as measured by the number of battles played daily per user. This metric reflects the main goal of the model, which is to enhance the user experience and satisfaction by creating balanced and fair battles. If the model is successful, it should increase the user retention and loyalty, as well as the word-of-mouth and referrals. This metric is also easy to measure and interpret, as it can be directly obtained from the user activity data.

The other options are not optimal for the following reasons:

A . Average time players wait before being assigned to a team is not a good metric, as it does not capture the quality or outcome of the battles. It only measures the efficiency of the model, which is not the primary objective. Moreover, this metric can be influenced by external factors, such as the availability and demand of players, the network latency, and the server capacity.

B . Precision and recall of assigning players to teams based on their predicted versus actual ability is not a good metric, as it is difficult to measure and interpret. It requires having a reliable and consistent way of estimating the player's ability, which can be subjective and dynamic. It also requires having a ground truth label for each assignment, which can be costly and impractical to obtain. Moreover, this metric does not reflect the user feedback or satisfaction, which is the ultimate goal of the model.

D . Rate of return as measured by additional revenue generated minus the cost of developing a new model is not a good metric, as it is not directly related to the model's performance. It measures the profitability of the model, which is a secondary objective. Moreover, this metric can be affected by many other factors, such as the market conditions, the pricing strategy, the marketing campaigns, and the competition.

Professional ML Engineer Exam Guide

Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

Google Cloud launches machine learning engineer certification

How to measure user engagement

How to choose the right metrics for your machine learning model

Question No: 4

MultipleChoice

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?

Options

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
CExplanation

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 No: 5

MultipleChoice

Your company manages a video sharing website where users can watch and upload videos. You need to

create an ML model to predict which newly uploaded videos will be the most popular so that those videos can be prioritized on your company's website. Which result should you use to determine whether the model is successful?

Options

AThe model predicts videos as popular if the user who uploads them has over 10,000 likes.

BThe model predicts 97.5% of the most popular clickbait videos measured by number of clicks.

CThe model predicts 95% of the most popular videos measured by watch time within 30 days of being
uploaded.

DThe Pearson correlation coefficient between the log-transformed number of views after 7 days and 30 days after publication is equal to 0.

Answer
CExplanation

In this scenario, the goal is to create an ML model to predict which newly uploaded videos will be the most popular on a video sharing website. The result that should be used to determine whether the model is successful is the one that best aligns with the business objective and the evaluation metric. Option C is the correct answer because it defines the most popular videos as the ones that have the highest watch time within 30 days of being uploaded, and it sets a high accuracy threshold of 95% for the model prediction.

Option C: The model predicts 95% of the most popular videos measured by watch time within 30 days of being uploaded. This option is the best result for the scenario because it reflects the business objective and the evaluation metric. The business objective is to prioritize the videos that will attract and retain the most viewers on the website. The watch time is a good indicator of the viewer engagement and satisfaction, as it measures how long the viewers watch the videos. The 30-day window is a reasonable time frame to capture the popularity trend of the videos, as it accounts for the initial interest and the viral potential of the videos. The 95% accuracy threshold is a high standard for the model prediction, as it means that the model can correctly identify 95 out of 100 of the most popular videos based on the watch time metric.

Option A: The model predicts videos as popular if the user who uploads them has over 10,000 likes. This option is not a good result for the scenario because it does not reflect the business objective or the evaluation metric. The business objective is to prioritize the videos that will be the most popular on the website, not the users who upload them. The number of likes that a user has is not a good indicator of the popularity of their videos, as it does not measure the viewer engagement or satisfaction with the videos. Moreover, this option does not specify a time frame or an accuracy threshold for the model prediction, making it vague and unreliable.

Option B: The model predicts 97.5% of the most popular clickbait videos measured by number of clicks. This option is not a good result for the scenario because it does not reflect the business objective or the evaluation metric. The business objective is to prioritize the videos that will be the most popular on the website, not the videos that have the most misleading or sensational titles or thumbnails. The number of clicks that a video has is not a good indicator of the popularity of the video, as it does not measure the viewer engagement or satisfaction with the video content. Moreover, this option only focuses on the clickbait videos, which may not represent the majority or the diversity of the videos on the website.

Option D: The Pearson correlation coefficient between the log-transformed number of views after 7 days and 30 days after publication is equal to 0. This option is not a good result for the scenario because it does not reflect the business objective or the evaluation metric. The business objective is to prioritize the videos that will be the most popular on the website, not the videos that have the most consistent or inconsistent number of views over time. The Pearson correlation coefficient is a metric that measures the linear relationship between two variables, not the popularity of the videos. A correlation coefficient of 0 means that there is no linear relationship between the log-transformed number of views after 7 days and 30 days, which does not indicate whether the videos are popular or not. Moreover, this option does not specify a threshold or a target value for the correlation coefficient, making it meaningless and irrelevant.

Question No: 6

MultipleChoice

You work for an advertising company and want to understand the effectiveness of your company's latest advertising campaign. You have streamed 500 MB of campaign data into BigQuery. You want to query the table, and then manipulate the results of that query with a pandas dataframe in an Al Platform notebook. What should you do?

Options

AUse Al Platform Notebooks' BigQuery cell magic to query the data, and ingest the results as a pandas dataframe

BExport your table as a CSV file from BigQuery to Google Drive, and use the Google Drive API to ingest the file into your notebook instance

CDownload your table from BigQuery as a local CSV file, and upload it to your Al Platform notebook instance Use pandas. read_csv to ingest the file as a pandas dataframe

DFrom a bash cell in your Al Platform notebook, use the bq extract command to export the table as a CSV file to Cloud Storage, and then use gsutii cp to copy the data into the notebook Use pandas. read_csv to ingest the file as a pandas dataframe

Answer
AExplanation

AI Platform Notebooks is a service that provides managed Jupyter notebooks for data science and machine learning.You can use AI Platform Notebooks to create, run, and share your code and analysis in a collaborative and interactive environment1. BigQuery is a service that allows you to analyze large-scale and complex data using SQL queries.You can use BigQuery to stream, store, and query your data in a fast and cost-effective way2. Pandas is a popular Python library that provides data structures and tools for data analysis and manipulation.You can use pandas to create, manipulate, and visualize dataframes, which are tabular data structures with rows and columns3.

AI Platform Notebooks provides a cell magic, %%bigquery, that allows you to run SQL queries on BigQuery data and ingest the results as a pandas dataframe. A cell magic is a special command that applies to the whole cell in a Jupyter notebook.The %%bigquery cell magic can take various arguments, such as the name of the destination dataframe, the name of the destination table in BigQuery, the project ID, and the query parameters4. By using the %%bigquery cell magic, you can query the data in BigQuery with minimal code and manipulate the results with pandas in AI Platform Notebooks. This is the most convenient and efficient way to achieve your goal.

The other options are not as good as option A, because they involve more steps, more code, and more manual effort. Option B requires you to export your table as a CSV file from BigQuery to Google Drive, and then use the Google Drive API to ingest the file into your notebook instance. This option is cumbersome and time-consuming, as it involves moving the data across different services and formats. Option C requires you to download your table from BigQuery as a local CSV file, and then upload it to your AI Platform notebook instance. This option is also inefficient and impractical, as it involves downloading and uploading large files, which can take a long time and consume a lot of bandwidth. Option D requires you to use a bash cell in your AI Platform notebook to export the table as a CSV file to Cloud Storage, and then copy the data into the notebook. This option is also complex and unnecessary, as it involves using different commands and tools to move the data around. Therefore, option A is the best option for this use case.

AI Platform Notebooks documentation

BigQuery documentation

pandas documentation

Using Jupyter magics to query BigQuery data

Question No: 7

MultipleChoice

You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high-throughput online prediction. Which architecture should you use?

Options

A* Validate the accuracy of the model that you trained on preprocessed data
* Create a new model that uses the raw data and is available in real time
* Deploy the new model onto Al Platform for online prediction

B* Send incoming prediction requests to a Pub/Sub topic
* Transform the incoming data using a Dataflow job
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue

C* Stream incoming prediction request data into Cloud Spanner
* Create a view to abstract your preprocessing logic.
* Query the view every second for new records
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue.

D* Send incoming prediction requests to a Pub/Sub topic
* Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic.
* Implement your preprocessing logic in the Cloud Function
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue

Question No: 8

MultipleChoice

You developed a Vertex Al pipeline that trains a classification model on data stored in a large BigQuery table. The pipeline has four steps, where each step is created by a Python function that uses the KubeFlow v2 API The components have the following names:

You launch your Vertex Al pipeline as the following:

You perform many model iterations by adjusting the code and parameters of the training step. You observe high costs associated with the development, particularly the data export and preprocessing steps. You need to reduce model development costs. What should you do?

A.

B.

C.

D.

Options

AOption A

BOption B

COption C

DOption D

Question No: 9

MultipleChoice

You have trained a DNN regressor with TensorFlow to predict housing prices using a set of predictive features. Your default precision is tf.float64, and you use a standard TensorFlow estimator;

estimator = tf.estimator.DNNRegressor(

feature_columns=[YOUR_LIST_OF_FEATURES],

hidden_units-[1024, 512, 256],

dropout=None)

Your model performs well, but Just before deploying it to production, you discover that your current serving latency is 10ms @ 90 percentile and you currently serve on CPUs. Your production requirements expect a model latency of 8ms @ 90 percentile. You are willing to accept a small decrease in performance in order to reach the latency requirement Therefore your plan is to improve latency while evaluating how much the model's prediction decreases. What should you first try to quickly lower the serving latency?

Options

AIncrease the dropout rate to 0.8 in_PREDICT mode by adjusting the TensorFlow Serving parameters

BIncrease the dropout rate to 0.8 and retrain your model.

CSwitch from CPU to GPU serving

DApply quantization to your SavedModel by reducing the floating point precision to tf.float16.

Question No: 10

MultipleChoice

You are experimenting with a built-in distributed XGBoost model in Vertex AI Workbench user-managed notebooks. You use BigQuery to split your data into training and validation sets using the following queries:

CREATE OR REPLACE TABLE 'myproject.mydataset.training' AS

(SELECT * FROM 'myproject.mydataset.mytable' WHERE RAND() <= 0.8);

CREATE OR REPLACE TABLE 'myproject.mydataset.validation' AS

(SELECT * FROM 'myproject.mydataset.mytable' WHERE RAND() <= 0.2);

After training the model, you achieve an area under the receiver operating characteristic curve (AUC ROC) value of 0.8, but after deploying the model to production, you notice that your model performance has dropped to an AUC ROC value of 0.65. What problem is most likely occurring?

Options

AThere is training-serving skew in your production environment.

BThere is not a sufficient amount of training data.

CThe tables that you created to hold your training and validation records share some records, and you may not be using all the data in your initial table.

DThe RAND() function generated a number that is less than 0.2 in both instances, so every record in the validation table will also be in the training table.

Question No: 11

MultipleChoice

Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers1 account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?

A.

1. Create a Pub/Sub topic for each user

2 Deploy a Cloud Function that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.

B.

1. Create a Pub/Sub topic for each user

2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts that

a user's account balance will drop below the $25 threshold

C.

1. Build a notification system on Firebase

2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold

Options

D1 Build a notification system on Firebase
2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user's account balance will drop below the $25 threshold

Question No: 12

MultipleChoice

You need to train a computer vision model that predicts the type of government ID present in a given image using a GPU-powered virtual machine on Compute Engine. You use the following parameters:

* Optimizer: SGD

* Image shape = 224x224

* Batch size = 64

* Epochs = 10

* Verbose = 2

During training you encounter the following error: ResourceExhaustedError: out of Memory (oom) when allocating tensor. What should you do?

Options

AChange the optimizer

BReduce the batch size

CChange the learning rate

DReduce the image shape

Question No: 13

MultipleChoice

You work for a manufacturing company. You need to train a custom image classification model to detect product defects at the end of an assembly line Although your model is performing well some images in your holdout set are consistently mislabeled with high confidence You want to use Vertex Al to understand your model's results What should you do?

A.

B.

C.

D.

Options

AOption A

BOption B

COption C

DOption D

Question No: 14

MultipleChoice

You trained a model, packaged it with a custom Docker container for serving, and deployed it to Vertex Al Model Registry. When you submit a batch prediction job, it fails with this error 'Error model server never became ready Please validate that your model file or container configuration are valid. There are no additional errors in the logs What should you do?

A Add a logging configuration to your application to emit logs to Cloud Logging.

Options

BChange the HTTP port in your model's configuration to the default value of 8080

CChange the health Route value in your models configuration to /heal thcheck.

DPull the Docker image locally and use the decker run command to launch it locally. Use the docker logs command to explore the error logs.

Question No: 15

MultipleChoice

You work for a gaming company that manages a popular online multiplayer game where teams with 6 players play against each other in 5-minute battles. There are many new players every day. You need to build a model that automatically assigns available players to teams in real time. User research indicates that the game is more enjoyable when battles have players with similar skill levels. Which business metrics should you track to measure your model's performance?

Options

AAverage time players wait before being assigned to a team

BPrecision and recall of assigning players to teams based on their predicted versus actual ability

CUser engagement as measured by the number of battles played daily per user

DRate of return as measured by additional revenue generated minus the cost of developing a new model

Question No: 16

MultipleChoice

Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers1 account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?

Options

A1. Create a Pub/Sub topic for each user
2 Deploy a Cloud Function that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.

B1. Create a Pub/Sub topic for each user
2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold

C1. Build a notification system on Firebase
2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold

D1 Build a notification system on Firebase
2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user's account balance will drop below the $25 threshold

Question No: 17

MultipleChoice

Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this:

A)

B)

C)

D)

Options

AOption A

BOption B

COption C

DOption D

Question No: 18

MultipleChoice

You are designing an architecture with a serveress ML system to enrich customer support tickets with informative metadata before they are routed to a support agent. You need a set of models to predict ticket priority, predict ticket resolution time, and perform sentiment analysis to help agents make strategic decisions when they process support requests. Tickets are not expected to have any domain-specific terms or jargon.

The proposed architecture has the following flow:

Which endpoints should the Enrichment Cloud Functions call?

Options

A1 = Al Platform, 2 = Al Platform, 3 = AutoML Vision

B1 = Al Platform, 2 = Al Platform, 3 = AutoML Natural Language

C1 = Al Platform, 2 = Al Platform, 3 = Cloud Natural Language API

D1 = cloud Natural Language API, 2 = Al Platform, 3 = Cloud Vision API

Question No: 19

MultipleChoice

You started working on a classification problem with time series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven't explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?

Options

AAddress the model overfitting by using a less complex algorithm.

BAddress data leakage by applying nested cross-validation during model training.

CAddress data leakage by removing features highly correlated with the target value.

DAddress the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.

Free Google Professional Machine Learning Engineer Exam Dumps September 2026