Google Professional Machine Learning Engineer Practice Exams
Last updated on Oct 09,2026- Exam Code: Professional Machine Learning Engineer
- Exam Name: Google Professional Machine Learning Engineer
- Certification Provider: Google
- Latest update: Oct 09,2026
Question #150
You work for a global footwear retailer and need to predict when an item will be out of stock based on historical inventory data. Customer behavior is highly dynamic since footwear demand is influenced by many different factors. You want to serve models that are trained on all available data, but track your performance on specific subsets of data before pushing to production.
What is the most streamlined and reliable way to perform this validation?
- A . Use the TFX ModelValidator tools to specify performance metrics for production readiness
- B . Use k-fold cross-validation as a validation strategy to ensure that your model is ready for production.
- C . Use the last relevant week of data as a validation set to ensure that your model is performing accurately on current data
- D . Use the entire dataset and treat the area under the receiver operating characteristics curve (AUC ROC) as the main metric.
Correct Answer: A
A
Explanation:
TFX ModelValidator is a tool that allows you to compare new models against a baseline model and evaluate their performance on different metrics and data slices1. You can use this tool to validate your models before deploying them to production and ensure that they meet your expectations and requirements.
k-fold cross-validation is a technique that splits the data into k subsets and trains the model on k-1 subsets while testing it on the remaining subset. This is repeated k times and the average performance is reported2. This technique is useful for estimating the generalization error of a model, but it does not account for the dynamic nature of customer behavior or the potential changes in data distribution over time.
Using the last relevant week of data as a validation set is a simple way to check the model’s performance on recent data, but it may not be representative of the entire data or capture the long-term trends and patterns. It also does not allow you to compare the model with a baseline or evaluate it on different data slices.
Using the entire dataset and treating the AUC ROC as the main metric is not a good practice because it does not leave any data for validation or testing. It also assumes that the AUC ROC is the only metric that matters, which may not be true for your business problem. You may want to consider other metrics such as precision, recall, or revenue.
A
Explanation:
TFX ModelValidator is a tool that allows you to compare new models against a baseline model and evaluate their performance on different metrics and data slices1. You can use this tool to validate your models before deploying them to production and ensure that they meet your expectations and requirements.
k-fold cross-validation is a technique that splits the data into k subsets and trains the model on k-1 subsets while testing it on the remaining subset. This is repeated k times and the average performance is reported2. This technique is useful for estimating the generalization error of a model, but it does not account for the dynamic nature of customer behavior or the potential changes in data distribution over time.
Using the last relevant week of data as a validation set is a simple way to check the model’s performance on recent data, but it may not be representative of the entire data or capture the long-term trends and patterns. It also does not allow you to compare the model with a baseline or evaluate it on different data slices.
Using the entire dataset and treating the AUC ROC as the main metric is not a good practice because it does not leave any data for validation or testing. It also assumes that the AUC ROC is the only metric that matters, which may not be true for your business problem. You may want to consider other metrics such as precision, recall, or revenue.
Question #152
You work for an online publisher that delivers news articles to over 50 million readers. You have built an AI model that recommends content for the company’s weekly newsletter. A recommendation is considered successful if the article is opened within two days of the newsletter’s published date and the user remains on the page for at least one minute.
All the information needed to compute the success metric is available in BigQuery and is updated hourly. The model is trained on eight weeks of data, on average its performance degrades below the acceptable baseline after five weeks, and training time is 12 hours. You want to ensure that the model’s performance is above the acceptable baseline while minimizing cost.
How should you monitor the model to determine when retraining is necessary?
- A . Use Vertex AI Model Monitoring to detect skew of the input features with a sample rate of 100% and a monitoring frequency of two days.
- B . Schedule a cron job in Cloud Tasks to retrain the model every week before the newsletter is created.
- C . Schedule a weekly query in BigQuery to compute the success metric.
- D . Schedule a daily Dataflow job in Cloud Composer to compute the success metric.
Correct Answer: C
C
Explanation:
The best option for monitoring the model to determine when retraining is necessary is to schedule a weekly query in BigQuery to compute the success metric. This option has the following advantages: It allows the model performance to be evaluated regularly, based on the actual outcome of the recommendations. By computing the success metric, which is the percentage of articles that are opened within two days and read for at least one minute, you can measure how well the model is achieving its objective and compare it with the acceptable baseline.
It leverages the scalability and efficiency of BigQuery, which is a serverless, fully managed, and highly scalable data warehouse that can run complex queries over petabytes of data in seconds. By using BigQuery, you can access and analyze all the information needed to compute the success metric, such as the newsletter publication date, the article opening date, and the user reading time, without worrying about the infrastructure or the cost.
It simplifies the model monitoring and retraining workflow, as the weekly query can be scheduled and executed automatically using BigQuery’s built-in scheduling feature. You can also set up alerts or notifications to inform you when the success metric falls below the acceptable baseline, and trigger the model retraining process accordingly.
The other options are less optimal for the following reasons:
Option A: Using Vertex AI Model Monitoring to detect skew of the input features with a sample rate of 100% and a monitoring frequency of two days introduces additional complexity and overhead. This option requires setting up and managing a Vertex AI Model Monitoring service, which is a managed service that provides various tools and features for machine learning, such as training, tuning, serving, and monitoring. However, using Vertex AI Model Monitoring to detect skew of the input features may not reflect the actual performance of the model, as skew is the discrepancy between the distributions of the features in the training dataset and the serving data, which may not affect the outcome of the recommendations. Moreover, using a sample rate of 100% and a monitoring frequency of two days may incur unnecessary cost and latency, as it requires analyzing all the input features every two days, which may not be needed for the model monitoring.
Option B: Scheduling a cron job in Cloud Tasks to retrain the model every week before the newsletter is created introduces additional cost and risk. This option requires creating and running a cron job in Cloud Tasks, which is a fully managed service that allows you to schedule and execute tasks that are invoked by HTTP requests. However, using Cloud Tasks to retrain the model every week may not be optimal, as it may retrain the model more often than necessary, wasting compute resources and cost.
Moreover, using Cloud Tasks to retrain the model before the newsletter is created may introduce risk, as it may deploy a new model version that has not been tested or validated, potentially affecting the quality of the recommendations.
Option D: Scheduling a daily Dataflow job in Cloud Composer to compute the success metric introduces additional complexity and cost. This option requires creating and running a Dataflow job in Cloud Composer, which is a fully managed service that runs Apache Airflow pipelines for workflow orchestration. Dataflow is a fully managed service that runs Apache Beam pipelines for data processing and transformation. However, using Dataflow and Cloud Composer to compute the success metric may not be necessary, as it may add more steps and overhead to the model monitoring process. Moreover, using Dataflow and Cloud Composer to compute the success metric daily may not be optimal, as it may compute the success metric more often than needed, consuming more compute resources and cost.
Reference: [BigQuery documentation]
[Vertex AI Model Monitoring documentation]
[Cloud Tasks documentation]
[Cloud Composer documentation]
[Dataflow documentation]
C
Explanation:
The best option for monitoring the model to determine when retraining is necessary is to schedule a weekly query in BigQuery to compute the success metric. This option has the following advantages: It allows the model performance to be evaluated regularly, based on the actual outcome of the recommendations. By computing the success metric, which is the percentage of articles that are opened within two days and read for at least one minute, you can measure how well the model is achieving its objective and compare it with the acceptable baseline.
It leverages the scalability and efficiency of BigQuery, which is a serverless, fully managed, and highly scalable data warehouse that can run complex queries over petabytes of data in seconds. By using BigQuery, you can access and analyze all the information needed to compute the success metric, such as the newsletter publication date, the article opening date, and the user reading time, without worrying about the infrastructure or the cost.
It simplifies the model monitoring and retraining workflow, as the weekly query can be scheduled and executed automatically using BigQuery’s built-in scheduling feature. You can also set up alerts or notifications to inform you when the success metric falls below the acceptable baseline, and trigger the model retraining process accordingly.
The other options are less optimal for the following reasons:
Option A: Using Vertex AI Model Monitoring to detect skew of the input features with a sample rate of 100% and a monitoring frequency of two days introduces additional complexity and overhead. This option requires setting up and managing a Vertex AI Model Monitoring service, which is a managed service that provides various tools and features for machine learning, such as training, tuning, serving, and monitoring. However, using Vertex AI Model Monitoring to detect skew of the input features may not reflect the actual performance of the model, as skew is the discrepancy between the distributions of the features in the training dataset and the serving data, which may not affect the outcome of the recommendations. Moreover, using a sample rate of 100% and a monitoring frequency of two days may incur unnecessary cost and latency, as it requires analyzing all the input features every two days, which may not be needed for the model monitoring.
Option B: Scheduling a cron job in Cloud Tasks to retrain the model every week before the newsletter is created introduces additional cost and risk. This option requires creating and running a cron job in Cloud Tasks, which is a fully managed service that allows you to schedule and execute tasks that are invoked by HTTP requests. However, using Cloud Tasks to retrain the model every week may not be optimal, as it may retrain the model more often than necessary, wasting compute resources and cost.
Moreover, using Cloud Tasks to retrain the model before the newsletter is created may introduce risk, as it may deploy a new model version that has not been tested or validated, potentially affecting the quality of the recommendations.
Option D: Scheduling a daily Dataflow job in Cloud Composer to compute the success metric introduces additional complexity and cost. This option requires creating and running a Dataflow job in Cloud Composer, which is a fully managed service that runs Apache Airflow pipelines for workflow orchestration. Dataflow is a fully managed service that runs Apache Beam pipelines for data processing and transformation. However, using Dataflow and Cloud Composer to compute the success metric may not be necessary, as it may add more steps and overhead to the model monitoring process. Moreover, using Dataflow and Cloud Composer to compute the success metric daily may not be optimal, as it may compute the success metric more often than needed, consuming more compute resources and cost.
Reference: [BigQuery documentation]
[Vertex AI Model Monitoring documentation]
[Cloud Tasks documentation]
[Cloud Composer documentation]
[Dataflow documentation]
Question #153
You need to quickly build and train a model to predict the sentiment of customer reviews with custom categories without writing code. You do not have enough data to train a model from scratch. The resulting model should have high predictive performance.
Which service should you use?
- A . AutoML Natural Language
- B . Cloud Natural Language API
- C . AI Hub pre-made Jupyter Notebooks
- D . AI Platform Training built-in algorithms
Correct Answer: A
A
Explanation:
AutoML Natural Language is a service that allows you to build and train custom natural language models without writing code. You can use AutoML Natural Language to perform sentiment analysis with custom categories, such as positive, negative, or neutral. You can also use pre-trained models or transfer learning to leverage existing knowledge and reduce the amount of data required to train a model from scratch. AutoML Natural Language provides a user-friendly interface and a powerful AutoML engine that optimizes your model for high predictive performance.
Cloud Natural Language API is a service that provides pre-trained models for common natural language tasks, such as sentiment analysis, entity analysis, and syntax analysis. However, it does not allow you to customize the categories or use your own data for training.
AI Hub pre-made Jupyter Notebooks are interactive documents that contain code, text, and visualizations for various machine learning scenarios. However, they require some coding skills and data preparation to use them effectively.
AI Platform Training built-in algorithms are pre-configured machine learning algorithms that you can use to train models on AI Platform. However, they do not support sentiment analysis as a natural language task.
Reference: AutoML Natural Language documentation
Cloud Natural Language API documentation
AI Hub documentation
AI Platform Training documentation
A
Explanation:
AutoML Natural Language is a service that allows you to build and train custom natural language models without writing code. You can use AutoML Natural Language to perform sentiment analysis with custom categories, such as positive, negative, or neutral. You can also use pre-trained models or transfer learning to leverage existing knowledge and reduce the amount of data required to train a model from scratch. AutoML Natural Language provides a user-friendly interface and a powerful AutoML engine that optimizes your model for high predictive performance.
Cloud Natural Language API is a service that provides pre-trained models for common natural language tasks, such as sentiment analysis, entity analysis, and syntax analysis. However, it does not allow you to customize the categories or use your own data for training.
AI Hub pre-made Jupyter Notebooks are interactive documents that contain code, text, and visualizations for various machine learning scenarios. However, they require some coding skills and data preparation to use them effectively.
AI Platform Training built-in algorithms are pre-configured machine learning algorithms that you can use to train models on AI Platform. However, they do not support sentiment analysis as a natural language task.
Reference: AutoML Natural Language documentation
Cloud Natural Language API documentation
AI Hub documentation
AI Platform Training documentation
Question #153
You need to quickly build and train a model to predict the sentiment of customer reviews with custom categories without writing code. You do not have enough data to train a model from scratch. The resulting model should have high predictive performance.
Which service should you use?
- A . AutoML Natural Language
- B . Cloud Natural Language API
- C . AI Hub pre-made Jupyter Notebooks
- D . AI Platform Training built-in algorithms
Correct Answer: A
A
Explanation:
AutoML Natural Language is a service that allows you to build and train custom natural language models without writing code. You can use AutoML Natural Language to perform sentiment analysis with custom categories, such as positive, negative, or neutral. You can also use pre-trained models or transfer learning to leverage existing knowledge and reduce the amount of data required to train a model from scratch. AutoML Natural Language provides a user-friendly interface and a powerful AutoML engine that optimizes your model for high predictive performance.
Cloud Natural Language API is a service that provides pre-trained models for common natural language tasks, such as sentiment analysis, entity analysis, and syntax analysis. However, it does not allow you to customize the categories or use your own data for training.
AI Hub pre-made Jupyter Notebooks are interactive documents that contain code, text, and visualizations for various machine learning scenarios. However, they require some coding skills and data preparation to use them effectively.
AI Platform Training built-in algorithms are pre-configured machine learning algorithms that you can use to train models on AI Platform. However, they do not support sentiment analysis as a natural language task.
Reference: AutoML Natural Language documentation
Cloud Natural Language API documentation
AI Hub documentation
AI Platform Training documentation
A
Explanation:
AutoML Natural Language is a service that allows you to build and train custom natural language models without writing code. You can use AutoML Natural Language to perform sentiment analysis with custom categories, such as positive, negative, or neutral. You can also use pre-trained models or transfer learning to leverage existing knowledge and reduce the amount of data required to train a model from scratch. AutoML Natural Language provides a user-friendly interface and a powerful AutoML engine that optimizes your model for high predictive performance.
Cloud Natural Language API is a service that provides pre-trained models for common natural language tasks, such as sentiment analysis, entity analysis, and syntax analysis. However, it does not allow you to customize the categories or use your own data for training.
AI Hub pre-made Jupyter Notebooks are interactive documents that contain code, text, and visualizations for various machine learning scenarios. However, they require some coding skills and data preparation to use them effectively.
AI Platform Training built-in algorithms are pre-configured machine learning algorithms that you can use to train models on AI Platform. However, they do not support sentiment analysis as a natural language task.
Reference: AutoML Natural Language documentation
Cloud Natural Language API documentation
AI Hub documentation
AI Platform Training documentation
Question #153
You need to quickly build and train a model to predict the sentiment of customer reviews with custom categories without writing code. You do not have enough data to train a model from scratch. The resulting model should have high predictive performance.
Which service should you use?
- A . AutoML Natural Language
- B . Cloud Natural Language API
- C . AI Hub pre-made Jupyter Notebooks
- D . AI Platform Training built-in algorithms
Correct Answer: A
A
Explanation:
AutoML Natural Language is a service that allows you to build and train custom natural language models without writing code. You can use AutoML Natural Language to perform sentiment analysis with custom categories, such as positive, negative, or neutral. You can also use pre-trained models or transfer learning to leverage existing knowledge and reduce the amount of data required to train a model from scratch. AutoML Natural Language provides a user-friendly interface and a powerful AutoML engine that optimizes your model for high predictive performance.
Cloud Natural Language API is a service that provides pre-trained models for common natural language tasks, such as sentiment analysis, entity analysis, and syntax analysis. However, it does not allow you to customize the categories or use your own data for training.
AI Hub pre-made Jupyter Notebooks are interactive documents that contain code, text, and visualizations for various machine learning scenarios. However, they require some coding skills and data preparation to use them effectively.
AI Platform Training built-in algorithms are pre-configured machine learning algorithms that you can use to train models on AI Platform. However, they do not support sentiment analysis as a natural language task.
Reference: AutoML Natural Language documentation
Cloud Natural Language API documentation
AI Hub documentation
AI Platform Training documentation
A
Explanation:
AutoML Natural Language is a service that allows you to build and train custom natural language models without writing code. You can use AutoML Natural Language to perform sentiment analysis with custom categories, such as positive, negative, or neutral. You can also use pre-trained models or transfer learning to leverage existing knowledge and reduce the amount of data required to train a model from scratch. AutoML Natural Language provides a user-friendly interface and a powerful AutoML engine that optimizes your model for high predictive performance.
Cloud Natural Language API is a service that provides pre-trained models for common natural language tasks, such as sentiment analysis, entity analysis, and syntax analysis. However, it does not allow you to customize the categories or use your own data for training.
AI Hub pre-made Jupyter Notebooks are interactive documents that contain code, text, and visualizations for various machine learning scenarios. However, they require some coding skills and data preparation to use them effectively.
AI Platform Training built-in algorithms are pre-configured machine learning algorithms that you can use to train models on AI Platform. However, they do not support sentiment analysis as a natural language task.
Reference: AutoML Natural Language documentation
Cloud Natural Language API documentation
AI Hub documentation
AI Platform Training documentation
Question #156
You developed a custom model by using Vertex Al to predict your application’s user churn rate You are using Vertex Al Model Monitoring for skew detection The training data stored in BigQuery contains two sets of features – demographic and behavioral You later discover that two separate models trained on each set perform better than the original model
You need to configure a new model mentioning pipeline that splits traffic among the two models You want to use the same prediction-sampling-rate and monitoring-frequency for each model You also want to minimize management effort.
What should you do?
- A . Keep the training dataset as is Deploy the models to two separate endpoints and submit two Vertex Al Model Monitoring jobs with appropriately selected feature-thresholds parameters
- B . Keep the training dataset as is Deploy both models to the same endpoint and submit a Vertex Al Model Monitoring job with a monitoring-config-from parameter that accounts for the model IDs and feature selections
- C . Separate the training dataset into two tables based on demographic and behavioral features Deploy the models to two separate endpoints, and submit two Vertex Al Model Monitoring jobs
- D . Separate the training dataset into two tables based on demographic and behavioral features. Deploy both models to the same endpoint and submit a Vertex Al Model Monitoring job with a monitoring-config-from parameter that accounts for the model IDs and training datasets
Correct Answer: D
D
Explanation:
Option A is incorrect because it does not separate the training dataset into two tables based on the features, which is necessary to train the two models separately and accurately.
Option B is incorrect because it does not separate the training dataset into two tables based on the features, and because it uses the same monitoring-config-from parameter for both models, which would not account for the different feature selections.
Option C is incorrect because it deploys the models to two separate endpoints, which would increase the management effort and complexity of the pipeline.
Option D is correct because it separates the training dataset into two tables based on the features, which would enable the two models to be trained separately and accurately. It also deploys both models to the same endpoint, which would simplify the pipeline and reduce the management effort. It also submits a Vertex Al Model Monitoring job with a monitoring-config-from parameter that accounts for the model IDs and training datasets, which would enable the skew detection to work properly for each model.
D
Explanation:
Option A is incorrect because it does not separate the training dataset into two tables based on the features, which is necessary to train the two models separately and accurately.
Option B is incorrect because it does not separate the training dataset into two tables based on the features, and because it uses the same monitoring-config-from parameter for both models, which would not account for the different feature selections.
Option C is incorrect because it deploys the models to two separate endpoints, which would increase the management effort and complexity of the pipeline.
Option D is correct because it separates the training dataset into two tables based on the features, which would enable the two models to be trained separately and accurately. It also deploys both models to the same endpoint, which would simplify the pipeline and reduce the management effort. It also submits a Vertex Al Model Monitoring job with a monitoring-config-from parameter that accounts for the model IDs and training datasets, which would enable the skew detection to work properly for each model.
Question #156
You developed a custom model by using Vertex Al to predict your application’s user churn rate You are using Vertex Al Model Monitoring for skew detection The training data stored in BigQuery contains two sets of features – demographic and behavioral You later discover that two separate models trained on each set perform better than the original model
You need to configure a new model mentioning pipeline that splits traffic among the two models You want to use the same prediction-sampling-rate and monitoring-frequency for each model You also want to minimize management effort.
What should you do?
- A . Keep the training dataset as is Deploy the models to two separate endpoints and submit two Vertex Al Model Monitoring jobs with appropriately selected feature-thresholds parameters
- B . Keep the training dataset as is Deploy both models to the same endpoint and submit a Vertex Al Model Monitoring job with a monitoring-config-from parameter that accounts for the model IDs and feature selections
- C . Separate the training dataset into two tables based on demographic and behavioral features Deploy the models to two separate endpoints, and submit two Vertex Al Model Monitoring jobs
- D . Separate the training dataset into two tables based on demographic and behavioral features. Deploy both models to the same endpoint and submit a Vertex Al Model Monitoring job with a monitoring-config-from parameter that accounts for the model IDs and training datasets
Correct Answer: D
D
Explanation:
Option A is incorrect because it does not separate the training dataset into two tables based on the features, which is necessary to train the two models separately and accurately.
Option B is incorrect because it does not separate the training dataset into two tables based on the features, and because it uses the same monitoring-config-from parameter for both models, which would not account for the different feature selections.
Option C is incorrect because it deploys the models to two separate endpoints, which would increase the management effort and complexity of the pipeline.
Option D is correct because it separates the training dataset into two tables based on the features, which would enable the two models to be trained separately and accurately. It also deploys both models to the same endpoint, which would simplify the pipeline and reduce the management effort. It also submits a Vertex Al Model Monitoring job with a monitoring-config-from parameter that accounts for the model IDs and training datasets, which would enable the skew detection to work properly for each model.
D
Explanation:
Option A is incorrect because it does not separate the training dataset into two tables based on the features, which is necessary to train the two models separately and accurately.
Option B is incorrect because it does not separate the training dataset into two tables based on the features, and because it uses the same monitoring-config-from parameter for both models, which would not account for the different feature selections.
Option C is incorrect because it deploys the models to two separate endpoints, which would increase the management effort and complexity of the pipeline.
Option D is correct because it separates the training dataset into two tables based on the features, which would enable the two models to be trained separately and accurately. It also deploys both models to the same endpoint, which would simplify the pipeline and reduce the management effort. It also submits a Vertex Al Model Monitoring job with a monitoring-config-from parameter that accounts for the model IDs and training datasets, which would enable the skew detection to work properly for each model.
Question #158
You are training a custom language model for your company using a large dataset. You plan to use the ReductionServer strategy on Vertex Al. You need to configure the worker pools of the distributed training job.
What should you do?
- A . Configure the machines of the first two worker pools to have GPUs and to use a container image where your training code runs Configure the third worker pool to have GPUs: and use the reduction server container image.
- B . Configure the machines of the first two worker pools to have GPUs and to use a container image where your training code runs. Configure the third worker pool to use the reductionserver container image without accelerators, and choose a machine type that prioritizes bandwidth.
- C . Configure the machines of the first two worker pools to have TPUs and to use a container image where your training code runs Configure the third worker pool without accelerators, and use the reductionserver container image without accelerators and choose a machine type that prioritizes bandwidth.
- D . Configure the machines of the first two pools to have TPUs. and to use a container image where your training code runs Configure the third pool to have TPUs: and use the reductionserver container image.
Correct Answer: B
B
Explanation:
According to the web search results, Reduction Server is a faster GPU all-reduce algorithm developed at Google that uses a dedicated set of reducers to aggregate gradients from workers12. Reducers are lightweight CPU VM instances that are significantly cheaper than GPU VMs2. Therefore, the third worker pool should not have any accelerators, and should use a machine type that has high network bandwidth to optimize the communication between workers and reducers2. TPUs are not supported by Reduction Server, so the first two worker pools should have GPUs and use a container image that contains the training code12. The reduction-server container image is provided by Google and should be used for the third worker pool2.
B
Explanation:
According to the web search results, Reduction Server is a faster GPU all-reduce algorithm developed at Google that uses a dedicated set of reducers to aggregate gradients from workers12. Reducers are lightweight CPU VM instances that are significantly cheaper than GPU VMs2. Therefore, the third worker pool should not have any accelerators, and should use a machine type that has high network bandwidth to optimize the communication between workers and reducers2. TPUs are not supported by Reduction Server, so the first two worker pools should have GPUs and use a container image that contains the training code12. The reduction-server container image is provided by Google and should be used for the third worker pool2.
Question #159
You work for a social media company. You need to detect whether posted images contain cars. Each training example is a member of exactly one class. You have trained an object detection neural network and deployed the model version to Al Platform Prediction for evaluation. Before deployment, you created an evaluation job and attached it to the Al Platform Prediction model version. You notice that the precision is lower than your business requirements allow.
How should you adjust the model’s final layer softmax threshold to increase precision?
- A . Increase the recall
- B . Decrease the recall.
- C . Increase the number of false positives
- D . Decrease the number of false negatives
Correct Answer: B
B
Explanation:
Precision and recall are two common metrics for evaluating the performance of a classification model. Precision measures the proportion of positive predictions that are correct, while recall measures the proportion of positive examples that are correctly predicted. Precision and recall are inversely related, meaning that increasing one will decrease the other, and vice versa. The trade-off between precision and recall depends on the goal and the cost of the classification problem1.
For the use case of detecting whether posted images contain cars, precision is more important than recall, as the social media company wants to minimize the number of false positives, or images that are incorrectly labeled as containing cars. A high precision means that the model is confident and accurate in its positive predictions, while a low recall means that the model may miss some positive examples, or images that actually contain cars. The cost of missing some positive examples is lower than the cost of making wrong positive predictions, as the latter may affect the user experience and the reputation of the social media company.
The softmax function is a function that transforms a vector of real numbers into a probability distribution over the possible classes. The softmax function is often used as the final layer of a neural network for multi-class classification problems, as it assigns a probability to each class, and the class with the highest probability is chosen as the prediction. The softmax function is defined as: softmax (x_i) exp (x_i) / sum_j exp (x_j)
where x_i is the input value for class i, and softmax (x_i) is the output probability for class i.
The softmax threshold is a parameter that determines the minimum probability that a class must have to be chosen as the prediction. For example, if the softmax threshold is 0.5, then the class with the highest probability must have at least 0.5 to be selected, otherwise the prediction is none. The softmax threshold can be used to adjust the trade-off between precision and recall, as a higher threshold will increase the precision and decrease the recall, while a lower threshold will decrease the precision and increase the recall2.
For the use case of detecting whether posted images contain cars, the best way to adjust the model’s final layer softmax threshold to increase precision is to decrease the recall. This means that the softmax threshold should be increased, so that the model will only make positive predictions when it is highly confident, and avoid making false positives. By increasing the softmax threshold, the model will become more selective and accurate in its positive predictions, and improve the precision metric. Therefore, decreasing the recall is the best option for this use case.
Reference: Precision and recall – Wikipedia
How to add a threshold in softmax scores – Stack Overflow
B
Explanation:
Precision and recall are two common metrics for evaluating the performance of a classification model. Precision measures the proportion of positive predictions that are correct, while recall measures the proportion of positive examples that are correctly predicted. Precision and recall are inversely related, meaning that increasing one will decrease the other, and vice versa. The trade-off between precision and recall depends on the goal and the cost of the classification problem1.
For the use case of detecting whether posted images contain cars, precision is more important than recall, as the social media company wants to minimize the number of false positives, or images that are incorrectly labeled as containing cars. A high precision means that the model is confident and accurate in its positive predictions, while a low recall means that the model may miss some positive examples, or images that actually contain cars. The cost of missing some positive examples is lower than the cost of making wrong positive predictions, as the latter may affect the user experience and the reputation of the social media company.
The softmax function is a function that transforms a vector of real numbers into a probability distribution over the possible classes. The softmax function is often used as the final layer of a neural network for multi-class classification problems, as it assigns a probability to each class, and the class with the highest probability is chosen as the prediction. The softmax function is defined as: softmax (x_i) exp (x_i) / sum_j exp (x_j)
where x_i is the input value for class i, and softmax (x_i) is the output probability for class i.
The softmax threshold is a parameter that determines the minimum probability that a class must have to be chosen as the prediction. For example, if the softmax threshold is 0.5, then the class with the highest probability must have at least 0.5 to be selected, otherwise the prediction is none. The softmax threshold can be used to adjust the trade-off between precision and recall, as a higher threshold will increase the precision and decrease the recall, while a lower threshold will decrease the precision and increase the recall2.
For the use case of detecting whether posted images contain cars, the best way to adjust the model’s final layer softmax threshold to increase precision is to decrease the recall. This means that the softmax threshold should be increased, so that the model will only make positive predictions when it is highly confident, and avoid making false positives. By increasing the softmax threshold, the model will become more selective and accurate in its positive predictions, and improve the precision metric. Therefore, decreasing the recall is the best option for this use case.
Reference: Precision and recall – Wikipedia
How to add a threshold in softmax scores – Stack Overflow
Question #159
You work for a social media company. You need to detect whether posted images contain cars. Each training example is a member of exactly one class. You have trained an object detection neural network and deployed the model version to Al Platform Prediction for evaluation. Before deployment, you created an evaluation job and attached it to the Al Platform Prediction model version. You notice that the precision is lower than your business requirements allow.
How should you adjust the model’s final layer softmax threshold to increase precision?
- A . Increase the recall
- B . Decrease the recall.
- C . Increase the number of false positives
- D . Decrease the number of false negatives
Correct Answer: B
B
Explanation:
Precision and recall are two common metrics for evaluating the performance of a classification model. Precision measures the proportion of positive predictions that are correct, while recall measures the proportion of positive examples that are correctly predicted. Precision and recall are inversely related, meaning that increasing one will decrease the other, and vice versa. The trade-off between precision and recall depends on the goal and the cost of the classification problem1.
For the use case of detecting whether posted images contain cars, precision is more important than recall, as the social media company wants to minimize the number of false positives, or images that are incorrectly labeled as containing cars. A high precision means that the model is confident and accurate in its positive predictions, while a low recall means that the model may miss some positive examples, or images that actually contain cars. The cost of missing some positive examples is lower than the cost of making wrong positive predictions, as the latter may affect the user experience and the reputation of the social media company.
The softmax function is a function that transforms a vector of real numbers into a probability distribution over the possible classes. The softmax function is often used as the final layer of a neural network for multi-class classification problems, as it assigns a probability to each class, and the class with the highest probability is chosen as the prediction. The softmax function is defined as: softmax (x_i) exp (x_i) / sum_j exp (x_j)
where x_i is the input value for class i, and softmax (x_i) is the output probability for class i.
The softmax threshold is a parameter that determines the minimum probability that a class must have to be chosen as the prediction. For example, if the softmax threshold is 0.5, then the class with the highest probability must have at least 0.5 to be selected, otherwise the prediction is none. The softmax threshold can be used to adjust the trade-off between precision and recall, as a higher threshold will increase the precision and decrease the recall, while a lower threshold will decrease the precision and increase the recall2.
For the use case of detecting whether posted images contain cars, the best way to adjust the model’s final layer softmax threshold to increase precision is to decrease the recall. This means that the softmax threshold should be increased, so that the model will only make positive predictions when it is highly confident, and avoid making false positives. By increasing the softmax threshold, the model will become more selective and accurate in its positive predictions, and improve the precision metric. Therefore, decreasing the recall is the best option for this use case.
Reference: Precision and recall – Wikipedia
How to add a threshold in softmax scores – Stack Overflow
B
Explanation:
Precision and recall are two common metrics for evaluating the performance of a classification model. Precision measures the proportion of positive predictions that are correct, while recall measures the proportion of positive examples that are correctly predicted. Precision and recall are inversely related, meaning that increasing one will decrease the other, and vice versa. The trade-off between precision and recall depends on the goal and the cost of the classification problem1.
For the use case of detecting whether posted images contain cars, precision is more important than recall, as the social media company wants to minimize the number of false positives, or images that are incorrectly labeled as containing cars. A high precision means that the model is confident and accurate in its positive predictions, while a low recall means that the model may miss some positive examples, or images that actually contain cars. The cost of missing some positive examples is lower than the cost of making wrong positive predictions, as the latter may affect the user experience and the reputation of the social media company.
The softmax function is a function that transforms a vector of real numbers into a probability distribution over the possible classes. The softmax function is often used as the final layer of a neural network for multi-class classification problems, as it assigns a probability to each class, and the class with the highest probability is chosen as the prediction. The softmax function is defined as: softmax (x_i) exp (x_i) / sum_j exp (x_j)
where x_i is the input value for class i, and softmax (x_i) is the output probability for class i.
The softmax threshold is a parameter that determines the minimum probability that a class must have to be chosen as the prediction. For example, if the softmax threshold is 0.5, then the class with the highest probability must have at least 0.5 to be selected, otherwise the prediction is none. The softmax threshold can be used to adjust the trade-off between precision and recall, as a higher threshold will increase the precision and decrease the recall, while a lower threshold will decrease the precision and increase the recall2.
For the use case of detecting whether posted images contain cars, the best way to adjust the model’s final layer softmax threshold to increase precision is to decrease the recall. This means that the softmax threshold should be increased, so that the model will only make positive predictions when it is highly confident, and avoid making false positives. By increasing the softmax threshold, the model will become more selective and accurate in its positive predictions, and improve the precision metric. Therefore, decreasing the recall is the best option for this use case.
Reference: Precision and recall – Wikipedia
How to add a threshold in softmax scores – Stack Overflow