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 #11
You have deployed a model on Vertex AI for real-time inference. During an online prediction request, you get an “Out of Memory” error.
What should you do?
- A . Use batch prediction mode instead of online mode.
- B . Send the request again with a smaller batch of instances.
- C . Use base64 to encode your data before using it for prediction.
- D . Apply for a quota increase for the number of prediction requests.
Correct Answer: B
B
Explanation:
Option A is incorrect because using batch prediction mode instead of online mode does not solve the “Out of Memory” error, but rather changes the latency and throughput of the prediction service. Batch prediction mode is suitable for large-scale, asynchronous, and non-urgent predictions, while online prediction mode is suitable for low-latency, synchronous, and real-time predictions1.
Option B is correct because sending the request again with a smaller batch of instances can reduce the memory consumption of the prediction service and avoid the “Out of Memory” error. The batch size is the number of instances that are processed together in one request. A smaller batch size means less data to load into memory at once2.
Option C is incorrect because using base64 to encode your data before using it for prediction does not reduce the memory consumption of the prediction service, but rather increases it. Base64 encoding is a way of representing binary data as ASCII characters, which increases the size of the data by about 33%3. Base64 encoding is only required for certain data types, such as images and audio, that cannot be represented as JSON or CSV4.
Option D is incorrect because applying for a quota increase for the number of prediction requests does not solve the “Out of Memory” error, but rather increases the number of requests that can be sent to the prediction service per day. Quotas are limits on the usage of Google Cloud resources, such as CPU, memory, disk, and network5. Quotas do not affect the performance of the prediction service, but rather the availability and cost of the service.
Reference: Choosing between online and batch prediction
Online prediction input data
Base64 encoding
Preparing data for prediction
Quotas and limits
B
Explanation:
Option A is incorrect because using batch prediction mode instead of online mode does not solve the “Out of Memory” error, but rather changes the latency and throughput of the prediction service. Batch prediction mode is suitable for large-scale, asynchronous, and non-urgent predictions, while online prediction mode is suitable for low-latency, synchronous, and real-time predictions1.
Option B is correct because sending the request again with a smaller batch of instances can reduce the memory consumption of the prediction service and avoid the “Out of Memory” error. The batch size is the number of instances that are processed together in one request. A smaller batch size means less data to load into memory at once2.
Option C is incorrect because using base64 to encode your data before using it for prediction does not reduce the memory consumption of the prediction service, but rather increases it. Base64 encoding is a way of representing binary data as ASCII characters, which increases the size of the data by about 33%3. Base64 encoding is only required for certain data types, such as images and audio, that cannot be represented as JSON or CSV4.
Option D is incorrect because applying for a quota increase for the number of prediction requests does not solve the “Out of Memory” error, but rather increases the number of requests that can be sent to the prediction service per day. Quotas are limits on the usage of Google Cloud resources, such as CPU, memory, disk, and network5. Quotas do not affect the performance of the prediction service, but rather the availability and cost of the service.
Reference: Choosing between online and batch prediction
Online prediction input data
Base64 encoding
Preparing data for prediction
Quotas and limits
Question #12
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?
- A . Ensure that training is reproducible
- B . Ensure that all hyperparameters are tuned
- C . Ensure that model performance is monitored
- D . Ensure that feature expectations are captured in the schema
Correct Answer: C
C
Explanation:
Monitoring model performance is an essential part of production readiness, as it allows the team to
detect and address any issues that may arise after deployment, such as data drift, model
degradation, or errors.
Other Options:
C
Explanation:
Monitoring model performance is an essential part of production readiness, as it allows the team to
detect and address any issues that may arise after deployment, such as data drift, model
degradation, or errors.
Other Options:
Question #13
You have created multiple versions of an ML model and have imported them to Vertex AI Model Registry. You want to perform A/B testing to identify the best-performing model using the simplest approach.
What should you do?
- A . Split incoming traffic among separate Cloud Run instances of deployed models. Monitor the performance of each version using Cloud Monitoring.
- B . Split incoming traffic to distribute prediction requests among the versions. Monitor the performance of each version using Looker Studio dashboards that compare logged data for each version.
- C . Split incoming traffic among Google Kubernetes Engine (GKE) clusters and use Traffic Director to distribute prediction requests to different versions. Monitor the performance of each version using Cloud Monitoring.
- D . Split incoming traffic to distribute prediction requests among the versions. Monitor the performance of each version using Vertex AI’s built-in monitoring tools.
Correct Answer: D
D
Explanation:
Vertex AI Model Registry supports traffic splitting and built-in monitoring, making A/B testing seamless. This approach eliminates the need for additional monitoring tools and infrastructure overhead. Cloud Run and GKE solutions (Options A and C) add unnecessary complexity, while Looker Studio (Option B) requires additional configuration for monitoring.
D
Explanation:
Vertex AI Model Registry supports traffic splitting and built-in monitoring, making A/B testing seamless. This approach eliminates the need for additional monitoring tools and infrastructure overhead. Cloud Run and GKE solutions (Options A and C) add unnecessary complexity, while Looker Studio (Option B) requires additional configuration for monitoring.
Question #14
You work on a growing team of more than 50 data scientists who all use Al Platform. You are designing a strategy to organize your jobs, models, and versions in a clean and scalable way.
Which strategy should you choose?
- A . Set up restrictive I AM permissions on the Al Platform notebooks so that only a single user or group can access a given instance.
- B . Separate each data scientist’s work into a different project to ensure that the jobs, models, and versions created by each data scientist are accessible only to that user.
- C . Use labels to organize resources into descriptive categories. Apply a label to each created resource so that users can filter the results by label when viewing or monitoring the resources
- D . Set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about Al Platform resource usage In BigQuery create a SQL view that maps users to the resources they are using.
Correct Answer: C
C
Explanation:
Labels are key-value pairs that can be attached to any AI Platform resource, such as jobs, models, versions, or endpoints1. Labels can help you organize your resources into descriptive categories, such as project, team, environment, or purpose. You can use labels to filter the results when you list or monitor your resources, or to group them for billing or quota purposes2. Using labels is a simple and scalable way to manage your AI Platform resources without creating unnecessary complexity or overhead. Therefore, using labels to organize resources is the best strategy for this use case.
Reference: Using labels
Filtering and grouping by labels
C
Explanation:
Labels are key-value pairs that can be attached to any AI Platform resource, such as jobs, models, versions, or endpoints1. Labels can help you organize your resources into descriptive categories, such as project, team, environment, or purpose. You can use labels to filter the results when you list or monitor your resources, or to group them for billing or quota purposes2. Using labels is a simple and scalable way to manage your AI Platform resources without creating unnecessary complexity or overhead. Therefore, using labels to organize resources is the best strategy for this use case.
Reference: Using labels
Filtering and grouping by labels
Question #15
You have been asked to productionize a proof-of-concept ML model built using Keras. The model was trained in a Jupyter notebook on a data scientist’s local machine. The notebook contains a cell that performs data validation and a cell that performs model analysis. You need to orchestrate the steps contained in the notebook and automate the execution of these steps for weekly retraining. You expect much more training data in the future. You want your solution to take advantage of managed services while minimizing cost.
What should you do?
- A . Move the Jupyter notebook to a Notebooks instance on the largest N2 machine type, and schedule the execution of the steps in the Notebooks instance using Cloud Scheduler.
- B . Write the code as a TensorFlow Extended (TFX) pipeline orchestrated with Vertex AI Pipelines. Use standard TFX components for data validation and model analysis, and use Vertex AI Pipelines for model retraining.
- C . Rewrite the steps in the Jupyter notebook as an Apache Spark job, and schedule the execution of the job on ephemeral Dataproc clusters using Cloud Scheduler.
- D . Extract the steps contained in the Jupyter notebook as Python scripts, wrap each script in an Apache Airflow BashOperator, and run the resulting directed acyclic graph (DAG) in Cloud Composer.
Correct Answer: B
B
Explanation:
The best option for productionizing a Keras model is to use TensorFlow Extended (TFX), a framework for building end-to-end machine learning pipelines that can handle large-scale data and complex workflows. TFX provides standard components for data ingestion, transformation, validation, analysis, training, tuning, serving, and monitoring. TFX pipelines can be orchestrated with Vertex AI Pipelines, a managed service that runs on Google Cloud Platform and leverages Kubernetes and Argo. Vertex AI Pipelines allows you to automate the execution of your TFX pipeline steps, schedule retraining jobs, and scale up or down the resources as needed. By using TFX and Vertex AI Pipelines, you can take advantage of the following benefits:
You can reuse the existing code in your Jupyter notebook, as TFX supports Keras as a first-class citizen. You can also use the Keras Tuner to optimize your model hyperparameters.
You can ensure data quality and consistency by using the TFX Data Validation component, which can detect anomalies, drift, and skew in your data. You can also use the TFX SchemaGen component to generate a schema for your data and enforce it throughout the pipeline.
You can analyze your model performance and fairness by using the TFX Model Analysis component, which can produce various metrics and visualizations. You can also use the TFX Model Validation component to compare your new model with a baseline model and set thresholds for deploying the model to production.
You can deploy your model to various serving platforms by using the TFX Pusher component, which can push your model to Vertex AI, Cloud AI Platform, TensorFlow Serving, or TensorFlow Lite. You can also use the TFX Model Registry to manage the versions and metadata of your models.
You can monitor your model performance and health by using the TFX Model Monitor component, which can detect data drift, concept drift, and prediction skew in your model. You can also use the TFX Evaluator component to compute metrics and validate your model against a baseline or a slice of data.
You can reduce the cost and complexity of managing your own infrastructure by using Vertex AI Pipelines, which provides a serverless environment for running your TFX pipeline. You can also use the Vertex AI Experiments and Vertex AI TensorBoard to track and visualize your pipeline runs.
Reference: [TensorFlow Extended (TFX)]
[Vertex AI Pipelines]
[TFX User Guide]
B
Explanation:
The best option for productionizing a Keras model is to use TensorFlow Extended (TFX), a framework for building end-to-end machine learning pipelines that can handle large-scale data and complex workflows. TFX provides standard components for data ingestion, transformation, validation, analysis, training, tuning, serving, and monitoring. TFX pipelines can be orchestrated with Vertex AI Pipelines, a managed service that runs on Google Cloud Platform and leverages Kubernetes and Argo. Vertex AI Pipelines allows you to automate the execution of your TFX pipeline steps, schedule retraining jobs, and scale up or down the resources as needed. By using TFX and Vertex AI Pipelines, you can take advantage of the following benefits:
You can reuse the existing code in your Jupyter notebook, as TFX supports Keras as a first-class citizen. You can also use the Keras Tuner to optimize your model hyperparameters.
You can ensure data quality and consistency by using the TFX Data Validation component, which can detect anomalies, drift, and skew in your data. You can also use the TFX SchemaGen component to generate a schema for your data and enforce it throughout the pipeline.
You can analyze your model performance and fairness by using the TFX Model Analysis component, which can produce various metrics and visualizations. You can also use the TFX Model Validation component to compare your new model with a baseline model and set thresholds for deploying the model to production.
You can deploy your model to various serving platforms by using the TFX Pusher component, which can push your model to Vertex AI, Cloud AI Platform, TensorFlow Serving, or TensorFlow Lite. You can also use the TFX Model Registry to manage the versions and metadata of your models.
You can monitor your model performance and health by using the TFX Model Monitor component, which can detect data drift, concept drift, and prediction skew in your model. You can also use the TFX Evaluator component to compute metrics and validate your model against a baseline or a slice of data.
You can reduce the cost and complexity of managing your own infrastructure by using Vertex AI Pipelines, which provides a serverless environment for running your TFX pipeline. You can also use the Vertex AI Experiments and Vertex AI TensorBoard to track and visualize your pipeline runs.
Reference: [TensorFlow Extended (TFX)]
[Vertex AI Pipelines]
[TFX User Guide]
Question #16
You work for a company that is developing a new video streaming platform. You have been asked to create a recommendation system that will suggest the next video for a user to watch. After a review by an AI Ethics team, you are approved to start development. Each video asset in your company’s catalog has useful metadata (e.g., content type, release date, country), but you do not have any historical user event data.
How should you build the recommendation system for the first version of the product?
- A . Launch the product without machine learning. Present videos to users alphabetically, and start collecting user event data so you can develop a recommender model in the future.
- B . Launch the product without machine learning. Use simple heuristics based on content metadata to recommend similar videos to users, and start collecting user event data so you can develop a recommender model in the future.
- C . Launch the product with machine learning. Use a publicly available dataset such as MovieLens to train a model using the Recommendations AI, and then apply this trained model to your data.
- D . Launch the product with machine learning. Generate embeddings for each video by training an autoencoder on the content metadata using TensorFlow. Cluster content based on the similarity of these embeddings, and then recommend videos from the same cluster.
Correct Answer: B
B
Explanation:
The best option for building a recommendation system without any user event data is to use simple heuristics based on content metadata. This is a type of content-based filtering, which recommends items that are similar to the ones that the user has interacted with or selected, based on their attributes. For example, if a user selects a comedy movie from the US released in 2020, the system can recommend other comedy movies from the US released in 2020 or nearby years. This approach does not require any machine learning, but it can leverage the existing metadata of the videos to provide relevant recommendations. It also allows the system to start collecting user event data, such as views, likes, ratings, etc., which can be used to train a more sophisticated machine learning model in the future, such as a collaborative filtering model or a hybrid model that combines content and collaborative information.
Reference: Recommendation Systems
Content-Based Filtering
Collaborative Filtering
Hybrid Recommender Systems: A Systematic Literature Review
B
Explanation:
The best option for building a recommendation system without any user event data is to use simple heuristics based on content metadata. This is a type of content-based filtering, which recommends items that are similar to the ones that the user has interacted with or selected, based on their attributes. For example, if a user selects a comedy movie from the US released in 2020, the system can recommend other comedy movies from the US released in 2020 or nearby years. This approach does not require any machine learning, but it can leverage the existing metadata of the videos to provide relevant recommendations. It also allows the system to start collecting user event data, such as views, likes, ratings, etc., which can be used to train a more sophisticated machine learning model in the future, such as a collaborative filtering model or a hybrid model that combines content and collaborative information.
Reference: Recommendation Systems
Content-Based Filtering
Collaborative Filtering
Hybrid Recommender Systems: A Systematic Literature Review
Question #17
Your team needs to build a model that predicts whether images contain a driver’s license, passport, or credit card. The data engineering team already built the pipeline and generated a dataset composed of 10,000 images with driver’s licenses, 1,000 images with passports, and 1,000 images with credit cards. You now have to train a model with the following label map: [‘driversjicense’, ‘passport’, ‘credit_card’].
Which loss function should you use?
- A . Categorical hinge
- B . Binary cross-entropy
- C . Categorical cross-entropy
- D . Sparse categorical cross-entropy
Correct Answer: C
C
Explanation:
Categorical cross-entropy is a loss function that is suitable for multi-class classification problems, where the target variable has more than two possible values. Categorical cross-entropy measures the difference between the true probability distribution of the target classes and the predicted probability distribution of the model. It is defined as: L – sum(y_i * log(p_i))
where y_i is the true probability of class i, and p_i is the predicted probability of class i. Categorical cross-entropy penalizes the model for making incorrect predictions, and encourages the model to assign high probabilities to the correct classes and low probabilities to the incorrect classes. For the use case of building a model that predicts whether images contain a driver’s license, passport, or credit card, categorical cross-entropy is the appropriate loss function to use. This is because the problem is a multi-class classification problem, where the target variable has three possible values: [‘drivers_license’, ‘passport’, ‘credit_card’]. The label map is a list that maps the class names to the class indices, such that ‘drivers_license’ corresponds to index 0, ‘passport’ corresponds to index 1, and ‘credit_card’ corresponds to index 2. The model should output a probability distribution over the three classes for each image, and the categorical cross-entropy loss function should compare the output with the true labels. Therefore, categorical cross-entropy is the best loss function for this use case.
C
Explanation:
Categorical cross-entropy is a loss function that is suitable for multi-class classification problems, where the target variable has more than two possible values. Categorical cross-entropy measures the difference between the true probability distribution of the target classes and the predicted probability distribution of the model. It is defined as: L – sum(y_i * log(p_i))
where y_i is the true probability of class i, and p_i is the predicted probability of class i. Categorical cross-entropy penalizes the model for making incorrect predictions, and encourages the model to assign high probabilities to the correct classes and low probabilities to the incorrect classes. For the use case of building a model that predicts whether images contain a driver’s license, passport, or credit card, categorical cross-entropy is the appropriate loss function to use. This is because the problem is a multi-class classification problem, where the target variable has three possible values: [‘drivers_license’, ‘passport’, ‘credit_card’]. The label map is a list that maps the class names to the class indices, such that ‘drivers_license’ corresponds to index 0, ‘passport’ corresponds to index 1, and ‘credit_card’ corresponds to index 2. The model should output a probability distribution over the three classes for each image, and the categorical cross-entropy loss function should compare the output with the true labels. Therefore, categorical cross-entropy is the best loss function for this use case.
Question #18
You are an ML engineer at a mobile gaming company. A data scientist on your team recently trained a TensorFlow model, and you are responsible for deploying this model into a mobile application. You discover that the inference latency of the current model doesn’t meet production requirements. You need to reduce the inference time by 50%, and you are willing to accept a small decrease in model accuracy in order to reach the latency requirement.
Without training a new model, which model optimization technique for reducing latency should you try first?
- A . Weight pruning
- B . Dynamic range quantization
- C . Model distillation
- D . Dimensionality reduction
Correct Answer: B
B
Explanation:
Dynamic range quantization is a model optimization technique for reducing latency that reduces the numerical precision of the weights and activations of models. This technique can reduce the model size, memory usage, and inference time by up to 4x with negligible accuracy loss. Dynamic range quantization can be applied to a trained TensorFlow model without retraining, and it is suitable for mobile applications that require low latency and power consumption.
Weight pruning, model distillation, and dimensionality reduction are also model optimization techniques for reducing latency, but they have some limitations or drawbacks compared to dynamic range quantization:
Weight pruning works by removing parameters within a model that have only a minor impact on its predictions. Pruned models are the same size on disk, and have the same runtime latency, but can be compressed more effectively. This makes pruning a useful technique for reducing model download size, but not for reducing inference time.
Model distillation works by training a smaller and simpler model (student) to mimic the behavior of a larger and complex model (teacher). Distilled models can have lower latency and memory usage than the original models, but they require retraining and may not preserve the accuracy of the teacher model.
Dimensionality reduction works by reducing the number of features or dimensions in the input data or the model layers. Dimensionality reduction can improve the computational efficiency and generalization ability of models, but it may also lose some information or introduce noise in the data or the model. Dimensionality reduction also requires retraining or modifying the model architecture.
Reference: [TensorFlow Model Optimization]
[TensorFlow Model Optimization Toolkit ― Post-Training Integer Quantization] [Model optimization methods to cut latency, adapt to new data]
B
Explanation:
Dynamic range quantization is a model optimization technique for reducing latency that reduces the numerical precision of the weights and activations of models. This technique can reduce the model size, memory usage, and inference time by up to 4x with negligible accuracy loss. Dynamic range quantization can be applied to a trained TensorFlow model without retraining, and it is suitable for mobile applications that require low latency and power consumption.
Weight pruning, model distillation, and dimensionality reduction are also model optimization techniques for reducing latency, but they have some limitations or drawbacks compared to dynamic range quantization:
Weight pruning works by removing parameters within a model that have only a minor impact on its predictions. Pruned models are the same size on disk, and have the same runtime latency, but can be compressed more effectively. This makes pruning a useful technique for reducing model download size, but not for reducing inference time.
Model distillation works by training a smaller and simpler model (student) to mimic the behavior of a larger and complex model (teacher). Distilled models can have lower latency and memory usage than the original models, but they require retraining and may not preserve the accuracy of the teacher model.
Dimensionality reduction works by reducing the number of features or dimensions in the input data or the model layers. Dimensionality reduction can improve the computational efficiency and generalization ability of models, but it may also lose some information or introduce noise in the data or the model. Dimensionality reduction also requires retraining or modifying the model architecture.
Reference: [TensorFlow Model Optimization]
[TensorFlow Model Optimization Toolkit ― Post-Training Integer Quantization] [Model optimization methods to cut latency, adapt to new data]
Question #19
You work at a leading healthcare firm developing state-of-the-art algorithms for various use cases You have unstructured textual data with custom labels You need to extract and classify various medical phrases with these labels.
What should you do?
- A . Use the Healthcare Natural Language API to extract medical entities.
- B . Use a BERT-based model to fine-tune a medical entity extraction model.
- C . Use AutoML Entity Extraction to train a medical entity extraction model.
- D . Use TensorFlow to build a custom medical entity extraction model.
Correct Answer: B
B
Explanation:
Medical entity extraction is a task that involves identifying and classifying medical terms or concepts from unstructured textual data, such as electronic health records, clinical notes, or research papers. Medical entity extraction can help with various use cases, such as information retrieval, knowledge discovery, decision support, and data analysis1.
One possible approach to perform medical entity extraction is to use a BERT-based model to fine-tune a medical entity extraction model. BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained language model that can capture the contextual information from both left and right sides of a given token2. BERT can be fine-tuned on a specific downstream task, such as medical entity extraction, by adding a task-specific layer on top of the pre-trained model and updating the model parameters with a small amount of labeled data3.
A BERT-based model can achieve high performance on medical entity extraction by leveraging the large-scale pre-training on general-domain corpora and the fine-tuning on domain-specific data.
For example, Nesterov and Umerenkov4 proposed a novel method of doing medical entity extraction from electronic health records as a single-step multi-label classification task by fine-tuning a transformer model pre-trained on a large EHR dataset. They showed that their model can achieve human-level quality for most frequent entities.
Reference: 1: Medical Named Entity Recognition from Un-labelled Medical Records based on Pre-trained Language Models and Domain Dictionary | Data Intelligence | MIT Press
2: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
3: Fine-tuning BERT for Medical Entity Extraction
4: Distantly supervised end-to-end medical entity extraction from electronic health records with human-level quality
B
Explanation:
Medical entity extraction is a task that involves identifying and classifying medical terms or concepts from unstructured textual data, such as electronic health records, clinical notes, or research papers. Medical entity extraction can help with various use cases, such as information retrieval, knowledge discovery, decision support, and data analysis1.
One possible approach to perform medical entity extraction is to use a BERT-based model to fine-tune a medical entity extraction model. BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained language model that can capture the contextual information from both left and right sides of a given token2. BERT can be fine-tuned on a specific downstream task, such as medical entity extraction, by adding a task-specific layer on top of the pre-trained model and updating the model parameters with a small amount of labeled data3.
A BERT-based model can achieve high performance on medical entity extraction by leveraging the large-scale pre-training on general-domain corpora and the fine-tuning on domain-specific data.
For example, Nesterov and Umerenkov4 proposed a novel method of doing medical entity extraction from electronic health records as a single-step multi-label classification task by fine-tuning a transformer model pre-trained on a large EHR dataset. They showed that their model can achieve human-level quality for most frequent entities.
Reference: 1: Medical Named Entity Recognition from Un-labelled Medical Records based on Pre-trained Language Models and Domain Dictionary | Data Intelligence | MIT Press
2: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
3: Fine-tuning BERT for Medical Entity Extraction
4: Distantly supervised end-to-end medical entity extraction from electronic health records with human-level quality
Question #20
You work for a hotel and have a dataset that contains customers’ written comments scanned from paper-based customer feedback forms which are stored as PDF files Every form has the same layout. You need to quickly predict an overall satisfaction score from the customer comments on each form.
How should you accomplish this task’?
- A . Use the Vision API to parse the text from each PDF file Use the Natural Language API analyzesentiment feature to infer overall satisfaction scores.
- B . Use the Vision API to parse the text from each PDF file Use the Natural Language API analyzeEntitysentiment feature to infer overall satisfaction scores.
- C . Uptrain a Document Al custom extractor to parse the text in the comments section of each PDF file. Use the Natural Language API analyze sentiment feature to infer overall satisfaction scores.
- D . Uptrain a Document Al custom extractor to parse the text in the comments section of each PDF file. Use the Natural Language API analyzeEntitySentiment feature to infer overall satisfaction scores.
Correct Answer: C
C
Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to “design, build, and productionalize ML models to solve business challenges using Google Cloud technologies”. Document AI2 is a document understanding platform that takes unstructured data from documents and transforms it into structured data, making it easier to understand, analyze, and consume. Document AI Workbench3 allows you to create custom extractors to parse the text in specific sections of your documents. Natural Language API4 is a service that provides natural language understanding technologies, such as sentiment analysis, entity analysis, and other text annotations. The analyzeSentiment feature5 inspects the given text and identifies the prevailing emotional opinion within the text, especially to determine a writer’s attitude as positive, negative, or neutral. Therefore, option C is the best way to accomplish the task of predicting an overall satisfaction score from the customer comments on each form. The other options are not relevant or optimal for this scenario.
Reference: Professional ML Engineer Exam Guide
Document AI
Document AI Workbench
Natural Language API
Sentiment analysis
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
C
Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to “design, build, and productionalize ML models to solve business challenges using Google Cloud technologies”. Document AI2 is a document understanding platform that takes unstructured data from documents and transforms it into structured data, making it easier to understand, analyze, and consume. Document AI Workbench3 allows you to create custom extractors to parse the text in specific sections of your documents. Natural Language API4 is a service that provides natural language understanding technologies, such as sentiment analysis, entity analysis, and other text annotations. The analyzeSentiment feature5 inspects the given text and identifies the prevailing emotional opinion within the text, especially to determine a writer’s attitude as positive, negative, or neutral. Therefore, option C is the best way to accomplish the task of predicting an overall satisfaction score from the customer comments on each form. The other options are not relevant or optimal for this scenario.
Reference: Professional ML Engineer Exam Guide
Document AI
Document AI Workbench
Natural Language API
Sentiment analysis
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions