Professional-Machine-Learning-Engineer Google Professional Machine Learning Engineer Free Practice Exam Questions (2026 Updated)

The best option to ensure real-time ingestion of the user activity data into BigQuery is to run a Dataflow streaming job to ingest the data into BigQuery. Dataflow is a fully managed service that can handle both batch and stream processing of data, and can integrate seamlessly with BigQuery and other Google Cloud services. Dataflow can also use Apache Beam as the programming model, which provides a unified and portable API for developing data pipelines. By using Dataflow, you can avoid the complexity and overhead of managing your own infrastructure, and focus on the logic and transformation of your data. Dataflow can also handle various types of data, such as structured, unstructured, or binary data, and can apply windowing, aggregation, and other operations on the data streams.

The other options are not optimal for the following reasons:

A. Configuring Pub/Sub to stream the data into BigQuery is not a good option, as Pub/Sub is a messaging service that can publish and subscribe to data streams, but cannot perform any transformation or processing on the data. Pub/Sub can be used as a source or a sink for Dataflow, but not as a standalone solution for ingesting data into BigQuery.

B. Running an Apache Spark streaming job on Dataproc to ingest the data into BigQuery is not a good option, as it requires setting up and managing your own cluster of virtual machines, which can increase the cost and complexity of your solution. Moreover, Apache Spark is not natively integrated with BigQuery, and requires using connectors or intermediate storage to write data to BigQuery, which can introduce latency and inefficiency.

D. Configuring Pub/Sub and a Dataflow streaming job to ingest the data into BigQuery is not a bad option, but it is not necessary, as Dataflow can directly read data from the mobile applications without using Pub/Sub as an intermediary. Using Pub/Sub can add an extra layer of abstraction and reliability, but it can also increase the cost and complexity of your solution, and introduce some delay in the data ingestion.

In the context of MLOps on Google Cloud and Vertex AI, the goal is to balance model performance with operational efficiency. Here is why Option D is the correct strategy for minimizing cost and effort while maintaining reliability:

Data Drift and Model Decay: In production environments, the distribution of live data often changes over time (a phenomenon known as Training-Serving Skew or Data Drift ). If the customer attributes in the real world no longer match the data the model was trained on, the model’s predictive power will degrade.

Vertex AI Model Monitoring: Vertex AI provides built-in tools to monitor for Feature Attribution Drift and Training-Serving Skew . By setting up alerts for these shifts, you implement " Performance-based " or " Condition-based " retraining. This is more cost-effective than retraining every week (Option C), which might use expensive compute resources to retrain a model that is still performing perfectly.

Why other options are incorrect:

Option A: Latency is an infrastructure/engineering metric, not a predictive quality metric. Retraining the model will not fix latency issues caused by high traffic; that would require scaling your prediction nodes.

Option B: While accuracy is important, waiting for a 10% drop on a new dataset often means the model has already been underperforming in production for some time. Furthermore, calculating accuracy requires " ground truth " (actual labels), which may not be available immediately for churn.

Option C: Retraining weekly regardless of performance leads to unnecessary compute costs and engineering overhead if the data hasn ' t changed significantly.

 Parallel interleave is a technique that can improve the performance of the input pipeline by reading and processing data from multiple files in parallel. This can reduce the idle time of the GPU and speed up the training process. Parallel interleave can be implemented using the tf.data.experimental.parallel_interleave () function in TensorFlow, which takes a map function that returns a dataset for each input element, and a cycle length that determines how many input elements are processed concurrently. Parallel interleave can also handle different file sizes and processing times by using a block length argument that controls how many consecutive elements are produced from each input element before switching to another input element. For more information about parallel interleave and how to use it, see the following references:

How to use parallel_inte rleave in TensorFlow

Better performance with the tf.data API

 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.  References :

Recommendation Systems

Content-Based Filtering

Collaborative Filtering

Hybrid Recommender Systems: A Systematic Literature Review

To create a Vertex AI Workbench environment for your team and limit access to other employees in your project, you should follow these steps:

Create a new service account and grant it the Vertex AI User role.  This role grants full access to all resources in Vertex AI, including creating and managing notebook instances 1 .

Grant the Service Account User role to each team member on the service account.  This role allow s the team members to impersonate the service account and use its permissions 2 .

Grant the Notebook Viewer role to each team member.  This role allows the team members to view and connect to the notebook instance, but not to modify or delete it 3 .

Provision a Vertex AI Workbench user-managed notebook instance that uses the new service account. This way, the notebook instance will run as the service account and only the team members who have the Service Account User and Notebook Viewer roles will be able to access it.

 Sentiment analysis is the process of identifying and extracting the emotions, opinions, and attitudes expressed in a text or speech. Sentiment analysis can help businesses understand their customers’ feedback, satisfaction, and preferences. There are different approaches to building a sentiment analysis tool, depending on the input data and the output format. Some of the common approaches are:

Extracting sentiment directly from the voice recordings: This approach involves using acoustic features, such as pitch, intensity, and prosody, to infer the sentiment of the speaker. This approach can capture the nuances and subtleties of the vocal expression, but it also requires a large and diverse dataset of labeled voice recordings, which may not be easily available or accessible. Moreover, this approach may not account for the semantic and contextual information of the speech, which can also affect the sentiment.

Converting the speech to text and building a model based on the words: This approach involves using automatic speech recognition (ASR) to transcribe the voice recordings into text, and then using lexical features, such as word frequency, polarity, and valence, to infer the sentiment of the text. This approach can leverage the existing text-based sentiment analysis models and tools, but it also introduces some challenges, such as the accuracy and reliability of the ASR system, the ambiguity and variability of the natural language, and the loss of the acoustic information of the speech.

Converting the speech to text and extracting sentiments based on the sentences: This approach involves using ASR to transcribe the voice recordings into text, and then using syntactic and semantic features, such as sentence structure, word order, and meaning, to infer the sentiment of the text. This approach can capture the higher-level and complex aspects of the natural language, such as negation, sarcasm, and irony, which can affect the sentiment. However, this approach also requires more sophisticated and advanced natural language processing techniques, such as parsing, dependency analysis, and semantic role labeling, which may not be readily available or easy to implement.

Converting the speech to text and extracting sentiment using syntactical analysis: This approach involves using ASR to transcribe the voice recordings into text, and then using syntactical analysis, such as part-of-speech tagging, phrase chunking, and constituency parsing, to infer the sentiment of the text. This approach can identify the grammatical and structural elements of the natural language, such as nouns, verbs, adjectives, and clauses, which can indicate the sentiment. However, this approach may not account for the pragmatic and contextual information of the speech, such as the speaker’s intention, tone, and situation, which can also influence the sentiment.

For the use case of building a sentiment analysis tool that predicts customer sentiment from recorded phone conversations, the best approach is to convert the speech to text and extract sentiments based on the sentences. This approach can balance the trade-offs between the accuracy, complexity, and feasibility of the sentiment analysis tool, while ensuring that the gender, age, and cultural differences of the customers who called the contact center do not impact any stage of the model development pipeline and results. This approach can also handle different types and levels of sentiment, such as polarity (positive, negative, or neutral), intensity (strong or weak), and emotion (anger, joy, sadness, etc.). Therefore, converting the speech to text and extracting sentiments based on the sentences is the best approach for this use case.

The best option for analyzing large and complex datasets while minimizing computational resources is to use a combination of BigQuery and Vertex AI Workbench. BigQuery is a serverless, scalable, and cost-effective data warehouse that can perform fast and interactive queries on petabytes of data. BigQuery can calculate descriptive statistics such as mean, median, and mode by using SQL functions such as AVG, PERCENTILE_CONT, and MODE. Vertex AI Workbench is a managed service that provides an integrated development environment for data science and machine learning. Vertex AI Workbench allows users to create and run Jupyter notebooks on Google Cloud, and access various tools and libraries for data visualization and statistical analysis. Vertex AI Workbench can connect to BigQuery and use the results of the queries to create time plots and run statistical tests for hypothesis testing. By using BigQuery and Vertex AI Workbench, users can leverage the power and flexibility of Google Cloud to perform exploratory data analysis on large and complex datasets.  References:

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

Google Cloud Professional Machine Learning Engineer Exam Guide , Section 1: Architecting low-code ML solutions, 1.1 Developing ML models by using BigQuery ML

Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 3: Data Engineering for ML, Section 3.2: BigQuery for ML

The best option for automating the retraining of your model by using minimal additional code when model feature values change, and minimizing the number of times that your model is retrained to reduce training costs, is to create a Vertex AI Model Monitoring job configured to monitor prediction drift, configure alert monitoring to publish a message to a Pub/Sub queue when a monitoring alert is detected, and use a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery. This option allows you to leverage the power and simplicity of Vertex AI, Pub/Sub, and Cloud Functions to monitor your model performance and retrain your model when needed. Vertex AI is a unified platform for building and deploying machine learning solutions on Google Cloud. Vertex AI can deploy a trained model to an online prediction endpoint, which can provide low-latency predictions for individual instances. Vertex AI can also provide various tools and services for data analysis, model development, model deployment, model monitoring, and model governance. A Vertex AI Model Monitoring job is a resource that can monitor the performance and quality of your deployed models on Vertex AI. A Vertex AI Model Monitoring job can help you detect and diagnose issues with your models, such as data drift, prediction drift, training/serving skew, or model staleness. Prediction drift is a type of model monitoring metric that measures the difference between the distributions of the predictions generated by the model on the training data and the predictions generated by the model on the online data. Prediction drift can indicate that the model performance is degrading, or that the online data is changing over time. By creating a Vertex AI Model Monitoring job configured to monitor prediction drift, you can track the changes in the model predictions, and compare them with the expected predictions. Alert monitoring is a feature of Vertex AI Model Monitoring that can notify you when a monitoring metric exceeds a predefined threshold. Alert monitoring can help you set up rules and conditions for triggering alerts, and choose the notification channel for receiving alerts. Pub/Sub is a service that can provide reliable and scalable messaging and event streaming on Google Cloud. Pub/Sub can help you publish and subscribe to messages, and deliver them to various Google Cloud services, such as Cloud Functions. A Pub/Sub queue is a resource that can hold messages that are published to a Pub/Sub topic. A Pub/Sub queue can help you store and manage messages, and ensure that they are delivered to the subscribers. By configuring alert monitoring to publish a message to a Pub/Sub queue when a monitoring alert is detected, you can send a notification to a Pub/Sub topic, and trigger a downstream action based on the alert. Cloud Functions is a service that can run your stateless code in response to events on Google Cloud. Cloud Functions can help you create and execute functions without provisioning or managing servers, and pay only for the resources you use. A Cloud Function is a resource that can execute a piece of code in response to an event, such as a Pub/Sub message. A Cloud Function can help you perform various tasks, such as data processing, data transformation, or data analysis. BigQuery is a service that can store and query large-scale data on Google Cloud. BigQuery can help you analyze your data by using SQL queries, and perform various tasks, such as data exploration, data transformation, or data visualization. BigQuery ML is a feature of BigQuery that can create and execute machine learning models in BigQuery by using SQL queries. BigQuery ML can help you build and train various types of models, such as linear regression, logistic regression, k-means clustering, matrix factorization, and deep neural networks. By using a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery, you can automate the retraining of your model by using minimal additional code when model feature values change. You can write a Cloud Function that listens to the Pub/Sub queue, and executes a SQL query to retrain your model in BigQuery ML when a prediction drift alert is received.  By retraining your model in BigQuery ML, you can update your model parameters and improve your model performance and accuracy 1 .

The other options are not as good as option C, for the following reasons:

Option A: Enabling request-response logging on Vertex AI Endpoints, scheduling a TensorFlow Data Validation job to monitor prediction drift, and executing model retraining if there is significant distance between the distributions would require more skills and steps than creating a Vertex AI Model Monitoring job configured to monitor prediction drift, configuring alert monitoring to publish a message to a Pub/Sub queue when a monitoring alert is detected, and using a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery. Request-response logging is a feature of Vertex AI Endpoints that can record the requests and responses that are sent to and from the online prediction endpoint. Request-response logging can help you collect and analyze the online prediction data, and troubleshoot any issues with your model. TensorFlow Data Validation is a tool that can analyze and validate your data for machine learning. TensorFlow Data Validation can help you explore, understand, and clean your data, and detect various data issues, such as data drift, data skew, or data anomalies. Prediction drift is a type of data issue that measures the difference between the distributions of the predictions generated by the model on the training data and the predictions generated by the model on the online data. Prediction drift can indicate that the model performance is degrading, or that the online data is changing over time. By enabling request-response logging on Vertex AI Endpoints, and scheduling a TensorFlow Data Validation job to monitor prediction drift, you can collect and analyze the online prediction data, and compare the distributions of the predictions. However, enabling request-response logging on Vertex AI Endpoints, scheduling a TensorFlow Data Validation job to monitor prediction drift, and executing model retraining if there is significant distance between the distributions would require more skills and steps than creating a Vertex AI Model Monitoring job configured to monitor prediction drift, configuring alert monitoring to publish a message to a Pub/Sub queue when a monitoring alert is detected, and using a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery. You would need to write code, enable and configure the request-response logging, create and run the TensorFlow Data Validation job, define and measure the distance between the distributions, and execute the model retraining.  Moreover, this option would not automate the retraining of your model, as you would need to manually check the prediction drift and trigger the retraining 2 .

Option B: Enabling request-response logging on Vertex AI Endpoints, scheduling a TensorFlow Data Validation job to monitor training/serving skew, and executing model retraining if there is significant distance between the distributions would not help you monitor the changes in the model feature values, and could cause errors or poor performance. Training/serving skew is a type of data issue that measures the difference between the distributions of the features used to train the model and the features used to serve the model. Training/serving skew can indicate that the model is not trained on the representative data, or that the data is changing over time. By enabling request-response logging on Vertex AI Endpoints, and scheduling a TensorFlow Data Validation job to monitor training/serving skew, you can collect and analyze the online prediction data, and compare the distributions of the features. However, enabling request-response logging on Vertex AI Endpoints, scheduling a TensorFlow Data Validation job to monitor training/serving skew, and executing model retraining if there is significant distance between the distributions would not help you monitor the changes in the model feature values, and could cause errors or poor performance. You would need to write code, enable and configure the request-response logging, create and run the TensorFlow Data Validation job, define and measure the distance between the distributions, and execute the model retraining.  Moreover, this option would not monitor the prediction drift, which is a more direct and relevant metric for measuring the model performance and quality 2 .

Option D: Creating a Vertex AI Model Monitoring job configured to monitor training/serving skew, configuring alert monitoring to publish a message to a Pub/Sub queue when a monitoring alert is detected, and using a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery would not help you monitor the changes in the model feature values, and could cause errors or poor performance. Training/serving skew is a type of data issue that measures the difference between the distributions of the features used to train the model and the features used to serve the model. Training/serving skew can indicate that the model is not trained on the representative data, or that the data is changing over time. By creating a Vertex AI Model Monitoring job configured to monitor training/serving skew, you can track the changes in the model features, and compare them with the expected features. However, creating a Vertex AI Model Monitoring job configured to monitor training/serving skew, configuring alert monitoring to publish a message to a Pub/Sub queue when a monitoring alert is detected, and using a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery would not help you monitor the changes in the model feature values, and could cause errors or poor performance. You would need to write code, create and configure the Vertex AI Model Monitoring job, configure the alert monitoring, create and configure the Pub/Sub queue, and write a Cloud Function to trigger the retraining.  Moreover, this option would not monitor the prediction drift, which is a more direct and relevant metric for measuring t he model performance and quality 1 .

Cloud Vision API is a service that allows you to analyze images using pre-trained machine learning models 1 .  You can use Cloud Vision API to perform various tasks, such as face detection, text extraction, logo recognition, and object localization 1 .  Object localization is a feature that allows you to detect multiple objects in an image and draw bounding boxes around them 2 .  You can also get the labels and confidence scores for each detected object 2 .

By sending user-submitted images to the Cloud Vision API, you can use object localization to identify all objects in the image and compare the results against a list of animals.  You can use the  OBJECT_LOCALIZATION  feature type in the  A nnotateImageRequest  to request object localization 3 . You can then use the  localizedObjectAnnotations  field in the  AnnotateImageResponse  to get the list of detected objects, their labels, and their confidence scores. You can compare the labels with a predefined list of animals, such as dogs, cats, birds, etc., and determine whether the image has an animal or not.

This option is the best for your scenario, because it allows you to automatically and in near real time detect whether each uploaded photo has an animal, without requiring any manual labeling, model training, or model deployment. You can also prioritize time and minimize cost of your application development and deployment, as you can use the Cloud Vision API as a ready-to-use service, without needing any machine learning expertise or infrastructure.

The other options are not suitable for your scenario, because they either require manual labeling, model training, or model deployment, which would increase the time and cost of your application development and deployment, or they use object detection models, which are more complex and computationally expensive than object localization models, and are not necessary for your simple task of detecting whether an image has an animal or not.

According to the web search results, Vertex AI is a unified platform for machine learning development and deployment.  Vertex AI offers various services and tools for building, m anaging, and serving machine learning models 1 .  Vertex AI allows you to deploy your models to endpoints for online prediction, and configure the compute resources and autoscaling options for your deployed models 2 . Autoscaling with Vertex AI endpoints is (by default) based on the CPU utilization across all cores of the machine type you have specified. The default threshold of 60% represents 60% on all cores.  For example, for a 4 core machine, that means you need 240% utilization to trigger autoscaling 3 . Therefore, if you discover that the endpoint does not autoscale as expected when receiving multiple requests, you might need to decrease the CPU utilization target in the autoscaling configurations. This way, you can lower the threshold for triggering autoscaling and allocate more resources to handle the prediction requests. Therefore, option D is the best way to solve the problem for the given use case. The other options are not relevant or optimal for this scenario.  References :

Vertex AI

Deploy a model to an endpoint

Vertex AI endpoint doesn’t scale up / down

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

According to the official exam guide 1 , one of the skills assessed in the exam is to “design, build, and productionalize ML models to solve business challenges using Google Cloud technologies”.  Cloud Data Loss Prevention (DLP) API 2  is a service that provides programmatic access to a powerful detection engine for personally identifiable information and other privacy-sensitive data in unstructured data streams, such as text blocks and images. Cloud DLP API helps you discover, classify, and protect your sensitive data by using techniques such as de-identification, masking, tokenization, and bucketing. You can use Cloud DLP API to de-identify the PII data before performing data exploration and preprocessing, and retain the data utility for ML purposes. Therefore, option A is the best way to perform data exploration and preprocessing while ensuring the security and privacy of sensitive fields. The other options are not relevant or optimal for this scenario.  References :

Professional ML Engineer Exam Guide

Cloud Data Loss Prevention (DLP) API

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

Option A is correct because importing the TensorFlow model with BigQuery ML, and running the ml.predict function is the easiest way to execute a batch prediction on a large BigQuery table with a custom TensorFlow model, and store the predicted results in another BigQuery table.  BigQuery ML allows you to import TensorFlow models that are stored in Cloud Storage, and use them for prediction with SQL queries 1 .  The ml.predict function returns a table with the predicted values, which can be saved to another BigQuery table 2 .

Option B is incorrect because using the TensorFlow BigQuery reader to load the data, and using the BigQuery API to write the results to BigQuery requires more effort to build the inference pipeline than option A.  The TensorFlow BigQuery reader is a way to read data from BigQuery into TensorFlow datasets, which can be used for training or prediction 3 .  However, this option also requires writing code to load the TensorFlow model, run the prediction, and use the BigQuery API to write the results back to BigQuery 4 .

Option C is incorrect because creating a Dataflow pipeline to convert the data in BigQuery to TFRecords, running a batch inference on Vertex AI Prediction, and writing the results to BigQuery requires more effort to build the inference pipeline than option A.  Dataflow is a service for creating and running data processing pipelines, such as ETL (extract, transform, load ) or batch processing 5 . Vertex AI Prediction is a service for deploying and serving ML models for online or batch prediction. However, this option also requires writing code to create the Dataflow pipeline, convert the data to TFRecords, run the batch inference, and write the results to BigQuery.

Option D is incorrect because loading the TensorFlow SavedModel in a Dataflow pipeline, using the BigQuery I/O connector with a custom function to perform the inference within the pipeline, and writing the results to BigQuery requires more effort to build the inference pipeline than option A. The BigQuery I/O connector is a way to read and write data from BigQuery within a Dataflow pipeline. However, this option also requires writing code to load the TensorFlow SavedModel, create the custom function for inference, and write the results to BigQuery.

 Using bfloat16 instead of float32 can reduce the memory usage and training time of the model, while having minimal impact on the accuracy. Bfloat16 is a 16-bit floating-point format that preserves the range of 32-bit floating-point numbers, but reduces the precision from 24 bits to 8 bits. This means that bfloat16 can store the same magnitude of numbers as float32, but with less detail. Bfloat16 is supported by TPUs and some GPUs, and can be used as a drop-in replacement for float32 in most cases. Bfloat16 can also improve the numerical stability of the model, as it reduces the risk of overflow and underflow errors.

Reducing the global batch size, the number of layers, or the dimensions of the images can also reduce the memory usage and training time of the model, but they can also affect the model’s accuracy and performance. Reducing the global batch size can make the model less stable and converge slower, as it reduces the amount of information available for each gradient update. Reducing the number of layers can make the model less expressive and powerful, as it reduces the depth and complexity of the network. Reducing the dimensions of the images can make the model less accurate and robust, as it reduces the resolution and quality of the input data.  References:

Bfloat16: The secret to high performance on Cloud TPUs

Bfloat16 floating-point format

How does Batch Size impact your model learning

Option A is incorrect because reinforcement learning is not a suitable approach to build a model that identifies defects in products based on images of the product taken at the end of the assembly line.  Reinforcement learning is a type of machine learning that learns from its own actions and rewards, rather than from labeled data or explicit feedback 1 .  Reinforcement learning is more suitable for problems that involve sequentia l decision making, such as games, robotics, or control systems 1 . However, defect detection is a problem that involves image classification or segmentation, which requires supervised learning, not reinforcement learning.

Option B is incorrect because a recommender system is not a relevant approach to build a model that identifies defects in products based on images of the product taken at the end of the assembly line.  A recommender system is a system that suggests items or actions to users based on their preferences, beha vior, or context 2 .  A recommender system is more suitable for problems that involve personalization, such as e-commerce, entertainment, or social media 2 . However, defect detection is a problem that involves image classification or segmentation, which requires supervised learning, not recommender system.

Option C is incorrect because recurrent neural networks (RNN) are not the most efficient approach to build a model that identifies defects in products based on images of the product taken at the end of the assembly line.  RNNs are a type of neural networks that can process sequential data, such as text, speech, or video, by maintaining a hidden state that captures the temporal dependencies 3 .  RNNs are more suitable for problems that involve n atural language processing, speech recognition, or video analysis 3 . However, defect detection is a problem that involves image classification or segmentation, which does not require temporal dependencies, but rather spatial dependencies.  Moreover, RNNs are computationally expensive and prone to vanishing or exploding gradients 4 .

Option D is correct because convolutional neural networks (CNN) are the best approach to build a model that identifies defects in products based on images of the product taken at the end of the assembly line.  CNNs are a type of neural networks that can process image data, by applying convolutional filters that extract local features and reduce the dimensi onality of the data 5 .  CNNs are more suitable for problems that involve image classification, object detection, or segmentation 5 .  CN Ns can preprocess the images with lower computation to quickly extract features of defects in products, by using techniques such as pooling, dropout, or batch normalization 6 .

 Image classification is a task where the model assigns a label to an image based on its content, such as “stop sign” or " speed limit " 1 .  Object detection is a task where the model locates and identifies multiple objects in an image, and draws bounding boxes around them 2 . Since your dataset consists of images that were cropped to show one out of ten different traffic signs, you are dealing with an image classification problem, not an object detection problem. Therefore, you need to train a model for image classification, not object detection.

Vertex AI AutoML is a service that allows you to train and deploy high-qual ity ML models with minimal effort and machine learning expertise 3 .  You can use Vertex AI AutoML to train a model for image classification by uploading your images and labels to a Vertex AI dataset, and then launching an Au toML training job 4 .  However, Vertex AI AutoML does not allow you to tune the model during each training run, as it automatically selects the best m odel architecture and hyperparameters for your data 4 .

Vertex AI custom tr aining is a service that allows you to train and deploy your own custom ML models using your own code and frameworks 5 . You can use Vertex AI custom training to train a model for image classification by writing your own model training code, such as using TensorFlow or PyTorch, and then creating and running a custom training job. Vertex AI custom training allows you to tune the model during each training run, as you can specify the model architecture and hyperparameters in your code, and use Vertex AI Hyperparameter Tuning to optimize them .

Therefore, the best option for your scenario is to develop the model training code for image classification and train a model by using Vertex AI custom training.

 The best option for continuing experimenting and iterating on your pipeline to improve model performance, using Cloud Build for CI/CD, and deploying new pipelines into production quickly and easily, is to set up a CI/CD pipeline that builds and tests your source code and then deploys built artifacts into a pre-production environment. After a successful pipeline run in the pre-production environment, deploy the pipeline to production. This option allows you to leverage the power and simplicity of Cloud Build to automate, monitor, and manage your pipeline development and deployment workflow. Cloud Build is a service that can create and run continuous integration and continuous delivery (CI/CD) pipelines on Google Cloud. Cloud Build can build your source code, run unit tests, and deploy built artifacts to various Google Cloud services, such as Vertex AI Pipelines, Vertex AI Endpoints, and Artifact Registry. A CI/CD pipeline is a workflow that can automate the process of building, testing, and deploying software. A CI/CD pipeline can help you improve the quality and reliability of your software, accelerate the development and delivery cycle, and reduce the manual effort and errors. A pre-production environment is an environment that can simulate the production environment, but is isolated from the real users and data. A pre-production environment can help you test and validate your software before deploying it to production, and catch any bugs or issues that may affect the user experience or the system performance. By setting up a CI/CD pipeline that builds and tests your source code and then deploys built artifacts into a pre-production environment, you can ensure that your pipeline code is consistent and error-free, and that your pipeline artifacts are compatible and functional. After a successful pipeline run in the pre-production environment, you can deploy the pipeline to production, which is the environment where your software is accessible and usable by the real users and data.  By deploying the pipeline to production after a successful pipeline run in the pre-production environment, you can minimize the chance that the new pipeline implementations will break in production, and ensure that your software meets the user expectations and requirements 1 .

The other options are not as good as option C, for the following reasons:

Option A: Setting up a CI/CD pipeline that builds and tests your source code, and if the tests are successful, using the Google Cloud console to upload the built container to Artifact Registry and upload the compiled pipeline to Vertex AI Pipelines would not allow you to deploy new pipelines into production quickly and easily, and could increase the manual effort and errors. The Google Cloud console is a web-based user interface that can help you access and manage various Google Cloud services, such as Artifact Registry and Vertex AI Pipelines. Artifact Registry is a service that can store and manage your container images and other artifacts on Google Cloud. Artifact Registry can help you upload and organize your container images, and track the image versions and metadata. Vertex AI Pipelines is a service that can orchestrate machine learning workflows using Vertex AI. Vertex AI Pipelines can run preprocessing and training steps on custom Docker images, and evaluate, deploy, and monitor the machine learning model. However, setting up a CI/CD pipeline that builds and tests your source code, and if the tests are successful, using the Google Cloud console to upload the built container to Artifact Registry and upload the compiled pipeline to Vertex AI Pipelines would not allow you to deploy new pipelines into production quickly and easily, and could increase the manual effort and errors. You would need to write code, create and run the CI/CD pipeline, use the Google Cloud console to upload the built container to Artifact Registry, and use the Google Cloud console to upload the compiled pipeline to Vertex AI Pipelines.  Moreover, this option would not use a pre-production environment to test and validate your pipeline before deploying it to production, which could increase the chance that the new pipeline implemen tations will break in production 1 .

Option B: Setting up a CI/CD pipeline that builds your source code and then deploys built artifacts into a pre-production environment, running unit tests in the pre-production environment, and if the tests are successful, deploying the pipeline to production would not allow you to test and validate your pipeline before deploying it to production, and could cause errors or poor performance. A unit test is a type of test that can verify the functionality and correctness of a small and isolated unit of code, such as a function or a class. A unit test can help you debug and improve your code quality, and catch any bugs or issues that may affect the code logic or output. However, setting up a CI/CD pipeline that builds your source code and then deploys built artifacts into a pre-production environment, running unit tests in the pre-production environment, and if the tests are successful, deploying the pipeline to production would not allow you to test and validate your pipeline before deploying it to production, and could cause errors or poor performance. You would need to write code, create and run the CI/CD pipeline, deploy the built artifacts to the pre-production environment, run the unit tests in the pre-production environment, and deploy the pipeline to production.  Moreover, this option would not run the pipeline in the pre-production environme nt, which could prevent you from testing and validating the pipeline functionality and compatibility, and catching any bugs or issues that may affect the pipeline workflow or output 1 .

Option D: Setting up a CI/CD pipeline that builds and tests your source code and then deploys built artifacts into a pre-production environment, after a successful pipeline run in the pre-production environment, rebuilding the source code, and deploying the artifacts to production would not allow you to deploy new pipelines into production quickly and easily, and could increase the complexity and cost of the pipeline development and deployment. Rebuilding the source code is a process that can recompile and repackage the source code into executable artifacts, such as container images and pipeline files. Rebuilding the source code can help you incorporate any changes or updates that may have occurred in the source code, and ensure that the artifacts are consistent and up-to-date. However, setting up a CI/CD pipeline that builds and tests your source code and then deploys built artifacts into a pre-production environment, after a successful pipeline run in the pre-production environment, rebuilding the source code, and deploying the artifacts to production would not allow you to deploy new pipelines into production quickly and easily, and could increase the complexity and cost of the pipeline development and deployment. You would need to write code, create and run the CI/CD pipeline, deploy the built artifacts to the pre-production environment, run the pipeline in the pre-production environment, rebuild the source code, and deploy the artifacts to production.  Moreover, this option would increase the pipeline development and deployment time, as rebuilding the source code can be a time-consuming and resource-intensive process 1 .

A recommender system is a type of machine learning system that suggests relevant items to users based on their preferences and behavior.  Recommender systems are widely used in e-commerce, media, and enterta inment industries to enhance user experience and increase revenue 1

There are different types of recommender systems that use different filtering methods to generate recommendations. The most common types are:

Content-based filtering: This method uses the features of the items and the users to find the similarity between them.  For example, a content-based recommender system for movies may use the genre, director, cast, and ratings of the movies, and the preferences, demographics, and history of the users, to recommend movies that are similar to the ones the user liked before 2

Collaborative filtering: This method uses the feedback and ratings of the users to find the similarity between them and the items.  For example, a collaborative filtering recommender system for books may use the ratings of the users for different books, and recommend books that are liked by other users who have similar ratings to the target user 3

Hybrid method: This method combines content-based and collaborative filtering methods to overcome the limitations of each method and improve the accuracy and diversity of the recommendations.  For example, a hybrid recommender system for music may use both the features of the songs and the artists, and the ratings and listening habits of the users, to recommend songs that match the user’s taste and preferences 4

Deep learning-based: This method uses deep neural networks to learn complex and non-linear patterns from the data and generate recommendations. Deep learning-based recommender systems can handle large-scale and high-dimensional data, and incorporate various types of information, such as text, images, audio, and video. For example, a deep learning-based recommender system for fashion may use the images and descriptions of the products, and the profiles and feedback of the users, to recommend products that suit the user’s style and preferences.

For the use case of building a model that will recommend new products to the user based on their purchase behavior and similarity with other users, the best option is to build a collaborative-based filtering model. This is because collaborative filtering can leverage the implicit feedback and ratings of the users to find the items that are most likely to interest them.  Collaborative filtering can also help discover new products that the user may not be aware of, and in crease the diversity and serendipity of the recommendations 3

The other options are not as suitable for this use case. Building a classification model or a regression model using the features as predictors is not a good idea, as these models are not designed for recommendation tasks, and may not capture the preferences and behavior of the users. Building a knowledge-based filtering model is not relevant, as this method uses the explicit knowledge and requirements of the users to find the items that meet their criteria, and does not rely on the purchase behavior or similarity with other users.