Best Google Professional-Machine-Learning-Engineer Exam Practice Material Updated on Nov 08, 2026 [Q71-Q95]

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Best Google Professional-Machine-Learning-Engineer Exam Practice Material Updated on Nov 08, 2026

New Professional-Machine-Learning-Engineer Actual Exam Dumps, Google Practice Test

Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:

Section	Objectives
Topic 1: ML pipeline automation and orchestration	– Pipeline design 1. Use Vertex AI Pipelines 2. Build end-to-end ML pipelines
Topic 2: Data preparation and processing	– Data ingestion and pipelines 1. Use BigQuery and data processing services 2. Build data pipelines for training and serving – Feature engineering 1. Transform and preprocess datasets 2. Feature selection and representation techniques
Topic 3: Designing ML solutions	– ML architecture design 1. Design scalable ML systems on GCP 2. Select appropriate ML models and approaches – Framing ML problems 1. Translate business problems into ML tasks 2. Define success metrics and evaluation criteria
Topic 4: ML model development	– Model training and tuning 1. Train models using TensorFlow / Vertex AI 2. Hyperparameter tuning and optimization – Evaluation 1. Evaluate model performance metrics 2. Model validation strategies
Topic 5: Deployment and operations	– Monitoring and maintenance 1. Monitor model drift and performance 2. Retraining and lifecycle management – Model deployment 1. Deploy models using Vertex AI endpoints 2. Batch and online prediction systems

Q71. You are pre-training a large language model on Google Cloud. This model includes custom TensorFlow operations in the training loop Model training will use a large batch size, and you expect training to take several weeks You need to configure a training architecture that minimizes both training time and compute costs What should you do?

Q72. You need to develop a custom TensorRow model that will be used for online predictions. The training data is stored in BigQuery. You need to apply instance-level data transformations to the data for model training and serving. You want to use the same preprocessing routine during model training and serving. How should you configure the preprocessing routine?

Create a BigQuery script to preprocess the data, and write the result to another BigQuery table.

Create a pipeline in Vertex Al Pipelines to read the data from BigQuery and preprocess it using a custom preprocessing component.

Create a preprocessing function that reads and transforms the data from BigQuery Create a Vertex Al custom prediction routine that calls the preprocessing function at serving time.

Create an Apache Beam pipeline to read the data from BigQuery and preprocess it by using TensorFlow Transform and Dataflow.

Q73. You are an ML engineer at a global car manufacture. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?

Thee individual features: binned latitude, binned longitude, and one-hot encoded car type.

One feature obtained as an element-wise product between latitude, longitude, and car type.

One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type.

Two feature crosses as an element-wise product: the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type.

Q74. You need to deploy a scikit-learn classification model to production. The model must be able to serve requests 24/7 and you expect millions of requests per second to the production application from 8 am to 7 pm. You need to minimize the cost of deployment What should you do?

Deploy an online Vertex Al prediction endpoint Set the max replica count to 1

Deploy an online Vertex Al prediction endpoint Set the max replica count to 100

Deploy an online Vertex Al prediction endpoint with one GPU per replica Set the max replica count to 1.

Deploy an online Vertex Al prediction endpoint with one GPU per replica Set the max replica count to 100.

The best option for deploying a scikit-learn classification model to production is to deploy an online Vertex AI prediction endpoint and set the max replica count to 100. This option allows you to leverage the power and scalability of Google Cloud to serve requests 24/7 and handle millions of requests per second. Vertex AI is a unified platform for building and deploying machine learning solutions on Google Cloud. Vertex AI can deploy a trained scikit-learn model to an online prediction endpoint, which can provide low-latency predictions for individual instances. An online prediction endpoint consists of one or more replicas, which are copies of the model that run on virtual machines. The max replica count is a parameter that determines the maximum number of replicas that can be created for the endpoint. By setting the max replica count to 100, you can enable the endpoint to scale up to 100 replicas when the traffic increases, and scale down to zero replicas when the traffic decreases. This can help minimize the cost of deployment, as you only pay for the resources that you use. Moreover, you can use the autoscaling algorithm option to optimize the scaling behavior of the endpoint based on the latency and utilization metrics1.
The other options are not as good as option B, for the following reasons:
Option A: Deploying an online Vertex AI prediction endpoint and setting the max replica count to 1 would not be able to serve requests 24/7 and handle millions of requests per second. Setting the max replica count to 1 would limit the endpoint to only one replica, which can cause performance issues and service disruptions when the traffic increases. Moreover, setting the max replica count to 1 would prevent the endpoint from scaling down to zero replicas when the traffic decreases, which can increase the cost of deployment, as you pay for the resources that you do not use1.
Option C: Deploying an online Vertex AI prediction endpoint with one GPU per replica and setting the max replica count to 1 would not be able to serve requests 24/7 and handle millions of requests per second, and would increase the cost of deployment. Adding a GPU to each replica would increase the computational power of the endpoint, but it would also increase the cost of deployment, as GPUs are more expensive than CPUs. Moreover, setting the max replica count to 1 would limit the endpoint to only one replica, which can cause performance issues and service disruptions when the traffic increases, and prevent the endpoint from scaling down to zero replicas when the traffic decreases1. Furthermore, scikit-learn models do not benefit from GPUs, as scikit-learn is not optimized for GPU acceleration2.
Option D: Deploying an online Vertex AI prediction endpoint with one GPU per replica and setting the max replica count to 100 would be able to serve requests 24/7 and handle millions of requests per second, but it would increase the cost of deployment. Adding a GPU to each replica would increase the computational power of the endpoint, but it would also increase the cost of deployment, as GPUs are more expensive than CPUs. Setting the max replica count to 100 would enable the endpoint to scale up to 100 replicas when the traffic increases, and scale down to zero replicas when the traffic decreases, which can help minimize the cost of deployment. However, scikit-learn models do not benefit from GPUs, as scikit-learn is not optimized for GPU acceleration2. Therefore, using GPUs for scikit-learn models would be unnecessary and wasteful.
Reference:
Preparing for Google Cloud Certification: Machine Learning Engineer, Course 3: Production ML Systems, Week 2: Serving ML Predictions Google Cloud Professional Machine Learning Engineer Exam Guide, Section 3: Scaling ML models in production, 3.1 Deploying ML models to production Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 6: Production ML Systems, Section 6.2: Serving ML Predictions Online prediction Scaling online prediction scikit-learn FAQ

Q75. You are training a Resnet model on Al Platform using TPUs to visually categorize types of defects in automobile engines. You capture the training profile using the Cloud TPU profiler plugin and observe that it is highly input-bound. You want to reduce the bottleneck and speed up your model training process. Which modifications should you make to the tf .data dataset?
Choose 2 answers

Use the interleave option for reading data

Reduce the value of the repeat parameter

Increase the buffer size for the shuffle option.

Set the prefetch option equal to the training batch size

Decrease the batch size argument in your transformation

Q76. Your company’s business stakeholders want to understand the factors driving customer churn to inform their business strategy. You need to build a customer churn prediction model that prioritizes simple interpretability of your model’s results. You need to choose the ML framework and modeling technique that will explain which features led to the prediction. What should you do?

Build a TensorFlow deep neural network (DNN) model, and use SHAP values for feature importance analysis.

Build a PyTorch long short-term memory (LSTM) network, and use attention mechanisms for interpretability.

Build a logistic regression model in scikit-learn, and interpret the model’s output coefficients to understand feature impact.

Build a linear regression model in scikit-learn, and interpret the model’s standardized coefficients to understand feature impact.

Q77. You trained a text classification model. You have the following SignatureDefs:

What is the correct way to write the predict request?

data = json.dumps({“signature_name”: “serving_default’ “instances”: [fab’, ‘be1, ‘cd’]]})

data = json dumps({“signature_name”: “serving_default”! “instances”: [[‘a’, ‘b’, “c”, ‘d’, ‘e’, ‘f’]]})

data = json.dumps({“signature_name”: “serving_default, “instances”: [[‘a’, ‘b ‘c’1, [d ‘e T]]})

data = json dumps({“signature_name”: f,serving_default”, “instances”: [[‘a’, ‘b’], [c ‘d’], [‘e T]]})

Q78. You are working with a dataset that contains customer transactions. You need to build an ML model to predict customer purchase behavior You plan to develop the model in BigQuery ML, and export it to Cloud Storage for online prediction You notice that the input data contains a few categorical features, including product category and payment method You want to deploy the model as quickly as possible. What should you do?

Use the transform clause with the ML. ONE_HOT_ENCODER function on the categorical features at model creation and select the categorical and non-categorical features.

Use the ML. ONE_HOT_ENCODER function on the categorical features, and select the encoded categorical features and non-categorical features as inputs to create your model.

Use the create model statement and select the categorical and non-categorical features.

Use the ML. ONE_HOT_ENCODER function on the categorical features, and select the encoded categorical features and non-categorical features as inputs to create your model.

The best option for building an ML model to predict customer purchase behavior in BigQuery ML is to use the transform clause with the ML.ONE_HOT_ENCODER function on the categorical features at model creation and select the categorical and non-categorical features. This option allows you to encode the categorical features as one-hot vectors, which are binary vectors that have only one non-zero element. One-hot encoding is a common technique for handling categorical features in ML models, as it can reduce the dimensionality and sparsity of the data, and avoid the ordinality problem that arises when using numerical labels for categorical values1. The transform clause is a feature of BigQuery ML that lets you apply SQL expressions to transform the input data at model creation time. The transform clause can perform feature engineering, such as one-hot encoding, on the fly, without requiring you to create and store a new table with the transformed data2. By using the transform clause with the ML.ONE_HOT_ENCODER function, you can create and train an ML model in BigQuery ML with a single SQL statement, and export it to Cloud Storage for online prediction.
The other options are not as good as option A, for the following reasons:
Option B: Using the ML.ONE_HOT_ENCODER function on the categorical features, and selecting the encoded categorical features and non-categorical features as inputs to create your model, would require more steps and storage than using the transform clause. The ML.ONE_HOT_ENCODER function is a BigQuery ML function that returns a one-hot encoded vector for a given categorical value. However, using this function alone would not apply the one-hot encoding to the input data at model creation time. You would need to create a new table with the encoded features, and use that table as the input to create your model. This would incur additional storage costs and reduce the performance of the queries.
Option C: Using the create model statement and selecting the categorical and non-categorical features, would not handle the categorical features properly and could result in a poor model performance. The create model statement is a BigQuery ML statement that creates and trains an ML model from a SQL query. However, if the input data contains categorical features, you need to encode them as one-hot vectors or use the category_count option to specify the number of categories for each feature. Otherwise, BigQuery ML would treat the categorical features as numerical values, which can introduce bias and noise into the model3.
Option D: Using the ML.ONE_HOT_ENCODER function on the categorical features, and selecting the encoded categorical features and non-categorical features as inputs to create your model, is the same as option B, and has the same drawbacks.
Reference:
Preparing for Google Cloud Certification: Machine Learning Engineer, Course 2: Data Engineering for ML on Google Cloud, Week 2: Feature Engineering 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 One-hot encoding Using the TRANSFORM clause for feature engineering Creating a model ML.ONE_HOT_ENCODER function

Q79. You are investigating the root cause of a misclassification error made by one of your models. You used Vertex Al Pipelines to tram and deploy the model. The pipeline reads data from BigQuery. creates a copy of the data in Cloud Storage in TFRecord format trains the model in Vertex Al Training on that copy, and deploys the model to a Vertex Al endpoint. You have identified the specific version of that model that misclassified: and you need to recover the data this model was trained on. How should you find that copy of the data’?

Use Vertex Al Feature Store Modify the pipeline to use the feature store; and ensure that all training data is stored in it Search the feature store for the data used for the training.

Use the lineage feature of Vertex Al Metadata to find the model artifact Determine the version of the model and identify the step that creates the data copy, and search in the metadata for its location.

Use the logging features in the Vertex Al endpoint to determine the timestamp of the models deployment Find the pipeline run at that timestamp Identify the step that creates the data copy; and search in the logs for its location.

Find the job ID in Vertex Al Training corresponding to the training for the model Search in the logs of that job for the data used for the training.

* Option A is not the best answer because it requires modifying the pipeline to use the Vertex AI Feature Store, which may not be feasible or necessary for recovering the data that the model was trained on. The Vertex AI Feature Store is a service that helps you manage, store, and serve feature values for your machine learning models1, but it is not designed for storing the raw data or the TFRecord files.
* Option B is the best answer because it leverages the lineage feature of Vertex AI Metadata, which is a service that helps you track and manage the metadata of your machine learning workflows, such as datasets, models, metrics, and parameters2. The lineage feature allows you to view the relationships and dependencies among the artifacts and executions in your pipeline, and trace back the origin and history of any artifact3. By using the lineage feature, you can find the model artifact, determine the version of the model, identify the step that creates the data copy, and search in the metadata for its location.
* Option C is not the best answer because it relies on the logging features in the Vertex AI endpoint, which may not be accurate or reliable for finding the data copy. The logging features in the Vertex AI endpoint help you monitor and troubleshoot the online predictions made by your deployed models, but they do not provide information about the training data or the pipeline steps4. Moreover, the timestamp of the model deployment may not match the timestamp of the pipeline run, as there may be delays or errors in the deployment process.
* Option D is not the best answer because it requires finding the job ID in Vertex AI Training, which may not be easy or straightforward. Vertex AI Training is a service that helps you train your custom models on Google Cloud, but it does not provide a direct way to link the training job to the model version or the pipeline run. Moreover, searching in the logs of the job may not reveal the location of the data copy, as the logs may only contain information about the training process and the metrics.
References:
* 1: Introduction to Vertex AI Feature Store | Vertex AI | Google Cloud
* 2: Introduction to Vertex AI Metadata | Vertex AI | Google Cloud
* 3: View lineage for ML workflows | Vertex AI | Google Cloud
* 4: Monitor online predictions | Vertex AI | Google Cloud
* [5]: Train custom models | Vertex AI | Google Cloud

Q80. You work for a bank. You have created a custom model to predict whether a loan application should be flagged for human review. The input features are stored in a BigQuery table. The model is performing well and you plan to deploy it to production. Due to compliance requirements the model must provide explanations for each prediction. You want to add this functionality to your model code with minimal effort and provide explanations that are as accurate as possible What should you do?

Create an AutoML tabular model by using the BigQuery data with integrated Vertex Explainable Al.

Create a BigQuery ML deep neural network model, and use the ML. EXPLAIN_PREDICT method with the num_integral_steps parameter.

Upload the custom model to Vertex Al Model Registry and configure feature-based attribution by using sampled Shapley with input baselines.

Update the custom serving container to include sampled Shapley-based explanations in the prediction outputs.

Q81. You are developing a custom image classification model in Python. You plan to run your training application on Vertex Al Your input dataset contains several hundred thousand small images You need to determine how to store and access the images for training. You want to maximize data throughput and minimize training time while reducing the amount of additional code. What should you do?

Store image files in Cloud Storage and access them directly.

Store image files in Cloud Storage and access them by using serialized records.

Store image files in Cloud Filestore, and access them by using serialized records.

Store image files in Cloud Filestore and access them directly by using an NFS mount point.

Q82. You work at a marketing research firm. Your team’s current project involves analyzing a large volume of customer surveys. Your team decides to use LLMs in Model Garden and track model training artifacts. You need to determine how to effectively share notebooks within your team and how to orchestrate the ML workflow. You want to use Google-managed services as much as possible. What should you do?

Develop your code in Agent Platform Colab Enterprise, and orchestrate your ML workflow by using Agent Platform Pipelines.

Develop your code in Agent Platform Colab Enterprise, and orchestrate your ML workflow by using Cloud Workflows.

Develop your code in Agent Platform Workbench, and orchestrate your ML workflow by using Cloud Build.

Develop your code in Agent Platform Workbench, and orchestrate your ML workflow by using Agent Platform Pipelines.

Q83. You are an AI engineer with an apparel retail company. The sales team has observed seasonal sales patterns over the past 5-6 years. The sales team analyzes and visualizes the weekly sales data stored in CSV files. You have been asked to estimate weekly sales for future seasons to optimize inventory and personnel workloads. You want to use the most efficient approach. What should you do?

Upload the files into Cloud Storage. Use Python to preprocess and load the tabular data into BigQuery. Use time series forecasting models to predict weekly sales.

Upload the files into Cloud Storage. Use Python to preprocess and load the tabular data into BigQuery. Train a logistic regression model by using BigQuery ML to predict each product’s weekly sales as one of three categories: high, medium, or low.

Load the files into BigQuery. Preprocess data by using BigQuery SQL. Connect BigQuery to Looker. Create a Looker dashboard that shows weekly sales trends in real time and can slice and dice the data based on relevant filters.

Create a custom conversational application using Vertex AI Agent Builder. Include code that enables file upload functionality, and upload the files. Use few-shot prompting and retrieval- augmented generation (RAG) to predict future sales trends by using the Gemini large language model (LLM).

Q84. You are training and deploying updated versions of a regression model with tabular data by using Vertex AI Pipelines, Vertex AI Training, Vertex AI Experiments, and Vertex AI Endpoints. The model is deployed in a Vertex AI endpoint, and your users call the model by using the Vertex AI endpoint. You want to receive an email when the feature data distribution changes significantly, so you can retrigger the training pipeline and deploy an updated version of your model. What should you do?

Use Vertex Al Model Monitoring. Enable prediction drift monitoring on the endpoint, and specify a notification email.

In Cloud Logging, create a logs-based alert using the logs in the Vertex Al endpoint. Configure Cloud Logging to send an email when the alert is triggered.

In Cloud Monitoring create a logs-based metric and a threshold alert for the metric. Configure Cloud Monitoring to send an email when the alert is triggered.

Export the container logs of the endpoint to BigQuery. Create a Cloud Function to run a SQL query over the exported logs and send an email. Use Cloud Scheduler to trigger the Cloud Function.

Q85. You want to train an AutoML model to predict house prices by using a small public dataset stored in BigQuery. You need to prepare the data and want to use the simplest, most efficient approach.
What should you do?

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

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

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

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

Q86. Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?

Vertex AI Pipelines and App Engine

Vertex AI Pipelines, Vertex AI Prediction, and Vertex AI Model Monitoring

Cloud Composer, BigQuery ML, and Vertex AI Prediction

Cloud Composer, Vertex AI Training with custom containers, and App Engine

* Option A is incorrect because Vertex AI Pipelines and App Engine do not meet all the requirements of the system. Vertex AI Pipelines is a service that allows you to create, run, andmanage ML workflows using TensorFlow Extended (TFX) components or custom components1. App Engine is a service that allows you to build and deploy scalable web applications using standard or flexible environments2. However, App Engine does not support Docker containers in the standard environment, and does not provide a dedicated service for online prediction and monitoring of ML models3.
* Option B is correct because Vertex AI Pipelines, Vertex AI Prediction, and Vertex AI Model Monitoring meet all the requirements of the system. Vertex AI Prediction is a service that allows you to deploy and serve ML models for online or batch prediction, with support for autoscaling and custom containers4. Vertex AI Model Monitoring is a service that allows you to monitor the performance and fairness of your deployed models, and get alerts for any issues or anomalies5.
* Option C is incorrect because Cloud Composer, BigQuery ML, and Vertex AI Prediction do not meet all the requirements of the system. Cloud Composer is a service that allows you to create, schedule, and manage workflows using Apache Airflow. BigQuery ML is a service that allows you to create and use ML models within BigQuery using SQL queries. However, BigQuery ML does not support custom containers, and Vertex AI Prediction does not support scheduled model retraining or model monitoring.
* Option D is incorrect because Cloud Composer, Vertex AI Training with custom containers, and App Engine do not meet all the requirements of the system. Vertex AI Training is a service that allows you to train ML models using built-in algorithms or custom containers. However, Vertex AI Training does not support online prediction or model monitoring, and App Engine does not support Docker containers in the standard environment or online prediction and monitoring of ML models3.
References:
* Vertex AI Pipelines overview
* App Engine overview
* Choosing an App Engine environment
* Vertex AI Prediction overview
* Vertex AI Model Monitoring overview
* [Cloud Composer overview]
* [BigQuery ML overview]
* [BigQuery ML limitations]
* [Vertex AI Training overview]

Q87. You built a legal document assistant with Gemini. Each user session repeatedly asks questions about the same 300-page contract, and the contract text is resent with every request. Costs and time-to-first-token are both higher than acceptable. The contract does not change during a session. You need to reduce cost and latency. What should you do?

Use context caching so the contract is processed once and reused across requests in the session.

Summarize the contract to 5 pages, and send only the summary with each request.

Lower max_output_tokens so the model returns shorter responses.

Split the contract into 30 separate requests and merge the responses in the application.

Q88. You are an ML engineer at a global shoe store. You manage the ML models for the company’s website. You are asked to build a model that will recommend new products to the user based on their purchase behavior and similarity with other users. What should you do?

Build a classification model

Build a knowledge-based filtering model

Build a collaborative-based filtering model

Build a regression model using the features as predictors

Q89. You need to train a ControlNet model with Stable Diffusion XL for an image editing use case. You want to train this model as quickly as possible. Which hardware configuration should you choose to train your model?

Configure one a2-highgpu-1g instance with an NVIDIA A100 GPU with 80 GB of RAM. Use float32 precision during model training.

Configure one a2-highgpu-1g instance with an NVIDIA A100 GPU with 80 GB of RAM. Use bfloat16 quantization during model training.

Configure four n1-standard-16 instances, each with one NVIDIA Tesla T4 GPU with 16 GB of RAM.
Use float32 precision during model training.

Configure four n1-standard-16 instances, each with one NVIDIA Tesla T4 GPU with 16 GB of RAM.
Use float16 quantization during model training.

Q90. You work for a gaming company that manages a popular online multiplayer game where teams with 6 players play against each other in 5-minute battles. There are many new players every day. You need to build a model that automatically assigns available players to teams in real time.
User research indicates that the game is more enjoyable when battles have players with similar skill levels. Which business metrics should you track to measure your model’s performance?

Average time players wait before being assigned to a team

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

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

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

Q91. You have trained a DNN regressor with TensorFlow to predict housing prices using a set of predictive features. Your default precision is tf.float64, and you use a standard TensorFlow estimator:

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

Switch from CPU to GPU serving.

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

Increase the dropout rate to 0.8 and retrain your model.

Increase the dropout rate to 0.8 in _PREDICT mode by adjusting the TensorFlow Serving parameters.

Q92. You are developing a mode! to detect fraudulent credit card transactions. You need to prioritize detection because missing even one fraudulent transaction could severely impact the credit card holder. You used AutoML to tram a model on users’ profile information and credit card transaction dat a. After training the initial model, you notice that the model is failing to detect many fraudulent transactions. How should you adjust the training parameters in AutoML to improve model performance?
Choose 2 answers

Increase the score threshold.

Decrease the score threshold.

Add more positive examples to the training set.

Add more negative examples to the training set.

Reduce the maximum number of node hours for training.

Q93. You work for a company that captures live video footage of checkout areas in their retail stores You need to use the live video footage to build a mode! to detect the number of customers waiting for service in near real time You want to implement a solution quickly and with minimal effort How should you build the model?

Use the Vertex Al Vision Occupancy Analytics model.

Use the Vertex Al Vision Person/vehicle detector model

Train an AutoML object detection model on an annotated dataset by using Vertex AutoML

Train a Seq2Seq+ object detection model on an annotated dataset by using Vertex AutoML

Q94. 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?

Increase the recall

Decrease the recall.

Increase the number of false positives

Decrease the number of false negatives

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.
References:
* Precision and recall – Wikipedia
* How to add a threshold in softmax scores – Stack Overflow

Q95. You recently deployed a pipeline in Vertex Al Pipelines that trains and pushes a model to a Vertex Al endpoint to serve real-time traffic. You need to continue experimenting and iterating on your pipeline to improve model performance. You plan to use Cloud Build for CI/CD You want to quickly and easily deploy new pipelines into production and you want to minimize the chance that the new pipeline implementations will break in production. What should you do?

Set up a CI/CD pipeline that builds and tests your source code If the tests are successful use the Google Cloud console to upload the built container to Artifact Registry and upload the compiled pipeline to Vertex Al Pipelines.

Set up a CI/CD pipeline that builds your source code and then deploys built artifacts into a pre-production environment Run unit tests in the pre-production environment If the tests are successful deploy the pipeline to production.

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

Set up a CI/CD pipeline that builds and tests your source code and then deploys built arrets into a pre-production environment After a successful pipeline run in the pre-production environment, rebuild the source code, and deploy the artifacts to production

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 requirements1.
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 implementations will break in production1.
* 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 allowyou 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 environment, 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 output1.
* 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 process1.
References:
* Preparing for Google Cloud Certification: Machine Learning Engineer, Course 3: Production ML Systems, Week 3: MLOps
* Google Cloud Professional Machine Learning Engineer Exam Guide, Section 3: Scaling ML models in production, 3.2 Automating ML workflows
* Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 6:
Production ML Systems, Section 6.4: Automating ML Workflows
* Cloud Build
* Vertex AI Pipelines
* Artifact Registry
* Pre-production environment

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