Pass the Google Machine Learning Engineer Professional-Machine-Learning-Engineer Questions and answers with Dumpstech

Representation transformation (normalization) is a technique that transforms the features to be on a similar scale, such as between 0 and 1, or with mean 0 and standard deviation 1. This technique can improve the performance and training stability of the neural network model, as it can prevent the gradient optimization from being dominated by features with larger scales, and help the model converge faster and better. There are different types of normalization techniques, such as min-max scaling, z-score scaling, log scaling, etc. You can learn more about normalization techniques from the following web search results:

Normalization | Machine Learning | Google for Developers

NORMALIZATION TECHNIQUES IN TRAINING DNNS: METHODOLOG Y, ANALYSIS AND …

Visualizing Different Normalization Techniques | by Dibya … - Medium

Data Normalization Techniques: Easy to Advanced ( & the Best)

 Prediction drift is the change in the distribution of feature values or labels over time. It can affect the performance and accuracy of the model, and may require retraining or redeploying the model. Vertex AI Model Monitoring allows you to monitor prediction drift on your deployed models and endpoints, and set up alerts and notifications when the drift exceeds a certain threshold. You can specify an email address to receive the notifications, and use the information to retrigger the training pipeline and deploy an updated version of your model. This is the most direct and convenient way to achieve your goal.  References :

Vertex AI Model Monitoring

Monitoring prediction drift

Setting up alerts and notifications

To develop a credit risk model that meets the regulatory requirements and can be monitored over time, you should follow these steps:

Use Vertex Explainable AI with the sampled Shapley method.  Vertex Explainable AI is a service that provides feature attributions for machine learning models, which can help you understand how each feature contributes to the prediction 1 .  The sampled Shapley method is a technique that estimates the Shapley values for each feature, which are based on the marginal contribution of each feature to the prediction across all possible feature subsets 2 .  The sampled Shapley method is suitable for neural networks and other complex models, as it can capture the non-linear and interaction effects of the features 3 .

Enable Vertex AI Model Monitoring to check for feature distribution drift.  Vertex AI Model Monitoring is a service that helps you track and manage the performance and quality of your deployed models over time 4 . Feature distribution drift is a type of data drift that occurs when the distribution of the input features changes significantly from the training data, which can affect the model accuracy and reliability. By checking for feature distribution drift, you can detect when your model needs to be retrained or updated with new data.

 AutoML Tables is a service that allows you to automatically build, analyze, and deploy machine learning models on tabular data. It is suitable for large-scale regression and classification problems, and it supports various optimization objectives, data splitting methods, and hyperparameter tuning algorithms. AutoML Tables can handle both categorical and numerical features, and it can also handle missing values and outliers. AutoML Tables is a good choice for this problem because it minimizes the effort and training time required to train a regression model, while maximizing the model performance.

RMSLE stands for Root Mean Squared Logarithmic Error, and it is a metric that measures the average difference between the logarithm of the predicted values and the logarithm of the actual values. RMSLE is useful for regression problems where the target variable can include negative values, and where large differences between small values are more important than large differences between large values. For example, RMSLE penalizes underestimating a value of 10 by 2 more than overesti mating a value of 1000 by 20. RMSLE is a good optimization objective for this problem because it can handle negative values in the target variable, and it can reduce the impact of outliers and large errors.

For more information about AutoML Tables and RMSLE, see the following references:

AutoML Tables: end-to-end workflows on AI Platform Pipelines

Predict workload failures before they happen with AutoML Tables

How to Calculate RMSE in R

To organize the artifacts for dozens of pipelines, you should store the parameters in Vertex ML Metadata, store the models’ source code in GitHub, and store the models’ binaries in Cloud Storage. This option has the following advantages:

Vertex ML Metadata is a service that helps you track and manage the metadata of your ML workflows, such as datasets, models, metrics, and parameters 1 .  It can also help you with data lineage, model versioning, and model performance monitoring 2 .

GitHub is a popular platform for hosting and collaborating on code repositories.  It can help you manage the source code of your models, as well as the configuration files, scripts, and notebooks that are part of your ML pipelines 3 .

Cloud Storage is a scalable and durable object storage service that can store any type of data, including model binaries 4 .  It can also integrate with other services, such as Vertex AI, Cloud Functions, and Cloud Run, to enable easy deployment and serving of your models 5 .

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.

Cloud Vision API offers pre-trained models specialized in identifying explicit or inappropriate content. By sending a copy of each image to a Cloud Storage bucket and triggering Cloud Vision through Cloud Run, the detection of explicit content is automated with minimal development time. Vertex AI custom models require more training data and infrastructure management, while AutoML-based solutions add more complexity. Cloud Vision’s existing capabilities meet the requirement effectively and are highly scalable for real-time moderation needs.

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The best option for implementing a batch inference ML pipeline in Google Cloud, using a model that was developed using TensorFlow and is stored in SavedModel format in Cloud Storage, and a historical dataset containing 10 TB of data that is stored in a BigQuery table, is to configure a Vertex AI batch prediction job to apply the model to the historical data in BigQuery. This option allows you to leverage the power and simplicity of Vertex AI and BigQuery to perform large-scale batch inference with minimal code and configuration. Vertex AI is a unified platform for building and deploying machine learning solutions on Google Cloud. Vertex AI can run a batch prediction job, which can generate predictions for a large number of instances in batches. Vertex AI can also provide various tools and services for data analysis, model development, model deployment, model monitoring, and model governance. A batch prediction job is a resource that can run your model code on Vertex AI. A batch prediction job can help you generate predictions for a large number of instances in batches, and store the prediction results in a destination of your choice. A batch prediction job can accept various input formats, such as JSON, CSV, or TFRecord. A batch prediction job can also accept various input sources, such as Cloud Storage or BigQuery. A TensorFlow model is a resource that represents a machine learning model that is built using TensorFlow. TensorFlow is a framework that can perform large-scale data processing and machine learning. TensorFlow 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. A SavedModel format is a type of format that can store a TensorFlow model and its associated assets. A SavedModel format can help you save and load your TensorFlow model, and serve it for prediction. A SavedModel format can be stored in Cloud Storage, which is a service that can store and access large-scale data on Google Cloud. A historical dataset is a collection of data that contains historical information about a certain domain. A historical dataset can help you analyze the past trends and patterns of the data, and make predictions for the future. A historical dataset can be stored in BigQuery, which 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. By configuring a Vertex AI batch prediction job to apply the model to the historical data in BigQuery, you can implement a batch inference ML pipeline in Google Cloud with minimal code and configuration. You can use the Vertex AI API or the gcloud command-line tool to configure a batch prediction job, and provide the model name, the model version, the input source, the input format, the output destination, and the output format. Vertex AI will automatically run the batch prediction job, and apply the model to the historical data in BigQuery.  Vertex AI will also store th e prediction results in a destination of your choice, such as Cloud Storage or BigQuery 1 .

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

Option A: Exporting the historical data to Cloud Storage in Avro format, configuring a Vertex AI batch prediction job to generate predictions for the exported data would require more skills and steps than configuring a Vertex AI batch prediction job to apply the model to the historical data in BigQuery, and could increase the complexity and cost of the batch inference process. Avro is a type of format that can store and serialize data in a binary format. Avro can help you compress and encode your data, and support schema evolution and compatibility. By exporting the historical data to Cloud Storage in Avro format, configuring a Vertex AI batch prediction job to generate predictions for the exported data, you can perform batch inference with minimal code and configuration. You can use the BigQuery API or the bq command-line tool to export the historical data to Cloud Storage in Avro format, and use the Vertex AI API or the gcloud command-line tool to configure a batch prediction job, and provide the model name, the model version, the input source, the input format, the output destination, and the output format. However, exporting the historical data to Cloud Storage in Avro format, configuring a Vertex AI batch prediction job to generate predictions for the exported data would require more skills and steps than configuring a Vertex AI batch prediction job to apply the model to the historical data in BigQuery, and could increase the complexity and cost of the batch inference process. You would need to write code, export the historical data to Cloud Storage, configure a batch prediction job, and generate predictions for the exported data.  Moreover, this option would not use BigQuery as the input source for the batch prediction job, which can simplify the batch inference process, and provide various benefits, such as fast query performance, serverless scaling, and cost optimization 2 .

Option B: Importing the TensorFlow model by using the create model statement in BigQuery ML, applying the historical data to the TensorFlow model would not allow you to use Vertex AI to run the batch prediction job, and could increase the complexity and cost of the batch inference process. 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. A create model statement is a type of SQL statement that can create a machine learning model in BigQuery ML. A create model statement can help you specify the model name, the model type, the model options, and the model query. By importing the TensorFlow model by using the create model statement in BigQuery ML, applying the historical data to the TensorFlow model, you can perform batch inference with minimal code and configuration. You can use the BigQuery API or the bq command-line tool to import the TensorFlow model by using the create model statement in BigQuery ML, and provide the model name, the model type, the model options, and the model query. You can also use the BigQuery API or the bq command-line tool to apply the historical data to the TensorFlow model, and provide the model name, the input data, and the output destination. However, importing the TensorFlow model by using the create model statement in BigQuery ML, applying the historical data to the TensorFlow model would not allow you to use Vertex AI to run the batch prediction job, and could increase the complexity and cost of the batch inference process. You would need to write code, import the TensorFlow model, apply the historical data, and generate predictions.  Moreover, this option would not use Vertex AI, which is a unified platform for building and deploying machine learning solutions on Google Cloud, and provide various tools and services for data analysis, model development, model deployme nt, model monitoring, and model governance 3 .

Option C: Exporting the historical data to Cloud Storage in CSV format, configuring a Vertex AI batch prediction job to generate predictions for the exported data would require more skills and steps than configuring a Vertex AI batch prediction job to apply the model to the historical data in BigQuery, and could increase the complexity and cost of the batch inference process. CSV is a type of format that can store and serialize data in a comma-separated values format. CSV can help you store and exchange your data, and support various data types and formats. By exporting the historical data to Cloud Storage in CSV format, configuring a Vertex AI batch prediction job to generate predictions for the exported data, you can perform batch inference with minimal code and configuration. You can use the BigQuery API or the bq command-line tool to export the historical data to Cloud Storage in CSV format, and use the Vertex AI API or the gcloud command-line tool to configure a batch prediction job, and provide the model name, the model version, the input source, the input format, the output destination, and the output format. However, exporting the historical data to Cloud Storage in CSV format, configuring a Vertex AI batch prediction job to generate predictions for the exported data would require more skills and steps than configuring a Vertex AI batch prediction job to apply the model to the historical data in BigQuery, and could increase the complexity and cost of the batch inference process. You would need to write code, export the historical data to Cloud Storage, configure a batch prediction job, and generate predictions for the exported data.  Moreover, this option would not use BigQuery as the input source for the batch prediction job, which can simplify the batch inference p rocess, and provide various benefits, such as fast query performance, serverless scaling, and cost optimization 2 .

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.

Auto scaling is a feature that allows you to automatically adjust the number of prediction nodes based on the traffic and load of your deployed model 1 .  However, auto scaling depends on the CPU utilization of your prediction nodes, which is the percentage of CPU resources used by your model server 1 .  If your CPU utilization is low, even during periods of high load, it means that your model server is not fully utilizing the available CPU resources, and thus au to scaling will not trigger more replicas 2 .

On e possible reason for low CPU utilization is that your model server is using a single worker process to handle prediction requests 3 .  A worker process is a subprocess that runs your model code and handles prediction requests 3 .  If you have only one worker process, it can only handle one request at a time, which can lead to dropped requests when the traffic is high 3 .  To increase the CPU utilization and the throughput of your model server, you can increase the number of worker processes, which will allow your model server to handle multiple requests in parallel 3 .

To increase the number of workers i n your model server, you need to modify your custom model server code and use the  --workers  flag to specify the number of worker processes you want to use 3 . For example, if you are using a Gunicorn server, you can use the following command to start your model server with four worker processes:

gunicorn --bind :$PORT --workers 4 --threads 1 --timeout 60 main:app

By increasing the number of workers in your model server, you can increase the CPU utilization of your prediction nodes, and thus enable auto scaling to scale beyond one replica.

The other options are not suitable for your scenario, because they either do not address the root cause of low CPU utilization, such as attaching a GPU or scheduling scaling, or they do not enable auto scaling, such as increasing the minReplicaCount, which is a fixed number of nodes that will always run regardless of the traffi c 1 .