Solving business challenges with ML

A retail company wants to forecast weekly sales for each of its 500 stores. The data includes historical sales, promotions, holidays, and local weather. The company needs to update forecasts every week with new data. Which ML approach should they use?

A media company uses a custom Python script on a Compute Engine VM to run batch predictions with a large ML model. The script loads the model from Cloud Storage, processes records from a Pub/Sub pull subscription, and writes results to BigQuery. Predictions are taking too long and the VM often runs out of memory. Which two changes should the company implement to improve performance and scalability? (Choose TWO)

A hospital wants to deploy a machine learning model for detecting anomalies in patient vital signs. The model was trained on historical data but must comply with HIPAA regulations. The model serving must be low-latency (under 100 ms) and handle up to 1000 requests per second. Which architecture should they use on Google Cloud?

A data scientist deployed a TensorFlow model for sentiment analysis to Vertex AI Prediction. The model expects input key 'text' but the client sends requests with key 'review_text'. Which step should the data scientist take to resolve the error without retraining the model?

A logistics company uses a regression model to predict delivery times. The model currently uses features: distance (km), traffic index, weather condition, and time of day. The data scientist notices that the model's predictions are systematically too low for deliveries during peak traffic hours. Which action would best address this issue?

An e-commerce company uses a recommendation model that suggests products based on user browsing history. The model was trained on data from the past year and has high accuracy on the test set. However, after deployment, the click-through rate (CTR) on recommendations is much lower than expected. Which three steps should the data scientist take to diagnose and improve the model? (Choose THREE)

A data scientist runs a BigQuery ML prediction query and gets a region mismatch error. The model is in the US region, but the new_data table is in the EU region. What is the simplest way to resolve this?

A financial services company wants to detect fraudulent transactions in real-time. They have a trained XGBoost model that runs on a single Compute Engine instance. The current solution processes about 100 transactions per second, but they need to scale to 10,000 transactions per second. Which approach should they take?

A manufacturing company wants to predict equipment failure using sensor data. The data is highly imbalanced (only 1% failures). They are using a gradient boosted tree model with class weights. The model achieves 0.99 recall but 0.2 precision on the test set. Which two actions should they take to improve precision without significantly hurting recall? (Choose TWO)

A healthcare startup is building a diagnostic tool that uses a deep learning model to classify medical images. The model is trained on TensorFlow and deployed on Vertex AI Prediction. The startup has strict latency requirements: predictions must return within 200 ms for 95% of requests. Current performance shows p95 latency of 350 ms. The team has already tried using a smaller model, but accuracy dropped below acceptable levels. The traffic pattern is spiky: low load during nights but bursts of 1000 requests per second during business hours. Currently, they use a single n1-highmem-8 VM with a GPU attached. They have a budget for additional resources but need to optimize cost. The model is about 500 MB and requires GPU for inference. Which course of action should they take to meet the latency requirement while managing costs?

A retail company wants to forecast daily sales for inventory planning. They have 3 years of historical sales data with clear weekly and yearly seasonality. Which approach should they use?

A company is training a large neural network on Vertex AI and training jobs keep failing with 'Out of memory' errors. The VM uses a standard n1-standard-4 machine with 15 GB RAM. Which action should they take first?

A financial institution needs to deploy a fraud detection model with strict latency <100ms per prediction and high throughput (1000 predictions/sec). The model is a deep neural network. Which architecture on Google Cloud meets these requirements?

A startup wants to add sentiment analysis to their customer feedback app without any labeled data or custom model training. Which Google Cloud service should they use?

A data science team is using AI Platform for training. They want to track hyperparameters and metrics across multiple experiments. What should they use?

A company has a large dataset of 1 million unlabeled images for object detection. They want to use AutoML Vision but need to minimize labeling effort. Which strategy should they use?

A data scientist wants to perform feature engineering on a large dataset stored in BigQuery before training a model. Which feature engineering tool is most appropriate?

A model deployed on Vertex AI Prediction is returning high latency for real-time requests. The model is a small TensorFlow model. Which troubleshooting step should the team take first?

A company wants to use ML to predict customer churn. They have user activity logs in Cloud Storage, account data in BigQuery, and want an automated pipeline. Which pipeline architecture on Google Cloud should they use?

A company is evaluating Google Cloud ML solutions. Which TWO services are appropriate for building custom machine learning models (not using pre-built APIs)? (Choose TWO.)

A team is deploying a model on Vertex AI Prediction. Which THREE configuration settings have a direct impact on both latency and cost? (Choose THREE.)

A company wants to use pre-built Google Cloud APIs for text analysis. Which TWO APIs can they use? (Choose TWO.)

Refer to the exhibit. This IAM policy is applied at the project level. What is the effect of the condition?

Refer to the exhibit. A user attempts to upload a model to Vertex AI Model Registry using the gcloud CLI. The command fails with the error shown. What is the most likely cause?

Refer to the exhibit. A data scientist runs this Vertex AI training job code. What will be the outcome?

A retail company wants to predict customer churn using their transaction history and customer demographics. They have limited ML expertise and want to use a managed service on Google Cloud. Which service should they use?

A data scientist deployed a classification model on Vertex AI Endpoints. After a week, the model's accuracy drops significantly from 92% to 78%. The data scientist suspects training-serving skew. What is the first step to confirm this?

A media company wants to build a real-time recommendation system for articles. They have a large user base (10M+) and frequent updates to user interactions. They need to handle cold-start users and new articles. Which architecture on Vertex AI is most suitable?

A financial company is building a fraud detection model. The dataset has 1% fraud cases and 99% legitimate transactions. Which technique should they use to handle the class imbalance?

A data scientist trained a custom TensorFlow model using Vertex AI Training and wants to deploy it for online predictions with low latency (<100ms). Which deployment option on Google Cloud is best?

A mobile app company needs to run an image classification model on-device for real-time performance. The model is a ResNet-50 trained in TensorFlow. They need to reduce latency to under 50ms on a mid-range phone. Which optimization should they apply first?

Which TWO are best practices for building ML pipelines on Vertex AI Pipelines?

Which THREE are key capabilities of Vertex AI Feature Store?

Which TWO actions are recommended to detect and mitigate data drift in a production ML system on Vertex AI?

A global retail company uses Vertex AI Recommendations to provide product recommendations on their website. They have a large catalog and millions of users. The initial deployment works well for active users, but they notice that new users (with no purchase history) receive generic recommendations that are not personalized. The company wants to improve the cold-start experience. They have user demographic data (age, location) available at sign-up. Current recommendation model is a collaborative filtering model using the built-in Vertex AI Recommendations. What should the company do to improve personalization for new users?

A financial services company uses BigQuery ML to build a logistic regression model for fraud detection. The model is trained on the last 6 months of transaction data (about 50 million rows). After deployment, the fraud detection team notices a high false positive rate, causing customer dissatisfaction and extra manual review costs. The model is currently retrained monthly. The team wants to reduce false positives without sacrificing recall. They have access to real-time transaction streaming and can compute new features quickly. What is the most effective approach?

A healthcare startup is using Vertex AI to train a deep learning model for detecting anomalies in chest X-rays. The training dataset is 500 GB of images stored in Cloud Storage (GCS). They use a custom training container with TPU v3-32. The training job completes successfully, but the model performance is poor. On investigation, they discover that the input images were not preprocessed correctly: the images were resized to 256x256 instead of the required 512x512. They need to fix the preprocessing and retrain as quickly as possible. The preprocessing pipeline involves decompressing, resizing, normalizing, and augmenting images. They have a small team and limited time. Which approach should they take?

Refer to the exhibit. A data scientist is evaluating a binary classification model trained with BigQuery ML on an imbalanced dataset. The exhibit shows the output of ML.EVALUATE run on two different thresholds. Which TWO actions should the data scientist take to improve model performance? (Choose two.)

Refer to the exhibit. A machine learning engineer deployed a model on Vertex AI using this configuration. When testing the endpoint, the engineer receives a 400 error with the message: 'Invalid argument: Explanation metadata missing required field: `outputs`.' What is the most likely cause?

A retail company uses Vertex AI Tabular (AutoML Tables) to build a customer churn prediction model. The training dataset contains 50,000 rows and 30 features, with a 5% churn rate. The model achieves an AUC of 0.85 on the test set. When deployed for online predictions, the average latency is 800ms, while the business requirement is under 200ms. The engineer has already reduced the feature set to 10 features, but latency only dropped to 600ms. The model size is 2GB. The endpoint is in us-central1 using an n1-standard-4 machine with minReplicaCount=1. What should the engineer do to meet the latency requirement?