NEW QUESTION 1
Your team trained and tested a DNN regression model with good results. Six months after deployment, the model is performing poorly due to a change in the distribution of the input data. How should you address the input differences in production?

• A. Create alerts to monitor for skew, and retrain the model.
• B. Perform feature selection on the model, and retrain the model with fewer features
• C. Retrain the model, and select an L2 regularization parameter with a hyperparameter tuning service
• D. Perform feature selection on the model, and retrain the model on a monthly basis with fewer features

Answer: C

NEW QUESTION 2
You are building a model to predict daily temperatures. You split the data randomly and then transformed the training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model performed with 97% accuracy; however, after deploying to production, the model's accuracy dropped to 66%. How can you make your production model more accurate?

• A. Normalize the data for the training, and test datasets as two separate steps.
• B. Split the training and test data based on time rather than a random split to avoid leakage
• C. Add more data to your test set to ensure that you have a fair distribution and sample for testing
• D. Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.

Answer: C

NEW QUESTION 3
Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers1 account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?

• A. 1. Create a Pub/Sub topic for each user* 2 Deploy a Cloud Function that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.
• B. 1. Create a Pub/Sub topic for each user* 2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts thata user's account balance will drop below the $25 threshold
• C. 1. Build a notification system on Firebase* 2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold
• D. 1 Build a notification system on Firebase* 2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user's account balance will drop below the $25 threshold

Answer: B

NEW QUESTION 4
You are an ML engineer at a large grocery retailer with stores in multiple regions. You have been asked to create an inventory prediction model. Your models features include region, location, historical demand, and seasonal popularity. You want the algorithm to learn from new inventory data on a daily basis. Which algorithms should you use to build the model?

• A. Classification
• B. Reinforcement Learning
• C. Recurrent Neural Networks (RNN)
• D. Convolutional Neural Networks (CNN)

Answer: B

NEW QUESTION 5
You have written unit tests for a Kubeflow Pipeline that require custom libraries. You want to automate the execution of unit tests with each new push to your development branch in Cloud Source Repositories. What should you do?

• A. Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run
• B. Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.
• C. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories Configure a Pub/Sub trigger for Cloud Run, and execute the unit tests on Cloud Run.
• D. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositorie
• E. Execute the unit tests using a Cloud Function that is triggered when messages are sent to the Pub/Sub topic

Answer: B

NEW QUESTION 6
Your team needs to build a model that predicts whether images contain a driver's license, passport, or credit card. The data engineering team already built the pipeline and generated a dataset composed of 10,000 images with driver's licenses, 1,000 images with passports, and 1,000 images with credit cards. You now have to train a model with the following label map: ['driversjicense', 'passport', 'credit_card']. Which loss function should you use?

• A. Categorical hinge
• B. Binary cross-entropy
• C. Categorical cross-entropy
• D. Sparse categorical cross-entropy

Answer: B

NEW QUESTION 7
You need to build classification workflows over several structured datasets currently stored in BigQuery.
Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?

• A. Configure AutoML Tables to perform the classification task
• B. Run a BigQuery ML task to perform logistic regression for the classification
• C. Use Al Platform Notebooks to run the classification model with pandas library
• D. Use Al Platform to run the classification model job configured for hyperparameter tuning

Answer: C

NEW QUESTION 8
You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 30 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables. This data has a time signal that is spread across multiple columns. How should you ensure that AutoML fits the best model to your data?

• A. Manually combine all columns that contain a time signal into an array Allow AutoML to interpret this array appropriatelyChoose an automatic data split across the training, validation, and testing sets
• B. Submit the data for training without performing any manual transformations Allow AutoML to handle the appropriatetransformations Choose an automatic data split across the training, validation, and testing sets
• C. Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column Allow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets
• D. Submit the data for training without performing any manual transformations Use the columns that have a time signal to manually split your data Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing set is from 30 days after your validation set

Answer: D

NEW QUESTION 9
You are an ML engineer at a global car manufacturer. 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?

• A. Three individual features binned latitude, binned longitude, and one-hot encoded car type
• B. One feature obtained as an element-wise product between latitude, longitude, and car type
• C. One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type
• D. Two feature crosses as a 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

Answer: A

NEW QUESTION 10
You work for an online retail company that is creating a visual search engine. You have set up an end-to-end ML pipeline on Google Cloud to classify whether an image contains your company's product. Expecting the release of new products in the near future, you configured a retraining functionality in the pipeline so that new data can be fed into your ML models. You also want to use Al Platform's continuous evaluation service to ensure that the models have high accuracy on your test data set. What should you do?

• A. Keep the original test dataset unchanged even if newer products are incorporated into retraining
• B. Extend your test dataset with images of the newer products when they are introduced to retraining
• C. Replace your test dataset with images of the newer products when they are introduced to retraining.
• D. Update your test dataset with images of the newer products when your evaluation metrics drop below a pre-decided threshold.

Answer: C

NEW QUESTION 11
You have deployed multiple versions of an image classification model on Al Platform. You want to monitor the performance of the model versions overtime. How should you perform this comparison?

• A. Compare the loss performance for each model on a held-out dataset.
• B. Compare the loss performance for each model on the validation data
• C. Compare the receiver operating characteristic (ROC) curve for each model using the What-lf Tool
• D. Compare the mean average precision across the models using the Continuous Evaluation feature

Answer: B

NEW QUESTION 12
You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?

• A. Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.
• B. Use a model trained and deployed on BigQuery ML and trigger retraining with the scheduled query feature in BigQuery
• C. Write a Cloud Functions script that launches a training and deploying job on Ai Platform that is triggered by Cloud Scheduler
• D. Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model

Answer: B

NEW QUESTION 13
You work for a global footwear retailer and need to predict when an item will be out of stock based on historical inventory data. Customer behavior is highly dynamic since footwear demand is influenced by many different factors. You want to serve models that are trained on all available data, but track your performance on specific subsets of data before pushing to production. What is the most streamlined and reliable way to perform this validation?

• A. Use the TFX ModelValidator tools to specify performance metrics for production readiness
• B. Use k-fold cross-validation as a validation strategy to ensure that your model is ready for production.
• C. Use the last relevant week of data as a validation set to ensure that your model is performing accurately on current data
• D. Use the entire dataset and treat the area under the receiver operating characteristics curve (AUC ROC) as the main metric.

Answer: A

NEW QUESTION 14
You are designing an ML recommendation model for shoppers on your company's ecommerce website. You will use Recommendations Al to build, test, and deploy your system. How should you develop recommendations that increase revenue while following best practices?

• A. Use the "Other Products You May Like" recommendation type to increase the click-through rate
• B. Use the "Frequently Bought Together' recommendation type to increase the shopping cart size for each order.
• C. Import your user events and then your product catalog to make sure you have the highest quality event stream
• D. Because it will take time to collect and record product data, use placeholder values for the product catalog to test the viability of the model.

Answer: C

NEW QUESTION 15
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?

• A. Increase the recall
• B. Decrease the recall.
• C. Increase the number of false positives
• D. Decrease the number of false negatives

Answer: D

NEW QUESTION 16
You want to rebuild your ML pipeline for structured data on Google Cloud. You are using PySpark to conduct data transformations at scale, but your pipelines are taking over 12 hours to run. To speed up development and pipeline run time, you want to use a serverless tool and SQL syntax. You have already moved your raw data into Cloud Storage. How should you build the pipeline on Google Cloud while meeting the speed and processing requirements?

• A. Use Data Fusion's GUI to build the transformation pipelines, and then write the data into BigQuery
• B. Convert your PySpark into SparkSQL queries to transform the data and then run your pipeline on Dataproc to write the data into BigQuery.
• C. Ingest your data into Cloud SQL convert your PySpark commands into SQL queries to transform the data, and then use federated queries from BigQuery for machine learning
• D. Ingest your data into BigQuery using BigQuery Load, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table

Answer: B

NEW QUESTION 17
You are going to train a DNN regression model with Keras APIs using this code:

How many trainable weights does your model have? (The arithmetic below is correct.)

• A. 501*256+257*128+2 = 161154
• B. 500*256+256*128+128*2 = 161024
• C. 501*256+257*128+128*2=161408
• D. 500*256*0 25+256*128*0 25+128*2 = 40448

Answer: D

NEW QUESTION 18
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