You work for an online travel agency that also sells advertising placements on its website to other companies. You have been asked to predict the most relevant web banner that a user should see next. Security is important to your company. The model latency requirements are 300ms@p99, the inventory is thousands of web banners, and your exploratory analysis has shown that navigation context is a good predictor. You want to Implement the simplest solution. How should you configure the prediction pipeline?
A. Embed the client on the website, and then deploy the model on AI Platform Prediction.
B. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Firestore for writing and for reading the user's navigation context, and then deploy the model on AI Platform Prediction.
C. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Cloud Bigtable for writing and for reading the user's navigation context, and then deploy the model on AI Platform Prediction.
D. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Memorystore for writing and for reading the user's navigation context, and then deploy the model on Google Kubernetes Engine.
You have been given a dataset with sales predictions based on your company's marketing activities. The data is structured and stored in BigQuery, and has been carefully managed by a team of data analysts. You need to prepare a report providing insights into the predictive capabilities of the data. You were asked to run several ML models with different levels of sophistication, including simple models and multilayered neural networks. You only have a few hours to gather the results of your experiments. Which Google Cloud tools should you use to complete this task in the most efficient and self-serviced way?
A. Use BigQuery ML to run several regression models, and analyze their performance.
B. Read the data from BigQuery using Dataproc, and run several models using SparkML.
C. Use Vertex AI Workbench user-managed notebooks with scikit-learn code for a variety of ML algorithms and performance metrics.
D. Train a custom TensorFlow model with Vertex AI, reading the data from BigQuery featuring a variety of ML algorithms.
You are developing an ML model that uses sliced frames from video feed and creates bounding boxes around specific objects. You want to automate the following steps in your training pipeline: ingestion and preprocessing of data in Cloud Storage, followed by training and hyperparameter tuning of the object model using Vertex AI jobs, and finally deploying the model to an endpoint. You want to orchestrate the entire pipeline with minimal cluster management. What approach should you use?
A. Use Kubeflow Pipelines on Google Kubernetes Engine.
B. Use Vertex AI Pipelines with TensorFlow Extended (TFX) SDK.
C. Use Vertex AI Pipelines with Kubeflow Pipelines SDK.
D. Use Cloud Composer for the orchestration.
You are an ML engineer at a bank that has a mobile application. Management has asked you to build an ML-based biometric authentication for the app that verifies a customer's identity based on their fingerprint. Fingerprints are considered highly sensitive personal information and cannot be downloaded and stored into the bank databases. Which learning strategy should you recommend to train and deploy this ML mode?
A. Data Loss Prevention API
B. Federated learning
C. MD5 to encrypt data
D. Differential privacy
You are experimenting with a built-in distributed XGBoost model in Vertex AI Workbench user-managed notebooks. You use BigQuery to split your data into training and validation sets using the following queries:
CREATE OR REPLACE TABLE `myproject.mydataset.training` AS (SELECT * FROM `myproject.mydataset.mytable` WHERE RAND() <= 0.8);
CREATE OR REPLACE TABLE `myproject.mydataset.validation` AS (SELECT * FROM `myproject.mydataset.mytable` WHERE RAND() <= 0.2);
After training the model, you achieve an area under the receiver operating characteristic curve (AUC ROC) value of 0.8, but after deploying the model to production, you notice that your model performance has dropped to an AUC ROC value of 0.65. What problem is most likely occurring?
A. There is training-serving skew in your production environment.
B. There is not a sufficient amount of training data.
C. The tables that you created to hold your training and validation records share some records, and you may not be using all the data in your initial table.
D. The RAND() function generated a number that is less than 0.2 in both instances, so every record in the validation table will also be in the training table.
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. Train a TensorFlow model on Vertex AI.
B. Train a classification Vertex AutoML model.
C. Run a logistic regression job on BigQuery ML.
D. Use scikit-learn in Notebooks with pandas library.
You are an ML engineer in the contact center of a large enterprise. You need to build a sentiment analysis tool that predicts customer sentiment from recorded phone conversations. You need to identify the best approach to building a model 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. What should you do?
A. Convert the speech to text and extract sentiments based on the sentences.
B. Convert the speech to text and build a model based on the words.
C. Extract sentiment directly from the voice recordings.
D. Convert the speech to text and extract sentiment using syntactical analysis.
You are an ML engineer at an ecommerce company and have been tasked with building a model that predicts how much inventory the logistics team should order each month. Which approach should you take?
A. Use a clustering algorithm to group popular items together. Give the list to the logistics team so they can increase inventory of the popular items.
B. Use a regression model to predict how much additional inventory should be purchased each month. Give the results to the logistics team at the beginning of the month so they can increase inventory by the amount predicted by the model.
C. Use a time series forecasting model to predict each item's monthly sales. Give the results to the logistics team so they can base inventory on the amount predicted by the model.
D. Use a classification model to classify inventory levels as UNDER_STOCKED, OVER_STOCKED, and CORRECTLY_STOCKEGive the report to the logistics team each month so they can fine-tune inventory levels.
You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano, scikit-learn, and custom libraries. What should you do?
A. Use the Vertex AI Training to submit training jobs using any framework.
B. Configure Kubeflow to run on Google Kubernetes Engine and submit training jobs through TFJob.
C. Create a library of VM images on Compute Engine, and publish these images on a centralized repository.
D. Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.
You are developing an ML model using a dataset with categorical input variables. You have randomly split half of the data into training and test sets. After applying one-hot encoding on the categorical variables in the training set, you discover that one categorical variable is missing from the test set. What should you do?
A. Use sparse representation in the test set.
B. Randomly redistribute the data, with 70% for the training set and 30% for the test set
C. Apply one-hot encoding on the categorical variables in the test data
D. Collect more data representing all categories
