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?
A. Average time players wait before being assigned to a team
B. Precision and recall of assigning players to teams based on their predicted versus actual ability
C. User engagement as measured by the number of battles played daily per user
D. Rate of return as measured by additional revenue generated minus the cost of developing a new model
You are building an ML model to predict trends in the stock market based on a wide range of factors. While exploring the data, you notice that some features have a large range. You want to ensure that the features with the largest magnitude don't overfit the model. What should you do?
A. Standardize the data by transforming it with a logarithmic function.
B. Apply a principal component analysis (PCA) to minimize the effect of any particular feature.
C. Use a binning strategy to replace the magnitude of each feature with the appropriate bin number.
D. Normalize the data by scaling it to have values between 0 and 1.
You need to analyze user activity data from your company's mobile applications. Your team will use BigQuery for data analysis, transformation, and experimentation with ML algorithms. You need to ensure real-time ingestion of the user activity data into BigQuery. What should you do?
A. Configure Pub/Sub to stream the data into BigQuery.
B. Run an Apache Spark streaming job on Dataproc to ingest the data into BigQuery.
C. Run a Dataflow streaming job to ingest the data into BigQuery.
D. Configure Pub/Sub and a Dataflow streaming job to ingest the data into BigQuery,
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 work for a toy manufacturer that has been experiencing a large increase in demand. You need to build an ML model to reduce the amount of time spent by quality control inspectors checking for product defects. Faster defect detection is a priority. The factory does not have reliable Wi-Fi. Your company wants to implement the new ML model as soon as possible. Which model should you use?
A. AutoML Vision Edge mobile-high-accuracy-1 model
B. AutoML Vision Edge mobile-low-latency-1 model
C. AutoML Vision model
D. AutoML Vision Edge mobile-versatile-1 model
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 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.
During batch training of a neural network, you notice that there is an oscillation in the loss. How should you adjust your model to ensure that it converges?
A. Decrease the size of the training batch.
B. Decrease the learning rate hyperparameter.
C. Increase the learning rate hyperparameter.
D. Increase the size of the training batch.
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 building a linear regression model on BigQuery ML to predict a customer's likelihood of purchasing your company's products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables. What should you do?
A. Use TensorFlow to create a categorical variable with a vocabulary list. Create the vocabulary file, and upload it as part of your model to BigQuery ML.
B. Create a new view with BigQuery that does not include a column with city information
C. Use Cloud Data Fusion to assign each city to a region labeled as 1, 2, 3, 4, or 5, and then use that number to represent the city in the model.
D. Use Dataprep to transform the state column using a one-hot encoding method, and make each city a column with binary values.
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