You work for a large social network service provider whose users post articles and discuss news. Millions of comments are posted online each day, and more than 200 human moderators constantly review comments and flag those that are inappropriate. Your team is building an ML model to help human moderators check content on the platform. The model scores each comment and flags suspicious comments to be reviewed by a human. Which metric(s) should you use to monitor the model's performance?
A. Number of messages flagged by the model per minute
B. Number of messages flagged by the model per minute confirmed as being inappropriate by humans.
C. Precision and recall estimates based on a random sample of 0.1% of raw messages each minute sent to a human for review
D. Precision and recall estimates based on a sample of messages flagged by the model as potentially inappropriate each minute
Your company manages an application that aggregates news articles from many different online sources and sends them to users. You need to build a recommendation model that will suggest articles to readers that are similar to the articles they are currently reading. Which approach should you use?
A. Create a collaborative filtering system that recommends articles to a user based on the user's past behavior.
B. Encode all articles into vectors using word2vec, and build a model that returns articles based on vector similarity.
C. Build a logistic regression model for each user that predicts whether an article should be recommended to a user.
D. Manually label a few hundred articles, and then train an SVM classifier based on the manually classified articles that categorizes additional articles into their respective categories.
You are profiling the performance of your TensorFlow model training time and notice a performance issue caused by inefficiencies in the input data pipeline for a single 5 terabyte CSV file dataset on Cloud Storage. You need to optimize the input pipeline performance. Which action should you try first to increase the efficiency of your pipeline?
A. Preprocess the input CSV file into a TFRecord file.
B. Randomly select a 10 gigabyte subset of the data to train your model.
C. Split into multiple CSV files and use a parallel interleave transformation.
D. Set the reshuffle_each_iteration parameter to true in the tf.data.Dataset.shuffle method.
You need to design an architecture that serves asynchronous predictions to determine whether a particular mission-critical machine part will fail. Your system collects data from multiple sensors from the machine. You want to build a model that will predict a failure in the next N minutes, given the average of each sensor's data from the past 12 hours. How should you design the architecture?
A. 1. HTTP requests are sent by the sensors to your ML model, which is deployed as a microservice and exposes a REST API for prediction
2.
Your application queries a Vertex AI endpoint where you deployed your model.
3.
Responses are received by the caller application as soon as the model produces the prediction.
B. 1. Events are sent by the sensors to Pub/Sub, consumed in real time, and processed by a Dataflow stream processing pipeline.
2.
The pipeline invokes the model for prediction and sends the predictions to another Pub/Sub topic.
3.
Pub/Sub messages containing predictions are then consumed by a downstream system for monitoring.
C. 1. Export your data to Cloud Storage using Dataflow.
2.
Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data.
3.
Export the batch prediction job outputs from Cloud Storage and import them into Cloud SQL.
D. 1. Export the data to Cloud Storage using the BigQuery command-line tool
2.
Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data.
3.
Export the batch prediction job outputs from Cloud Storage and import them into BigQuery.
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.
Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?
A. Vertex AI Pipelines and App Engine
B. Vertex AI Pipelines, Vertex AI Prediction, and Vertex AI Model Monitoring
C. Cloud Composer, BigQuery ML, and Vertex AI Prediction
D. Cloud Composer, Vertex AI Training with custom containers, and App Engine
You are creating a deep neural network classification model using a dataset with categorical input values. Certain columns have a cardinality greater than 10,000 unique values. How should you encode these categorical values as input into the model?
A. Convert each categorical value into an integer value.
B. Convert the categorical string data to one-hot hash buckets.
C. Map the categorical variables into a vector of boolean values.
D. Convert each categorical value into a run-length encoded string.
You need to train a natural language model to perform text classification on product descriptions that contain millions of examples and 100,000 unique words. You want to preprocess the words individually so that they can be fed into a recurrent neural network. What should you do?
A. Create a hot-encoding of words, and feed the encodings into your model.
B. Identify word embeddings from a pre-trained model, and use the embeddings in your model.
C. Sort the words by frequency of occurrence, and use the frequencies as the encodings in your model.
D. Assign a numerical value to each word from 1 to 100,000 and feed the values as inputs in your model.
You are working on a classification problem with time series data. After conducting just a few experiments using random cross-validation, you achieved an Area Under the Receiver Operating Characteristic Curve (AUC ROC) value of 99% on the training data. You haven't explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?
A. Address the model overfitting by using a less complex algorithm and use k-fold cross-validation.
B. Address data leakage by applying nested cross-validation during model training.
C. Address data leakage by removing features highly correlated with the target value.
D. Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.
You need to execute a batch prediction on 100 million records in a BigQuery table with a custom TensorFlow DNN regressor model, and then store the predicted results in a BigQuery table. You want to minimize the effort required to build this inference pipeline. What should you do?
A. Import the TensorFlow model with BigQuery ML, and run the ml.predict function.
B. Use the TensorFlow BigQuery reader to load the data, and use the BigQuery API to write the results to BigQuery.
C. Create a Dataflow pipeline to convert the data in BigQuery to TFRecords. Run a batch inference on Vertex AI Prediction, and write the results to BigQuery.
D. Load the TensorFlow SavedModel in a Dataflow pipeline. Use the BigQuery I/O connector with a custom function to perform the inference within the pipeline, and write the results to BigQuery.
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