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問題 #1

You are the lead ML engineer for a large ecommerce company. The Data Analytics team has prepared a dataset of 50 million transaction rows, which is stored in a partitioned BigQuery table.
The team consists primarily of SQL experts with limited experience in Python or TensorFlow.
You need to build a binary classification model to predict the likelihood of a customer making a purchase in the next seven days. You need to provide daily batch predictions. You need to minimize development effort, data movement, and the overall complexity of the solution. What should you do?

A. Create a model with BigQuery ML that includes the MODEL_TYPE=AUTOML_CLASSIFIER settings. Use the ML.PREDICT function to generate predictions as part of your nightly data warehouse jobs.
B. Use the BigQuery ML TRANSFORM function to normalize and scale numerical features, one-hot encode categorical features, and train a MODEL_TYPE=LOGISTIC_REG mode. Use the ML.PREDICT function to generate predictions as part of your nightly data warehouse jobs.
C. Develop an XGBoost model on an Agent Platform Workbench notebook. Load the data into a pandas DataFrame, and pickle it to a Cloud Storage bucket. Create another notebook that loads the pickled model from Cloud Storage and saves predictions back to Cloud Storage. Schedule this notebook for nightly execution.
D. Train a classification model with Agent Platform AutoML Tabular Workflows, deploy the trained model to an Agent Platform endpoint, and add a nightly Cloud Run job to trigger batch predictions.

問題 #2

You built a binary classifier that detects fraudulent insurance claims. Only 0.4% of claims in the dataset are fraudulent. Your model reports 99.6% accuracy, but the fraud investigation team reports that almost no fraudulent claims are being flagged. Investigators have limited capacity, so every case they review must be worth their time. You need to select an evaluation approach that reflects business value. What should you do?

A. Evaluate the model using the area under the precision-recall curve, and select an operating threshold based on the investigators' review capacity.
B. Rebalance the test set by downsampling the majority class until classes are equal, and then report accuracy.
C. Continue optimizing for accuracy, but increase the classification threshold to 0.9.
D. Evaluate the model using log loss on the full imbalanced test set.

問題 #3

You have trained a text classification model in TensorFlow using AI Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead.
What should you do?

A. Submit a batch prediction job on AI Platform that points to the model location in Cloud Storage.
B. Use Dataflow with the SavedModel to read the data from BigQuery.
C. Deploy and version the model on AI Platform.
D. Export the model to BigQuery ML.

問題 #4

You work on a data science team at a bank and are creating an ML model to predict loan default risk. You have collected and cleaned hundreds of millions of records worth of training data in a BigQuery table, and you now want to develop and compare multiple models on this data using TensorFlow and Vertex AI. You want to minimize any bottlenecks during the data ingestion state while considering scalability. What should you do?

A. Use TensorFlow I/O's BigQuery Reader to directly read the data.
B. Export data to CSV files in Cloud Storage, and use tf.data.TextLineDataset() to read them.
C. Use the BigQuery client library to load data into a dataframe, and use tf.data.Dataset.from_tensor_slices() to read it.
D. Convert the data into TFRecords, and use tf.data.TFRecordDataset() to read them.

問題 #5

You are an ML researcher and are evaluating multiple deep learning-based model architectures and hyperparameter configurations. You need to implement a robust solution to track the progress of each model iteration, visualize key metrics, gain insights into model internals, and optimize training performance.
You want your solution to have the most efficient and powerful approach to compare the models and have the strongest visualization abilities. How should you bull this solution?

A. Use Vertex AI Experiments for tracking iterations and comparison, and use Vertex AI TensorBoard for visualization and analysis of the training metrics and model architecture.
B. Use Vertex AI TensorBoard for visualizing training progress and model behavior, and use Vertex AI Feature Store to stove and manage experiment data for analysis and reproducibility.
C. Use Vertex AI TensorBoard for in-depth visualization and analysis, and use BigQuery for experiment tracking and analysis.
D. Use Vertex AI Experiments for tracking iterations and comparison, and use BigQuery and Looker Studio for visualization and analysis of the training metrics and model architecture.

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