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.

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You are an ML engineer at a global shoe store. You manage the ML models for the company's website. You are asked to build a model that will recommend new products to the user based on their purchase behavior and similarity with other users. What should you do?

• A.Build a classification model
• B.Build a knowledge-based filtering model
• C.Build a collaborative-based filtering model
• D.Build a regression model using the features as predictors

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Your company manages an ecommerce website. You developed an ML model that recommends additional products to users in near real time based on items currently in the user's cart. The workflow will include the following processes.
1 The website will send a Pub/Sub message with the relevant data and then receive a message with the prediction from Pub/Sub.
2 Predictions will be stored in BigQuery
3. The model will be stored in a Cloud Storage bucket and will be updated frequently You want to minimize prediction latency and the effort required to update the model How should you reconfigure the architecture?

• A.Create a pipeline in Vertex Al Pipelines that performs preprocessing, prediction and postprocessing Configure the pipeline to be triggered by a Cloud Function when messages are sent to Pub/Sub.
• B.Write a Cloud Function that loads the model into memory for prediction Configure the function to be triggered when messages are sent to Pub/Sub.
• C.Expose the model as a Vertex Al endpoint Write a custom DoFn in a Dataflow job that calls the endpoint for prediction.
• D.Use the Runlnference API with watchFilePatterr. in a Dataflow job that wraps around the model and serves predictions.

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You have a demand forecasting pipeline in production that uses Dataflow to preprocess raw data prior to model training and prediction.
During preprocessing, you employ Z-score normalization on data stored in BigQuery and write it back to BigQuery.
New training data is added every week.
You want to make the process more efficient by minimizing computation time and manual intervention. What should you do?

• A.Normalize the data using Google Kubernetes Engine.
• B.Translate the normalization algorithm into SQL for use with BigQuery.
• C.Use the normalizer_fn argument in TensorFlow's Feature Column API.
• D.Normalize the data with Apache Spark using the Dataproc connector for BigQuery.

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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 model
• B.AutoML Vision Edge mobile-versatile-1 model
• C.AutoML Vision Edge mobile-low-latency-1 model
• D.AutoML Vision Edge mobile-high-accuracy-1 model

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