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NEW QUESTION 30 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. Convert the data into TFRecords, and use tf.data.TFRecordDataset() to read them.
• C. Export data to CSV files in Cloud Storage, and use tf.data.TextLineDataset() to read them.
• D. Use the BigQuery client library to load data into a dataframe, and use tf.data.Dataset.fromtensorslices() to read it.

Answer: C   NEW QUESTION 31 You are designing an architecture with a serveress ML system to enrich customer support tickets with informative metadata before they are routed to a support agent. You need a set of models to predict ticket priority, predict ticket resolution time, and perform sentiment analysis to help agents make strategic decisions when they process support requests. Tickets are not expected to have any domain-specific terms or jargon. The proposed architecture has the following flow: Which endpoints should the Enrichment Cloud Functions call?

• A. 1 = cloud Natural Language API, 2 = Al Platform, 3 = Cloud Vision API
• B. 1 = Al Platform, 2 = Al Platform, 3 = AutoML Natural Language
• C. 1 = Al Platform, 2 = Al Platform, 3 = AutoML Vision
• D. 1 = Al Platform, 2 = Al Platform, 3 = Cloud Natural Language API

Answer: B   NEW QUESTION 32 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 build a model based on the words
• B. Convert the speech to text and extract sentiments based on the sentences
• C. Convert the speech to text and extract sentiment using syntactical analysis
• D. Extract sentiment directly from the voice recordings

Answer: B   NEW QUESTION 33 You recently joined an enterprise-scale company that has thousands of datasets. You know that there are accurate descriptions for each table in BigQuery, and you are searching for the proper BigQuery table to use for a model you are building on AI Platform. How should you find the data that you need?

• A. Maintain a lookup table in BigQuery that maps the table descriptions to the table ID. Query the lookup table to find the correct table ID for the data that you need.
• B. Tag each of your model and version resources on AI Platform with the name of the BigQuery table that was used for training.
• C. Use Data Catalog to search the BigQuery datasets by using keywords in the table description.
• D. Execute a query in BigQuery to retrieve all the existing table names in your project using the INFORMATION_SCHEMA metadata tables that are native to BigQuery. Use the result o find the table that you need.

Answer: B   NEW QUESTION 34 ......