You are developing a recommendation engine for an online clothing store. The historical customer transaction data is stored in BigQuery and Cloud Storage. You need to perform exploratory data analysis (EDA), preprocessing and model training. You plan to rerun these EDA, preprocessing, and training steps as you experiment with different types of algorithms. You want to minimize the cost and development effort of running these steps as you experiment. How should you configure the environment?

• A.Create a Vertex Al Workbench managed notebook on a Dataproc cluster, and use the spark-bigquery-connector to access the tables.
• B.Create a Vertex Al Workbench managed notebook to browse and query the tables directly from the JupyterLab interface.
• C.Create a Vertex Al Workbench user-managed notebook on a Dataproc Hub. and use the %%bigquery magic commands in Jupyter to query the tables.
• D.Create a Vertex Al Workbench user-managed notebook using the default VM instance, and use the %%bigquery magic commands in Jupyter to query the tables.

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A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements:
* Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.
* Support event-driven ETL pipelines
* Provide a quick and easy way to understand metadata
Which approach meets these requirements?

• A.Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.
• B.Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.
• C.Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.
• D.Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.

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You work for a credit card company and have been asked to create a custom fraud detection model based on historical data using AutoML Tables. You need to prioritize detection of fraudulent transactions while minimizing false positives. Which optimization objective should you use when training the model?

• A.An optimization objective that minimizes Log loss
• B.An optimization objective that maximizes the Precision at a Recall value of 0.50
• C.An optimization objective that maximizes the area under the precision-recall curve (AUC PR) value
• D.An optimization objective that maximizes the area under the receiver operating characteristic curve (AUC ROC) value

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You work for a hotel and have a dataset that contains customers' written comments scanned from paper-based customer feedback forms which are stored as PDF files Every form has the same layout. You need to quickly predict an overall satisfaction score from the customer comments on each form. How should you accomplish this task'?

• A.Use the Vision API to parse the text from each PDF file Use the Natural Language API analyzesentiment feature to infer overall satisfaction scores.
• B.Use the Vision API to parse the text from each PDF file Use the Natural Language API analyzeEntitysentiment feature to infer overall satisfaction scores.
• C.Uptrain a Document Al custom extractor to parse the text in the comments section of each PDF file. Use the Natural Language API analyze sentiment feature to infer overall satisfaction scores.
• D.Uptrain a Document Al custom extractor to parse the text in the comments section of each PDF file. Use the Natural Language API analyzeEntitySentiment feature to infer overall satisfaction scores.

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You are working with a dataset that contains customer transactions. You need to build an ML model to predict customer purchase behavior You plan to develop the model in BigQuery ML, and export it to Cloud Storage for online prediction You notice that the input data contains a few categorical features, including product category and payment method You want to deploy the model as quickly as possible. What should you do?

• A.Use the create model statement and select the categorical and non-categorical features.
• B.Use the ML. ONE_HOT_ENCODER function on the categorical features, and select the encoded categorical features and non-categorical features as inputs to create your model.
• C.Use the ML. ONE_HOT_ENCODER function on the categorical features, and select the encoded categorical features and non-categorical features as inputs to create your model.
• D.Use the transform clause with the ML. ONE_HOT_ENCODER function on the categorical features at model creation and select the categorical and non-categorical features.

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