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NEW QUESTION 28 Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow. Numerous data logs are being are being generated during this step, and the team wants to analyze them. Due to the dynamic nature of the campaign, the data is growing exponentially every hour. The data scientists have written the following code to read the data for a new key features in the logs. BigQueryIO.Read .named("ReadLogData") .from("clouddataflow-readonly:samples.log_data") You want to improve the performance of this data read. What should you do?

• A. Use .fromQuery operation to read specific fields from the table.
• B. Call a transform that returns TableRow objects, where each element in the PCollection represents a single row in the table.
• C. Use of both the Google BigQuery TableSchema and TableFieldSchema classes.
• D. Specify the TableReference object in the code.

Answer: B   NEW QUESTION 29 Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub streaming data, one of the important business requirements is to be able to periodically identify the inputs and their timings during their campaign. Engineers have decided to use windowing and transformation in Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud Dataflow job fails for the all streaming insert. What is the most likely cause of this problem?

• A. They have not applied a non-global windowing function, which causes the job to fail when the pipeline is created
• B. They have not assigned the timestamp, which causes the job to fail
• C. They have not set the triggers to accommodate the data coming in late, which causes the job to fail
• D. They have not applied a global windowing function, which causes the job to fail when the pipeline is created

Answer: D   NEW QUESTION 30 Which Google Cloud Platform service is an alternative to Hadoop with Hive?

• A. Cloud Bigtable
• B. Cloud Dataflow
• C. BigQuery
• D. Cloud Datastore

Answer: C Explanation: Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis. Google BigQuery is an enterprise data warehouse. Reference: https://en.wikipedia.org/wiki/Apache_Hive   NEW QUESTION 31 You have a requirement to insert minute-resolution data from 50,000 sensors into a BigQuery table. You expect significant growth in data volume and need the data to be available within 1 minute of ingestion for real- time analysis of aggregated trends. What should you do?

• A. Use a Cloud Dataflow pipeline to stream data into the BigQuery table.
• B. Use the MERGE statement to apply updates in batch every 60 seconds.
• C. Use the INSERT statement to insert a batch of data every 60 seconds.
• D. Use bq loadto load a batch of sensor data every 60 seconds.

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