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NEW QUESTION: 1
Which methods can be used to reduce the number of rows processed by BigQuery?
A. Putting data in partitions; using the LIMIT clause
B. Splitting tables into multiple tables; putting data in partitions; using the LIMIT clause
C. Splitting tables into multiple tables; putting data in partitions
D. Splitting tables into multiple tables; using the LIMIT clause
Answer: C
Explanation:
Explanation
If you split a table into multiple tables (such as one table for each day), then you can limit your query to the data in specific tables (such as for particular days). A better method is to use a partitioned table, as long as your data can be separated by the day.
If you use the LIMIT clause, BigQuery will still process the entire table.
Reference: https://cloud.google.com/bigquery/docs/partitioned-tables

NEW QUESTION: 2
Which MAC protocol sets a random timer to reattempt communication?
A. RARP
B. CSMA/CD
C. CSMA/CA
D. IEEE 802.1x
Answer: B
Explanation:
Explanation/Reference:
Explanation:

NEW QUESTION: 3
Note: This question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question is independent of the other questions in this series. Information and details provided in a question apply only to that question.
You are implementing a batch processing solution by using Azure HDInsight.
You have two tables. Each table is larger than 250 TB. Both tables have approximately the same number of rows and columns.
You need to match the tables based on a key column. You must minimize the size of the data table that is produced.
What should you do?
A. Use an Azure Data Factory linked service that stores the data in Azure Data Lake.
B. Use a shuffle join in an Apache Hive query that stores the data in a JSON format.
C. Use an Azure Data Factory linked service that stores the data in an Azure DocumentDB database.
D. Increase the number of spark.executor.cores in an Apache Spark job that stores the data in a text format.
E. Decrease the level of parallelism in an Apache Spark job that stores the data in a text format.
F. Use a broadcast join in an Apache Hive query that stores the data in an ORC format.
G. Increase the number of spark.executor.instances in an Apache Spark job that stores the data in a text format.
H. Use an action in an Apache Oozie workflow that stores the data in a text format.
Answer: B
Explanation:
Explanation/Reference:
Explanation:
References: http://www.openkb.info/2014/11/understanding-hive-joins-in-explain.html