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購買後，立即下載 Apache-Hadoop-Developer 題庫 (Hadoop 2.0 Certification exam for Pig and Hive Developer): 成功付款後, 我們的體統將自動通過電子郵箱將您已購買的產品發送到您的郵箱。（如果在12小時內未收到，請聯繫我們，注意：不要忘記檢查您的垃圾郵件。）

最新的 HCAHD Apache-Hadoop-Developer 免費考試真題:

1. Which one of the following statements is true about a Hive-managed table?

A) Records can only be added to the table using the Hive INSERT command.
B) Hive dynamically defines the schema of the table based on the format of the underlying data.
C) Hive dynamically defines the schema of the table based on the FROM clause of a SELECT query.
D) When the table is dropped, the underlying folder in HDFS is deleted.

2. Which TWO of the following statements are true regarding Hive? Choose 2 answers

A) Offers real-time queries and row level updates
B) Useful for data analysts familiar with SQL who need to do ad-hoc queries
C) Is a relational database
D) Allows you to define a structure for your unstructured Big Data

3. Which one of the following statements is true regarding a MapReduce job?

A) The reduce method is invoked once for each unique value
B) The job's Partitioner shuffles and sorts all (key.value) pairs and sends the output to all reducers
C) The default Hash Partitioner sends key value pairs with the same key to the same Reducer
D) The Mapper must sort its output of (key.value) pairs in descending order based on value

4. For each input key-value pair, mappers can emit:

A) One intermediate key-value pair, of a different type.
B) As many intermediate key-value pairs as designed, as long as all the keys have the same types and all the values have the same type.
C) As many intermediate key-value pairs as designed, but they cannot be of the same type as the input key-value pair.
D) As many intermediate key-value pairs as designed. There are no restrictions on the types of those key-value pairs (i.e., they can be heterogeneous).
E) One intermediate key-value pair, but of the same type.

5. MapReduce v2 (MRv2/YARN) is designed to address which two issues?

A) Resource pressure on the JobTracker.
B) HDFS latency.
C) Standardize on a single MapReduce API.
D) Single point of failure in the NameNode.
E) Ability to run frameworks other than MapReduce, such as MPI.
F) Reduce complexity of the MapReduce APIs.

問題與答案：