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IBM InfoSphere DataStage v8.5 Sample Questions:

1. What are three valid join operations for the Join stage? (Choose three.)

A) Right outer join
B) Left outer join
C) Inner Join
D) Bottom inner join
E) Top outer join

2. You have a 3TB dataset hash-partitioned on CustID in a clustered environment. You need to join this dataset with 1GB of reference data on OrderID. Which technique is most appropriate?

A) Use Join stage, hash-partition and sort both link on OrderID.
B) Use Lookup stage, select auto partitioning for the stream link and entire partitioning for the reference link.
C) Use Lookup stage, select auto partitioning for the stream link and hash-partition the reference link on CustI
D) Use Lookup stage, select auto partitioning for both link.

3. What stage allows for more than one reject link?

A) Funnel stage
B) Lookup stage
C) Join stage
D) Peek stage
E) Merge stage

4. A customer is complaining of poor performance from the job shown in the exhibit, which aggregates and joins on ZIP code. Currently all partitioning is set to "Auto" and sort/partition insertion is allowed.

What would give the best performance improvement for the job?

A) Use same partitioning on all input links in the job; create the input data set by hash-partitioning its input on ZIP code.
B) Hash partition and sort on ZIP code on the input links to the Aggregator and Join stages.
C) Hash partition and sort on ZIP code prior to the Copy stage and use entire partitioning on the output of the Aggregator stage.
D) Hash partition and sort on ZIP code prior to the Copy stage, and use same partitioning on the Aggregator and Join stages.

5. Your job runs slowly and does not scale beyond two nodes. It appears the data has been partitioned on the DoNotCall flag, which has accumulating totals based on this flag using a sort method.
Which technique would improve scalability and performance?

A) Change the preserve partitioning option on the stage ahead of the aggregator to clear partitioning.
B) Add an additional column for partitioning to result in additional data partitions.
C) Increase the number of physical nodes available for partitioning.
D) Change the partitioning method to Round Robin; add a second Aggregator stage with Execution mode 'Sequential' and

Solutions: