ICC Performance Optimization 2 — Questions and Answers
Question 1: Which Informatica IICS feature allows you to run multiple mapping tasks concurrently within a single taskflow?
- Sequential task chaining
- Parallel task execution using Concurrent Tasks (Correct answer)
- Data Integration Service clustering
- Mass Ingestion batching
Correct answer: Parallel task execution using Concurrent Tasks
Concurrent Tasks in a taskflow allow multiple mapping tasks to execute in parallel, reducing overall pipeline wall-clock time.
Question 2: When optimizing a mapping that reads from a large Salesforce object, which technique most reduces API call volume?
- Enabling row-by-row error logging
- Using incremental extraction with a last-modified date filter (Correct answer)
- Disabling pushdown optimization
- Increasing the default commit interval
Correct answer: Using incremental extraction with a last-modified date filter
Incremental extraction filters records by a timestamp field so only changed data is pulled, dramatically cutting API calls on repeat runs.
Question 3: In IICS Data Integration, what does increasing the 'DTM buffer size' parameter primarily affect?
- The number of concurrent sessions allowed
- The amount of memory allocated for data transformation in-memory processing (Correct answer)
- The size of the network packet sent to the target
- The maximum number of partitions per pipeline stage
Correct answer: The amount of memory allocated for data transformation in-memory processing
DTM buffer size controls how much RAM is reserved for row processing; a larger buffer reduces disk spill and improves throughput.
Question 4: Which Source qualifier option in a mapping helps avoid a full-table scan on a relational source when only a subset of rows is needed?
- Enable high precision
- Use a SQL override with a WHERE clause (Correct answer)
- Set target load type to Normal
- Enable tracing level to Verbose Data
Correct answer: Use a SQL override with a WHERE clause
A SQL override with a WHERE clause pushes the filter predicate to the database, returning only matching rows and eliminating a full scan.
Question 5: What is the recommended approach when a Joiner transformation in IICS causes performance degradation due to a very large master pipeline?
- Switch to an Aggregator transformation
- Cache the smaller dataset and use it as the master input (Correct answer)
- Increase the session log verbosity
- Replace the Joiner with a Union transformation
Correct answer: Cache the smaller dataset and use it as the master input
Caching the smaller dataset as the master minimizes repeated comparison passes and greatly speeds up the join operation.
Question 6: Which IICS mapping parameter controls how many rows are written to the target in a single database commit?
- Array fetch size
- Commit interval (commit rows) (Correct answer)
- DTM buffer block size
- Session retry on deadlock
Correct answer: Commit interval (commit rows)
Commit interval (commit rows) determines how many rows accumulate before a COMMIT is issued, balancing memory use against transaction overhead.
Question 7: In Informatica Cloud Data Integration, enabling 'pushdown optimization' for a mapping that joins two tables in the same relational database achieves what performance benefit?
- It converts the mapping to run on a Spark cluster
- It offloads join and filter processing to the database engine, reducing data movement (Correct answer)
- It compresses the output CSV before writing to the target
- It caches all source rows in Secure Agent memory before transformation
Correct answer: It offloads join and filter processing to the database engine, reducing data movement
Pushdown optimization translates mapping logic into native SQL and executes it directly in the source or target database, avoiding data transfer to the Secure Agent.
ICC Performance Optimization 2
Which Informatica IICS feature allows you to run multiple mapping tasks concurrently within a single taskflow?