Free CISI ICWIM Exam Questions

Question 1

Your team is running a Dataflow streaming job to process and transform log data in real time. You notice a delay in the processed data reaching BigQuery, which suggests a potential bottleneck in the pipeline. You open the Dataflow Job UI to investigate. Which of the following steps in the Dataflow Job UI would be the most effective first step to identify where the bottleneck is occurring?

A : Enable the Debugging Agent on the Dataflow workers

B : Check the Throughput Metrics for each stage in the Job Graph

C : Adjust the auto-scaling settings in the Dataflow UI

D : View the Execution Details to check the pipeline’s windowing configuration

Answer: B

Question 2

A financial institution needs to prepare data from different transactional systems for machine learning. The data needs initial filtering and validation before loading into BigQuery, where additional, complex transformations will be applied. Which data processing methodology best suits this requirement?

A : ETL (Extract, Transform, Load)

B : ETLT (Extract, Transform, Load, Transform)

C : ELT (Extract, Load, Transform)

D : Manual data preparation using Google Sheets

Answer: B

Question 3

You need to design a data pipeline that ingests real-time data from IoT devices into Cloud Storage (GCS) and then processes it using Cloud Dataflow. The pipeline should run automatically, every five minutes, to process new data as it arrives. Which of the following solutions best meets these requirements?

A : Configure BigQuery scheduled queries to run every five minutes, pulling data from Cloud Storage and processing it directly within BigQuery.

B : Configure Cloud Scheduler to run a Cloud Function every five minutes, which will start a Dataflow job to process new data in Cloud Storage.

C : Use Cloud Functions to trigger a Dataflow job every five minutes whenever data arrives in a Cloud Storage bucket.

D : Set up a Pub/Sub topic to receive real-time data from IoT devices and trigger Dataflow streaming jobs continuously.

Answer: B

Question 4

Your team is loading data into BigQuery from various sources, including CSV files, JSON logs, and a relational database. During ingestion, you notice mismatched schemas, inconsistent null handling, and duplicate records. What should you do to identify and resolve these issues before loading the data?

A : Use Cloud Composer to write custom Python scripts for data validation.

B : Rely on source system data validation rules to ensure consistency.

C : Use Dataprep to visually inspect, clean, and standardize the data.

D : Load the data into BigQuery directly and identify issues using error messages.

Answer: C

Question 5

Your team is loading data into BigQuery from various sources, including CSV files, JSON logs, and a relational database. During ingestion, you notice mismatched schemas, inconsistent null handling, and duplicate records. What should you do to identify and resolve these issues before loading the data?

A : Use Cloud Composer to write custom Python scripts for data validation.

B : Rely on source system data validation rules to ensure consistency.

C : Use Dataprep to visually inspect, clean, and standardize the data.

D : Load the data into BigQuery directly and identify issues using error messages.

Answer: C