Question 1

Which Java SDK class can you use to run your Dataflow programs locally?

Options :

A : LocalRunner

B : DirectPipelineRunner

C : MachineRunner

D : LocalPipelineRunner

Answer: B

Question 2

As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.

Options :

A : Use Cloud Deployment Manager to automate access provision.

B : Introduce resource hierarchy to leverage access control policy inheritance.

C : Create distinct groups for various teams, and specify groups in Cloud IAM policies.

D : Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.

E : For each Cloud Storage bucket or BigQuery dataset, decide which projects need access. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.

Answer: A,C

Question 3

A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time. This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning. Over time, the query processing time has increased. You need to implement a change that would improve query performance in BigQuery. What should you do?

Options :

A : Implement clustering in BigQuery on the ingest date column.

B : Implement clustering in BigQuery on the package-tracking ID column.

C : Tier older data onto Cloud Storage files, and leverage extended tables.

D : Re-create the table using data partitioning on the package delivery date.

Answer: A

Question 4

You are migrating a table to BigQuery and are deeding on the data model. Your table stores information related to purchases made across several store locations and includes information like the time of the transaction, items purchased, the store ID and the city and state in which the store is located You frequently query this table to see how many of each item were sold over the past 30 days and to look at purchasing trends by state city and individual store. You want to model this table to minimize query time and cost. What should you do?

Options :

A : Partition by transaction time; cluster by state first, then city then store ID

B : Partition by transaction tome cluster by store ID first, then city, then stale

C : Top-level cluster by stale first, then city then store

D : Top-level cluster by store ID first, then city then state.

Answer: C

Question 5

A data pipeline uses Cloud Pub/Sub for ingesting data. The data is stored in topics and a Dataflow workflow reads from a subscription to that topic, processes the data, and writes output to BigQuery. What is the recommended way to authenticate when reading data from Cloud Pub/Sub?

Options :

A : Custom role

B : Google Workspace Identity

C : Use service accounts

D : Basic role

Answer: C

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