Question 1

You are tasked with designing a data pipeline that ingests daily transaction logs from Cloud Storage, performs transformations to filter and summarize the data, and loads the result into BigQuery. The pipeline needs to be managed and scheduled to run every night at midnight. Which set of Google Cloud products would best meet these requirements?

Options :

A : BigQuery ML to perform transformations, Cloud Functions for scheduling, and Cloud Storage as the final destination.

B : Cloud Composer for orchestration, Cloud Dataflow for transformations, and BigQuery as the final storage.

C : Cloud Functions to trigger, Cloud Storage for transformations, and Dataproc as the final destination.

D : Cloud Scheduler to trigger the pipeline, Pub/Sub for transformations, and BigQuery as the final storage.

Answer: B

Question 2

A retail company needs to process large volumes of real-time transaction data to detect fraud. The company wants a fully managed service that can handle both batch and stream processing with minimal latency. Given these requirements, which Google Cloud tool should they use?

Options :

A : Dataproc

B : Data Fusion

C : Dataflow

D : Cloud Composer

Answer: C

Question 3

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?

Options :

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 4

You have just completed training a machine learning model on customer behavior data. Your next step is to evaluate its performance to decide if it’s ready for deployment. Which two evaluation metrics are critical for assessing the quality of a model designed to predict customer churn? (Select two)

Options :

A : Precision

B : Throughput

C : Latency

D : Recall

E : Storage utilization

Answer: A,D

Question 5

A data team at your company manages sensitive financial data stored in BigQuery. To ensure secure access, they want to configure access controls that allow only specific teams to view and query the data. Additionally, they want to audit access attempts to this data to detect any unauthorized attempts. Which of the following is the best approach to meet these requirements?

Options :

A : Assign the BigQuery Data Owner role to a service account and let the data team use that service account to access data as needed.

B : Use BigQuery’s IAM roles to grant the BigQuery Data Viewer role specifically on the dataset containing financial data, and enable Data Access audit logs on BigQuery.

C : Create a custom IAM role combining bigquery.tables.get and bigquery.tables.list permissions, then assign it at the dataset level, and enable Admin Activity logs in Cloud Logging.

D : Use BigQuery’s IAM roles to grant all team members the BigQuery Data Viewer role at the project level, and enable Cloud Audit Logs for BigQuery.

Answer: B

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