A company wants to ingest data regularly from several external Software-as-a-Service (SaaS) applications, such as Salesforce and Google Ads, directly into BigQuery for reporting. The analytics team wants a fully managed, scheduled solution that requires no custom code or complex pipeline development. Which Google Cloud service should the team select to meet these requirements?

BigQuery Data Transfer Service: ### What BigQuery Data Transfer Service is **BigQuery Data Transfer Service (DTS)** is a fully managed data ingestion service designed to automate the scheduled movement of data into **BigQuery** from diverse sources, including third-party SaaS platforms, cloud storage, and external databases, without writing code. ### How it meets the requirements - **Automated SaaS ingestion:** BigQuery Data Transfer Service provides pre-built, native connectors for popular SaaS applications such as **Salesforce**, **Google Ads**, **Google Analytics 4**, and **ServiceNow**. - **No custom code required:** Analytics teams can configure, authenticate, and schedule recurring transfers directly through the Google Cloud console, the `bq` command-line tool, or the API without writing extraction scripts. - **Direct BigQuery integration:** Data is extracted and loaded directly into target BigQuery datasets, automatically handling schema definitions and regular ingestion schedules. ### Key advantages and capabilities - **Automated scheduling:** Set up recurring transfers (e.g., daily, hourly) with built-in retry logic. - **Historical backfills:** Easily initiate backfill runs to load past data or recover from historical gaps. - **Serverless and zero maintenance:** Eliminates the need to provision compute infrastructure or manage orchestration engines. - **Native data mapping:** Automatically maps external data types directly into standard BigQuery data types. ### Why this is optimal BigQuery Data Transfer Service is specifically built for straightforward, code-free, scheduled transfers from SaaS and cloud sources into BigQuery. It avoids the operational overhead of provisioning cluster-based visual tools or coding custom ETL pipelines when data only needs to be transferred directly into the data warehouse.

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FundamentalAssociate Data Practitioner

A data analytics team needs to ingest reporting data on a recurring daily schedule from SaaS platforms, including Salesforce and Google Ads, directly into BigQuery for analysis. The team has the following requirements: The solution must be fully managed and serverless. Data movement must occur automatically on a regular, predefined schedule. The setup should require no coding or custom pipeline development. Which Google Cloud service should the team use?

Choose the appropriate extraction tool (e.g., Dataflow, BigQuery Data Transfer Service, Database Migration Service, Cloud Data Fusion)

FundamentalAssociate Data Practitioner

A retail company needs to build a data pipeline that currently processes historical sales logs in batch from Cloud Storage, transforms the data, and loads it into BigQuery. In the near future, the company plans to ingest and transform live transactional events in real time from Pub/Sub using the exact same transformation logic and programming framework without rewriting application code. Which Google Cloud service should the company use to implement this unified batch and stream data processing pipeline?

FundamentalAssociate Data Practitioner

An enterprise collects high-volume telemetry and application logs published to a Google Cloud Pub/Sub topic. The engineering team needs to ingest these messages in real time into BigQuery for immediate SQL analytics while minimizing operational overhead and avoiding the deployment or maintenance of intermediate data processing pipelines. Which ingestion solution should the team implement?

FundamentalAssociate Data Practitioner

A data engineer is writing an automated bash script to ingest on-premises data into Google Cloud. The script needs to upload a local directory named /data/sales_records, which contains multiple nested subdirectories and CSV files, into a Cloud Storage bucket named gs://retail-analytics-lake. Which gcloud command should the engineer use to perform this upload?

Load data into Google Cloud storage systems using the appropriate tool (e.g., gcloud and BQ CLI, Storage Transfer Service, BigQuery Data Transfer Service, client libraries)

FundamentalAssociate Data Practitioner

An enterprise hosts a mission-critical database on a Cloud SQL instance configured for high availability (HA) within a single Google Cloud region. The organization wants to expand its disaster recovery (DR) architecture to protect against a potential full regional outage and achieve a recovery time objective (RTO) measured in minutes. Which replication strategy should the enterprise implement?

AdvancedProfessional Cloud Data Engineer

An enterprise data lake integrates Google Cloud Storage and BigQuery data assets governed by Dataplex. A data engineer needs to enforce row-level security on a centralized transactions table so regional analysts can only view data corresponding to their assigned region. A junior administrator recommends granting the Filtered Data Viewer (roles/bigquery.filteredDataViewer) IAM role directly to the regional analyst user groups at the BigQuery dataset level, and then executing CREATE ROW ACCESS POLICY DDL statements for each region. Why does this approach violate Google Cloud security best practices, and what is the correct implementation?

Associate Data Practitioner

Saas To Bigquery Data Transfer Service

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