Google Cloud Certified Associate Data Practitioner (GCC-ADP) Associate-Data-Practitioner Exam Questions
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You are using your own data to demonstrate the capabilities of BigQuery to your organization’s leadership team. You need to perform a one-time load of the files stored on your local machine into BigQuery using as little effort as possible. What should you do?
Correct Answer: C
Comprehensive and Detailed In-Depth Explanation:
A one-time load with minimal effort points to a simple, out-of-the-box tool. The files are local, so the solution must bridge on-premises to BigQuery easily.
Option A : A Python script with the Storage Write API requires coding, setup (authentication, libraries), and debugging—more effort than necessary for a one-time task.
Option B : Dataproc with Spark involves cluster creation, file transfer to Cloud Storage, and job scripting—far too complex for a simple load.
Option C : The bq load command (part of the Google Cloud SDK) is a CLI tool that uploads local files (e.g., CSV, JSON) directly to BigQuery with one command (e.g., bq load --source_format=CSV dataset.table file.csv). It’s pre-built, requires no coding, and leverages existing SDK installation, minimizing effort.
You work for a healthcare company that has a large on-premises data system containing patient records with personally identifiable information (PII) such as names, addresses, and medical diagnoses. You need a standardized managed solution that de-identifies PII across all your data feeds prior to ingestion to Google Cloud. What should you do?
Correct Answer: B
Using Cloud Data Fusion is the best solution for this scenario because:
Standardized managed solution : Cloud Data Fusion provides a visual interface for building data pipelines and includes prebuilt connectors and transformations for data cleaning and de-identification.
Compliance : It ensures sensitive data such as PII is de-identified prior to ingestion into Google Cloud, adhering to regulatory requirements for healthcare data.
Ease of use : Cloud Data Fusion is designed for transforming and preparing data, making it a managed and user-friendly tool for this purpose.
It's a fully managed, cloud-native data integration service for building ETL/ELT data pipelines visually.
It offers built-in transformations and connectors, including those suitable for data masking and de-identification.
It provides a standardized, visual interface, making it easier to create and manage data pipelines across various data sources.
It's designed for data integration and transformation, making it ideal for this scenario.
It helps to achieve a standardized managed solution.
Your team uses the Google Ads platform to visualize metrics. You want to export the data to BigQuery to get more granular insights. You need to execute a one-time transfer of historical data and automatically update data daily. You want a solution that is low-code, serverless, and requires minimal maintenance. What should you do?
Correct Answer: D
Your company is building a near real-time streaming pipeline to process JSON telemetry data from small appliances. You need to process messages arriving at a Pub/Sub topic, capitalize letters in the serial number field, and write results to BigQuery. You want to use a managed service and write a minimal amount of code for underlying transformations. What should you do?
Correct Answer: C
Using the "Pub/Sub to BigQuery" Dataflow template with a UDF (User-Defined Function) is the optimal choice because it combines near real-time processing, minimal code for transformations, and scalability. The UDF allows for efficient implementation of custom transformations, such as capitalizing letters in the serial number field, while Dataflow handles the rest of the managed pipeline seamlessly.
Your company has developed a website that allows users to upload and share video files. These files are most frequently accessed and shared when they are initially uploaded. Over time, the files are accessed and shared less frequently, although some old video files may remain very popular. You need to design a storage system that is simple and cost-effective. What should you do?
Correct Answer: B
The storage system must balance cost, simplicity, and access patterns: high initial access, decreasing over time, with some files remaining popular. Google Cloud Storage offers tailored options for this:
Option A : Custom Object Lifecycle Management (OLM) policies (e.g., transition to Nearline after 30 days, Archive after 90 days) are effective but static. They don’t adapt to actual usage, so popular old files in Archive would incur high retrieval costs.
Option B : Autoclass automatically adjusts storage classes (Standard, Nearline, Coldline, Archive) based on object access patterns, not just age. It keeps frequently accessed files in Standard (low latency/cost for access) and moves inactive ones to cheaper classes, minimizing costs while preserving simplicity. This fits the “some files remain popular” nuance.
Option C : A Cloud Scheduler job to manually change classes daily is complex (requires scripting, monitoring), error-prone, and less cost-effective than automated solutions like Autoclass or OLM.