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>> Exam Associate-Data-Practitioner Questions Answers <<
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NEW QUESTION # 37
Your organization needs to store historical customer order data. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?
Answer: C
Explanation:
UsingNearline storagein Cloud Storage is the best option for data that is accessed infrequently (such as once a month) but must be readily available within seconds when needed. Nearline offers a balance between low storage costs and quick retrieval times, making it ideal for scenarios like monthly analysis of historical data. It is specifically designed for infrequent access patterns while avoiding the higher retrieval costs and longer access times of Coldline or Archive storage.
NEW QUESTION # 38
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?
Answer: A
Explanation:
Using the bq load command is the simplest and most efficient way to perform a one-time load of files from your local machine into BigQuery. This command-line tool is easy to use, requires minimal setup, and supports direct uploads from local files to BigQuery tables. It meets the requirement for minimal effort while allowing you to quickly demonstrate BigQuery's capabilities to your organization's leadership team.
NEW QUESTION # 39
You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?
Answer: B
Explanation:
Using aCloud Run functiontriggered by Cloud Storage to load the data into BigQuery is the best solution because it minimizes both cost and maintenance while providing low-latency data ingestion. Cloud Run is a serverless platform that automatically scales based on the workload, ensuring efficient use of resources without requiring a dedicated instance or cluster. It integrates seamlessly with Cloud Storage event notifications, enabling real-time processing of incoming files and loading them into BigQuery. This approach is cost-effective, scalable, and easy to manage.
The goal is to load small CSV files into BigQuery upon arrival (event-driven) with minimal latency, cost, and maintenance. Google Cloud provides serverless, event-driven options that align with this requirement. Let's evaluate each option in detail:
Option A: Cloud Composer (managed Apache Airflow) can schedule a pipeline to check Cloud Storage every
10 minutes, but this polling approach introduces latency (up to 10 minutes) and incurs costs for running Composer even when no files arrive. Maintenance includes managing DAGs and the Composer environment, which adds overhead. This is better suited for scheduled batch jobs, not event-driven ingestion.
Option B: A Cloud Run function triggered by a Cloud Storage event (via Eventarc or Pub/Sub) loads files into BigQuery as soon as they arrive, minimizing latency. Cloud Run is serverless, scales to zero when idle (low cost), and requires minimal maintenance (deploy and forget). Using the BigQuery API in the function (e.g., Python client library) handles small CSV loads efficiently. This aligns with Google's serverless, event-driven best practices.
Option C: Dataproc with Spark is designed for large-scale, distributed processing, not small CSV ingestion. It requires cluster management, incurs higher costs (even with ephemeral clusters), and adds unnecessary complexity for a simple load task.
Option D: The bq command-line tool in Cloud Shell is manual and not automated, failing the "upon arrival" requirement. It's a one-off tool, not a pipeline solution, and Cloud Shell isn't designed for persistent automation.
Why B is Best: Cloud Run leverages Cloud Storage's object creation events, ensuring near-zero latency between file arrival and BigQuery ingestion. It's serverless, meaning no infrastructure to manage, and costs scale with usage (free when idle). For small CSVs, the BigQuery load job is lightweight, avoiding processing overhead.
Extract from Google Documentation: From "Triggering Cloud Run with Cloud Storage Events" (https://cloud.
google.com/run/docs/triggering/using-events): "You can trigger Cloud Run services in response to Cloud Storage events, such as object creation, using Eventarc. This serverless approach minimizes latency and maintenance, making it ideal for real-time data pipelines." Additionally, from "Loading Data into BigQuery" (https://cloud.google.com/bigquery/docs/loading-data-cloud-storage-csv): "Programmatically load CSV files from Cloud Storage using the BigQuery API, enabling automated ingestion with minimal overhead."
NEW QUESTION # 40
You are a database administrator managing sales transaction data by region stored in a BigQuery table. You need to ensure that each sales representative can only see the transactions in their region. What should you do?
Answer: B
Explanation:
Creating a row-level access policy in BigQuery ensures that each sales representative can see only the transactions relevant to their region. Row-level access policies allow you to define fine-grained access control by filtering rows based on specific conditions, such as matching the sales representative's region. This approach enforces security while providing tailored data access, aligning with the principle of least privilege.
NEW QUESTION # 41
You have a Dataflow pipeline that processes website traffic logs stored in Cloud Storage and writes the processed data to BigQuery. You noticed that the pipeline is failing intermittently. You need to troubleshoot the issue. What should you do?
Answer: D
Explanation:
To troubleshoot intermittent failures in a Dataflow pipeline, you should use Cloud Logging to view detailed error messages in the pipeline's logs. These logs provide insights into the specific issues causing failures, such as data format errors or resource limitations. Additionally, you should use Cloud Monitoring to analyze the pipeline's metrics, such as CPU utilization, memory usage, and throughput, to identify performance bottlenecks or resource constraints that may contribute to the failures. This approach provides a comprehensive view of the pipeline's health and helps pinpoint the root cause of the intermittent issues.
NEW QUESTION # 42
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