Associate-Data-Practitioner최신덤프자료, Associate-Data-Practitioner자격증참고서
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제일 간단한 방법으로 가장 어려운 문제를 해결해드리는것이KoreaDumps의 취지입니다.Google인증 Associate-Data-Practitioner시험은 가장 어려운 문제이고KoreaDumps의Google인증 Associate-Data-Practitioner 덤프는 어려운 문제를 해결할수 있는 제일 간단한 공부방법입니다. KoreaDumps의Google인증 Associate-Data-Practitioner 덤프로 시험준비를 하시면 아무리 어려운Google인증 Associate-Data-Practitioner시험도 쉬워집니다.
IT인증자격증을 취득하는 것은 IT업계에서 자신의 경쟁율을 높이는 유력한 수단입니다. 경쟁에서 밀리지 않으려면 자격증을 많이 취득하는 편이 안전합니다.하지만 IT자격증취득은 생각보다 많이 어려운 일입니다. Google인증 Associate-Data-Practitioner시험은 인기자격증을 취득하는데 필요한 시험과목입니다. KoreaDumps는 여러분이 자격증을 취득하는 길에서의 없어서는 안될 동반자입니다. KoreaDumps의Google인증 Associate-Data-Practitioner덤프로 자격증을 편하게 취득하는게 어떨가요?
>> Associate-Data-Practitioner최신 덤프자료 <<
Google Associate-Data-Practitioner자격증참고서, Associate-Data-Practitioner최신버전 시험대비 공부자료
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최신 Google Cloud Platform Associate-Data-Practitioner 무료샘플문제 (Q41-Q46):
질문 # 41
You recently inherited a task for managing Dataflow streaming pipelines in your organization and noticed that proper access had not been provisioned to you. You need to request a Google-provided IAM role so you can restart the pipelines. You need to follow the principle of least privilege. What should you do?
- A. Request the Dataflow Viewer role.
- B. Request the Dataflow Worker role.
- C. Request the Dataflow Admin role.
- D. Request the Dataflow Developer role.
정답:D
설명:
TheDataflow Developerrole provides the necessary permissions to manage Dataflow streaming pipelines, including the ability to restart pipelines. This role adheres to the principle of least privilege, as itgrants only the permissions required to manage and operate Dataflow jobs without unnecessary administrative access.
Other roles, such as Dataflow Admin, would grant broader permissions, which are not needed in this scenario.
질문 # 42
You are designing a pipeline to process data files that arrive in Cloud Storage by 3:00 am each day. Data processing is performed in stages, where the output of one stage becomes the input of the next. Each stage takes a long time to run. Occasionally a stage fails, and you have to address the problem. You need to ensure that the final output is generated as quickly as possible. What should you do?
- A. Design the workflow as a Cloud Workflow instance. Code the workflow to jump to a given stage based on an input parameter. Rerun the workflow after correcting any stage output data errors.
- B. Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.
- C. Design a Spark program that runs under Dataproc. Code the program to wait for user input when an error is detected. Rerun the last action after correcting any stage output data errors.
- D. Design the processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task after correcting any stage output data errors.
정답:D
설명:
Using Cloud Composer to design the processing pipeline as a Directed Acyclic Graph (DAG) is the most suitable approach because:
Fault tolerance: Cloud Composer (based on Apache Airflow) allows for handling failures at specific stages. You can clear the state of a failed task and rerun it without reprocessing the entire pipeline.
Stage-based processing: DAGs are ideal for workflows with interdependent stages where the output of one stage serves as input to the next.
Efficiency: This approach minimizes downtime and ensures that only failed stages are rerun, leading to faster final output generation.
질문 # 43
You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage.
The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery. Which data manipulation methodology should you choose?
- A. ETL
- B. EL
- C. ETLT
- D. ELT
정답:A
설명:
The ETL (Extract, Transform, Load) methodology is the best approach for this scenario because it allows you to extract data from the files, transform it by applying the necessary data cleansing (including removing malicious SQL injections), and then load the sanitized data into BigQuery. By transforming the data before loading it into BigQuery, you ensure that only clean and safe data is stored, which is critical for security and data quality.
질문 # 44
You need to create a weekly aggregated sales report based on a large volume of dat a. You want to use Python to design an efficient process for generating this report. What should you do?
- A. Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
- B. Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
- C. Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
- D. Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
정답:A
설명:
Using Dataflow with a Python-coded Directed Acyclic Graph (DAG) is the most efficient solution for generating a weekly aggregated sales report based on a large volume of data. Dataflow is optimized for large-scale data processing and can handle aggregation efficiently. Python allows you to customize the pipeline logic, and Cloud Scheduler enables you to automate the process to run weekly. This approach ensures scalability, efficiency, and the ability to process large datasets in a cost-effective manner.
질문 # 45
You are working on a project that requires analyzing daily social media data. You have 100 GB of JSON formatted data stored in Cloud Storage that keeps growing.
You need to transform and load this data into BigQuery for analysis. You want to follow the Google- recommended approach. What should you do?
- A. Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
- B. Use Dataflow to transform the data and write the transformed data to BigQuery.
- C. Use Cloud Run functions to transform and load the data into BigQuery.
- D. Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
정답:B
설명:
Comprehensive and Detailed in Depth Explanation:
Why C is correct:Dataflow is a fully managed service for transforming and enriching data in both batch and streaming modes.
Dataflow is googles recomended way to transform large datasets.
It is designed for parallel processing, making it suitable for large datasets.
Why other options are incorrect:A: Manual downloading and scripting is not scalable or efficient.
B: Cloud Run functions are for stateless applications, not large data transformations.
D: While Cloud Data fusion could work, Dataflow is more optimized for large scale data transformation.
질문 # 46
......
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