ADP指南 - ADP證照信息

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Google ADP Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|
| Topic 1: Data Processing and Analytics | 20-30% | - Query and analyze datasets - Build and maintain data pipelines - Apply statistical methods for analysis - Aggregate and summarize data - Use BigQuery and SQL for analytics
|
| Topic 2: Data Visualization and Insights | 20-30% | - Create dashboards and reports - Build visualizations using Looker Studio - Interpret and communicate findings - Present data insights to stakeholders - Choose appropriate visualization types
|
| Topic 3: Data-Driven Decision Making | 10-20% | - Identify stakeholders and requirements - Define success metrics - Translate business requirements into data solutions - Assess data quality and completeness
|
| Topic 4: Data Preparation and Exploration | 20-30% | - Ingest and acquire data - Identify data quality issues - Explore data through visualization and queries - Perform exploratory data analysis (EDA) - Transform and prepare data for analysis
|
>> ADP指南 <<
Google ADP證照信息 & 最新ADP題庫資源
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最新的 Google Cloud Certified ADP 免費考試真題 (Q22-Q27):
問題 #22
You created a curated dataset of market trends in BigQuery that you want to share with multiple external partners. You want to control the rows and columns that each partner has access to. You want to follow Google-recommended practices. What should you do?
- A. Create a separate project for each partner and copy the dataset into each project. Publish each dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
- B. Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
- C. Create a separate Cloud Storage bucket for each partner. Export the dataset to each bucket and assign each partner to their respective bucket. Grant bucket-level access by using 1AM roles.
- D. Grant each partner read access to the BigQuery dataset by using 1AM roles.
答案:B
問題 #23
Your company uses Looker to generate and share reports with various stakeholders. You have a complex dashboard with several visualizations that needs to be delivered to specific stakeholders on a recurring basis, with customized filters applied for each recipient. You need an efficient and scalable solution to automate the delivery of this customized dashboard. You want to follow the Google- recommended approach. What should you do?
- A. Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.
- B. Use the Looker Scheduler with a user attribute filter on the dashboard, and send the dashboard with personalized filters to each stakeholder based on their attributes.
- C. Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.
- D. Embed the Looker dashboard in a custom web application, and use the application's scheduling features to send the report with personalized filters.
答案:B
問題 #24
Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?
- A. Use Cloud Scheduler to schedule the jobs to run.
- B. Create directed acyclic graphs (DAGS) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
- C. Create directed acyclic graphs (DAGS) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
- D. Use Cloud Tasks to schedule and run the jobs asynchronously.
答案:C
問題 #25
Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard. What should you do?
- A. Create a materialized view in BigQuery that uses the SUM() function and the DATE_SUB() function.
- B. Create a BigQuery table that uses the SUM() function and the DATE_DIFF() function.
- C. Create a BigQuery table that uses the SUM() function and the _PARTITIONDATE filter.
- D. Create a saved query in the BigQuery console that uses the SUM() function and the DATE_SUB() function. Re-run the saved query every month, and save the results to a BigQuery table.
答案:A
問題 #26
Your organization has a BigQuery dataset that contains sensitive employee information such as salaries and performance reviews. The payroll specialist in the HR department needs to have continuous access to aggregated performance data, but they do not need continuous access to other sensitive dat a. You need to grant the payroll specialist access to the performance data without granting them access to the entire dataset using the simplest and most secure approach. What should you do?
- A. Create a SQL query with the aggregated performance data. Export the results to an Avro file in a Cloud Storage bucket. Share the bucket with the payroll specialist.
- B. Create row-level and column-level permissions and policies on the table that contains performance data in the dataset. Provide the payroll specialist with the appropriate permission set.
- C. Create a table with the aggregated performance data. Use table-level permissions to grant access to the payroll specialist.
- D. Use authorized views to share query results with the payroll specialist.
答案:D
問題 #27
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