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| Topic |
Details |
| Topic 1 |
- Data Analysis and Presentation: This domain assesses the competencies of Data Analysts in identifying data trends, patterns, and insights using BigQuery and Jupyter notebooks. Candidates will define and execute SQL queries to generate reports and analyze data for business questions.| Data Pipeline Orchestration: This section targets Data Analysts and focuses on designing and implementing simple data pipelines. Candidates will select appropriate data transformation tools based on business needs and evaluate use cases for ELT versus ETL.
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| Topic 2 |
- Data Preparation and Ingestion: This section of the exam measures the skills of Google Cloud Engineers and covers the preparation and processing of data. Candidates will differentiate between various data manipulation methodologies such as ETL, ELT, and ETLT. They will choose appropriate data transfer tools, assess data quality, and conduct data cleaning using tools like Cloud Data Fusion and BigQuery. A key skill measured is effectively assessing data quality before ingestion.
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| Topic 3 |
- Data Management: This domain measures the skills of Google Database Administrators in configuring access control and governance. Candidates will establish principles of least privilege access using Identity and Access Management (IAM) and compare methods of access control for Cloud Storage. They will also configure lifecycle management rules to manage data retention effectively. A critical skill measured is ensuring proper access control to sensitive data within Google Cloud services
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Google Cloud Associate Data Practitioner Sample Questions (Q32-Q37):
NEW QUESTION # 32
Your organization needs to implement near real-time analytics for thousands of events arriving each second in Pub/Sub. The incoming messages require transformations. You need to configure a pipeline that processes, transforms, and loads the data into BigQuery while minimizing development time. What should you do?
- A. Use Cloud Run functions to process the Pub/Sub messages, perform transformations, and write the results to BigQuery.
- B. Load the data from Pub/Sub into Cloud Storage using a Cloud Storage subscription. Create a Dataproc cluster, use PySpark to perform transformations in Cloud Storage, and write the results to BigQuery.
- C. Use a Google-provided Dataflow template to process the Pub/Sub messages, perform transformations, and write the results to BigQuery.
- D. Create a Cloud Data Fusion instance and configure Pub/Sub as a source. Use Data Fusion to process the Pub/Sub messages, perform transformations, and write the results to BigQuery.
Answer: C
Explanation:
Using a Google-provided Dataflow template is the most efficient and development-friendly approach to implement near real-time analytics for Pub/Sub messages. Dataflow templates are pre-built and optimized for processing streaming data, allowing you to quickly configure and deploy a pipeline with minimal development effort. These templates can handle message ingestion from Pub/Sub, perform necessary transformations, and load the processed data into BigQuery, ensuring scalability and low latency for near real-time analytics.
NEW QUESTION # 33
Another team in your organization is requesting access to a BigQuery dataset. You need to share the dataset with the team while minimizing the risk of unauthorized copying of data. You also want tocreate a reusable framework in case you need to share this data with other teams in the future. What should you do?
- A. Create a private exchange using Analytics Hub with data egress restriction, and grant access to the team members.
- B. Enable domain restricted sharing on the project. Grant the team members the BigQuery Data Viewer IAM role on the dataset.
- C. Export the dataset to a Cloud Storage bucket in the team's Google Cloud project that is only accessible by the team.
- D. Create authorized views in the team's Google Cloud project that is only accessible by the team.
Answer: A
Explanation:
Using Analytics Hub to create a private exchange with data egress restrictions ensures controlled sharing of the dataset while minimizing the risk of unauthorized copying. This approach allows you to provide secure, managed access to the dataset without giving direct access to the raw data. The egress restriction ensures that data cannot be exported or copied outside the designated boundaries. Additionally, this solution provides a reusable framework that simplifies future data sharing with other teams or projects while maintaining strict data governance.
Extract from Google Documentation: From "Analytics Hub Overview" (https://cloud.google.com/analytics- hub/docs):"Analytics Hub enables secure, controlled data sharing with private exchanges. Combine with organization policies like restrictDataEgress to prevent data copying, providing a reusable framework for sharing BigQuery datasets across teams."
NEW QUESTION # 34
Your organization consists of two hundred employees on five different teams. The leadership team is concerned that any employee can move or delete all Looker dashboards saved in the Shared folder. You need to create an easy-to-manage solution that allows the five different teams in your organization to view content in the Shared folder, but only be able to move or delete their team-specific dashboard. What should you do?
- A. 1. Move all team-specific content into the dashboard owner s personal folder. 2. Change the access level of the Shared folder to View for the All Users group. 3. Instruct each user to create content for their team in the user's personal folder.
- B. 1. Change the access level of the Shared folder to View for the All Users group. 2. Create five subfolders inside the Shared folder. Grant each team member the Manage Access, Edit access level to their corresponding subfolder.
- C. 1. Create Looker groups representing each of the five different teams, and add users to their corresponding group. 2. Create five subfolders inside the Shared folder. Grant each group the View access level to their corresponding subfolder.
- D. 1. Change the access level of the Shared folder to View for the All Users group. 2. Create Looker groups representing each of the five different teams, and add users to their corresponding group. 3.
Create five subfolders inside the Shared folder. Grant each group the Manage Access, Edit access level to their corresponding subfolder.
Answer: D
Explanation:
Comprehensive and Detailed in Depth Explanation:
Why C is correct:Setting the Shared folder to "View" ensures everyone can see the content.
Creating Looker groups simplifies access management.
Subfolders allow granular permissions for each team.
Granting "Manage Access, Edit" allows teams to modify only their own content.
Why other options are incorrect:A: Grants View access only, so teams can't edit.
B: Moving content to personal folders defeats the purpose of sharing.
D: Grants edit access to all members of the team, not the team as a whole, which is not ideal.
NEW QUESTION # 35
You have a Cloud SQL for PostgreSQL database that stores sensitive historical financial data. You need to ensure that the data is uncorrupted and recoverable in the event that the primary region is destroyed. The data is valuable, so you need to prioritize recovery point objective (RPO) over recovery time objective (RTO). You want to recommend a solution that minimizes latency for primary read and write operations. What should you do?
- A. Configure the Cloud SQL for PostgreSQL instance for multi-region backup locations.
- B. Configure the Cloud SQL for PostgreSQL instance for regional availability (HA) with synchronous replication to a secondary instance in a different zone.
- C. Configure the Cloud SQL for PostgreSQL instance for regional availability (HA) with asynchronous replication to a secondary instance in a different region.
- D. Configure the Cloud SQL for PostgreSQL instance for regional availability (HA). Back up the Cloud SQL for PostgreSQL database hourly to a Cloud Storage bucket in a different region.
Answer: B
Explanation:
Comprehensive and Detailed in Depth Explanation:
Why D is correct:Synchronous replication ensures that data is written to both the primary and secondary instances at the same time, minimizing data loss (RPO).
Regional availability (HA) within different zones provides redundancy within the same region, minimizing latency.
Why other options are incorrect:A: Asynchronous replication has a potential for data loss.
B: Multiregion backups are for disaster recovery, not minimizing latency.
C: Hourly backups do not provide the lowest possible RPO.
NEW QUESTION # 36
Your organization's ecommerce website collects user activity logs using a Pub/Sub topic. Your organization's leadership team wants a dashboard that contains aggregated user engagement metrics. You need to create a solution that transforms the user activity logs into aggregated metrics, while ensuring that the raw data can be easily queried. What should you do?
- A. Create a BigQuery subscription to the Pub/Sub topic, and load the activity logs into the table. Create a materialized view in BigQuery using SQL to transform the data for reporting
- B. Create a Cloud Storage subscription to the Pub/Sub topic. Load the activity logs into a bucket using the Avro file format. Use Dataflow to transform the data, and load it into a BigQuery table for reporting.
- C. Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.
- D. Create an event-driven Cloud Run function to trigger a data transformation pipeline to run. Load the transformed activity logs into a BigQuery table for reporting.
Answer: C
Explanation:
Using Dataflow to subscribe to the Pub/Sub topic and transform the activity logs is the best approach for this scenario. Dataflow is a managed service designed for processing and transforming streaming data in real time. It allows you to aggregate metrics from the raw activity logs efficiently and load the transformed data into a BigQuery table for reporting. This solution ensures scalability, supports real-time processing, and enables querying of both raw and aggregated data in BigQuery, providing the flexibility and insights needed for the dashboard.
NEW QUESTION # 37
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