Certification Associate-Data-Practitioner Exam Dumps | Latest Associate-Data-Practitioner Test Prep

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Google Associate-Data-Practitioner QUESTIONS: A TERRIFIC EXAM PREPARATION SOURCE [2025]

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Google Associate-Data-Practitioner Exam Syllabus Topics:

Topic	Details
Topic 1	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
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.
Topic 3	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.

Google Cloud Associate Data Practitioner Sample Questions (Q46-Q51):

NEW QUESTION # 46
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 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.
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 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.

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 # 47
You are responsible for managing Cloud Storage buckets for a research company. Your company has well- defined data tiering and retention rules. You need to optimize storage costs while achieving your data retention needs. What should you do?

A. Configure a lifecycle management policy on each bucket to downgrade the storage class and remove objects based on age.
B. Configure the buckets to use the Standard storage class and enable Object Versioning.
C. Configure the buckets to use the Archive storage class.
D. Configure the buckets to use the Autoclass feature.

Answer: A

Explanation:
Configuring alifecycle management policyon each Cloud Storage bucket allows you to automatically transition objects to lower-cost storage classes (such as Nearline, Coldline, or Archive) based on their age or other criteria. Additionally, the policy can automate the removal of objects once they are no longer needed, ensuring compliance with retention rules and optimizing storage costs. This approach aligns well with well- defined data tiering and retention needs, providing cost efficiency and automation.
Extract from Google Documentation: From "Object Lifecycle Management" (https://cloud.google.com
/storage/docs/lifecycle):"Use lifecycle management policies to automatically transition objects to lower-cost storage classes (e.g., Nearline, Coldline) and delete them based on age, optimizing costs according to your specific tiering and retention requirements."

NEW QUESTION # 48
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 BigQuery table that uses the SUM( ) function and the _PARTITIONDATE filter.
B. 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.
C. Create a BigQuery table that uses the SUM( ) function and the DATE_DIFF( ) function.
D. Create a materialized view in BigQuery that uses the SUM( ) function and the DATE_SUB( ) function.

Answer: D

Explanation:
Creating a materialized view in BigQuery with the SUM() function and the DATE_SUB() function is the best approach. Materialized views allow you to pre-aggregate and cache query results, making them efficient for repeated access, such as monthly reporting. By using the DATE_SUB() function, you can filter the inventory data to include only the most recent month. This approach ensures that the aggregation is up-to-date with minimal latency and provides efficient integration with Looker Studio for dashboarding.

NEW QUESTION # 49
You are designing an application that will interact with several BigQuery datasets. You need to grant the application's service account permissions that allow it to query and update tables within the datasets, and list all datasets in a project within your application. You want to follow the principle of least privilege. Which pre- defined IAM role(s) should you apply to the service account?

A. roles/bigquery.jobUser and roles/bigquery.dataOwner
B. roles/bigquery.admin
C. roles/bigquery.connectionUser and roles/bigquery.dataViewer
D. roles/bigquery.user and roles/bigquery.filteredDataViewer

Answer: A

Explanation:
* roles/bigquery.jobUser:
* This role allows a user or service account to run BigQuery jobs, including queries. This is necessary for the application to interact with and query the tables.
* From Google Cloud documentation: "BigQuery Job User can run BigQuery jobs, including queries, load jobs, export jobs, and copy jobs."
* roles/bigquery.dataOwner:
* This role grants full control over BigQuery datasets and tables. It allows the service account to update tables, which is a requirement of the application.
* From Google Cloud documentation: "BigQuery Data Owner can create, delete, and modify BigQuery datasets and tables. BigQuery Data Owner can also view data and run queries."
* Why other options are incorrect:
* B. roles/bigquery.connectionUser and roles/bigquery.dataViewer:
* roles/bigquery.connectionUser is used for external connections, which is not required for this task. roles/bigquery.dataViewer only allows viewing data, not updating it.
* C. roles/bigquery.admin:
* roles/bigquery.admin grants excessive permissions. Following the principle of least privilege, this role is too broad.
* D. roles/bigquery.user and roles/bigquery.filteredDataViewer:
* roles/bigquery.user grants the ability to run queries, but not the ability to modify data. roles
/bigquery.filteredDataViewer only provides permission to view filtered data, which is not sufficient for updating tables.
* Principle of Least Privilege:
* The principle of least privilege is a security concept that states that a user or service account should be granted only the permissions necessary to perform its intended tasks.
* By assigning roles/bigquery.jobUser and roles/bigquery.dataOwner, we provide the application with the exact permissions it needs without granting unnecessary access.
* Google Cloud Documentation References:
* BigQuery IAM roles:https://cloud.google.com/bigquery/docs/access-control-basic-roles
* IAM best practices:https://cloud.google.com/iam/docs/best-practices-for-using-iam

NEW QUESTION # 50
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 regional availability (HA) with synchronous replication to a secondary instance in a different zone.
B. 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.
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 multi-region backup locations.

Answer: A

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 # 51
......

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Certification Associate-Data-Practitioner Exam Dumps | Latest Associate-Data-Practitioner Test Prep

Google Associate-Data-Practitioner QUESTIONS: A TERRIFIC EXAM PREPARATION SOURCE [2025]

Google Associate-Data-Practitioner Exam Syllabus Topics:

Google Cloud Associate Data Practitioner Sample Questions (Q46-Q51):

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