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Google Associate-Data-Practitioner Exam Overview:

Certification Vendor:Google Cloud
Exam Name:Google Cloud Certified - Associate Data Practitioner
Exam Number:Associate-Data-Practitioner
Exam Price:USD 125
Exam Format:Multiple select, Multiple choice
Real Exam Qty:50-60
Available Languages:English, French, Portuguese (Brazil), Spanish, Japanese, German
Exam Duration:90 minutes
Certificate Validity Period:3 years
Related Certifications:Google Cloud Professional Data Engineer
Google Cloud Associate Cloud Engineer
Google Cloud Digital Leader
Recommended Training:Google Cloud Skills Boost
Exam Registration:Google Cloud Certification Registration
Sample Questions:Google Associate-Data-Practitioner Sample Questions
Exam Way:Online proctored exam or test center delivery
Pre Condition:No formal prerequisites required, but familiarity with basic data concepts and Google Cloud services is recommended.
Official Syllabus URL:https://cloud.google.com/certification

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Google Associate-Data-Practitioner Exam Syllabus Topics:

TopicDetails
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 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.
Topic 3
  • 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.

Google Cloud Associate Data Practitioner Sample Questions (Q30-Q35):

NEW QUESTION # 30
You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?

Answer: B

Explanation:
Using the BigQuery Python client library to query and preprocess data directly in BigQuery and then leveraging BigQueryML to train the churn prediction model is the Google-recommended approach for this scenario. BigQueryML allows you to build machine learning models directly within BigQuery using SQL, eliminating the need to export data or manage additional infrastructure. This minimizes overhead, scales effectively for a dataset as large as 50 PB, and simplifies the end-to-end process of building and training the churn prediction model.


NEW QUESTION # 31
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?

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 # 32
Your company has several retail locations. Your company tracks the total number of sales made at each location each day. You want to use SQL to calculate the weekly moving average of sales by location to identify trends for each store. Which query should you use?

Answer: D

Explanation:
To calculate the weekly moving average of sales by location:
* The query must group bystore_id(partitioning the calculation by each store).
* TheORDER BY dateensures the sales are evaluated chronologically.
* TheROWS BETWEEN 6 PRECEDING AND CURRENT ROWspecifies a rolling window of 7 rows (1 week if each row represents daily data).
* TheAVG(total_sales)computes the average sales over the defined rolling window.
Chosen querymeets these requirements:
PARTITION BY store_idgroups the calculation by each store.

ORDER BY dateorders the rows correctly for the rolling average.

ROWS BETWEEN 6 PRECEDING AND CURRENT ROWensures the 7-day moving average.

Extract from Google Documentation: From "Analytic Functions in BigQuery" (https://cloud.google.com
/bigquery/docs/reference/standard-sql/analytic-function-concepts):"Use ROWS BETWEEN n PRECEDING AND CURRENT ROW with ORDER BY a time column to compute moving averages over a fixed number of rows, such as a 7-day window, partitioned by a grouping key like store_id."


NEW QUESTION # 33
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?

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 # 34
You work for a home insurance company. You are frequently asked to create and save risk reports with charts for specific areas using a publicly available storm event dataset. You want to be able to quickly create and re-run risk reports when new data becomes available. What should you do?

Answer: B

Explanation:
Copying the storm event dataset into your BigQuery project and using BigQuery Studio to query and visualize the data in Looker Studio is the best approach. This solution allows you to create reusable and automated workflows for generating risk reports. BigQuery handles the querying efficiently, and Looker Studio provides powerful tools for creating and sharing dynamic charts and dashboards. This setup ensures that reports can be easily re-run with updated data, minimizing manual effort and providing a scalable, interactive solution for visualizing risk reports.


NEW QUESTION # 35
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