GOOGLE ASSOCIATE-DATA-PRACTITIONER LATEST QUESTIONS, ASSOCIATE-DATA-PRACTITIONER FREE TEST QUESTIONS

Google Associate-Data-Practitioner Latest Questions, Associate-Data-Practitioner Free Test Questions

Google Associate-Data-Practitioner Latest Questions, Associate-Data-Practitioner Free Test Questions

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Tags: Associate-Data-Practitioner Latest Questions, Associate-Data-Practitioner Free Test Questions, Latest Associate-Data-Practitioner Braindumps Sheet, Associate-Data-Practitioner Exam Price, Valid Braindumps Associate-Data-Practitioner Pdf

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In today's competitive Google industry, only the brightest and most qualified candidates are hired for high-paying positions. Obtaining Associate-Data-Practitioner certification is a wonderful approach to be successful because it can draw in prospects and convince companies that you are the finest in your field. Pass the Google Cloud Associate Data Practitioner to establish your expertise in your field and receive certification. However, passing the Google Cloud Associate Data Practitioner Associate-Data-Practitioner Exam is challenging.

>> Google Associate-Data-Practitioner Latest Questions <<

Associate-Data-Practitioner Free Test Questions & Latest Associate-Data-Practitioner Braindumps Sheet

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Google Associate-Data-Practitioner Exam Syllabus Topics:

TopicDetails
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.
Topic 2
  • 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 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 (Q24-Q29):

NEW QUESTION # 24
You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need to clean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?

  • A. Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
  • B. Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
  • C. Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.
  • D. Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.

Answer: D

Explanation:
Using BigQuery to batch load the data and perform cleaning and analysis with SQL is the best approach for this scenario. BigQuery provides powerful SQL capabilities to handle missing values, enforce correct data types, and remove duplicates efficiently. This method simplifies the pipeline by leveraging BigQuery's built-in processing power for both cleaning and analysis, reducing the need for additional tools or services and minimizing complexity.


NEW QUESTION # 25
You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?

  • A. Create separate Looker instances for each regional manager. Copy the LookML model and dashboard to each instance. Provision viewer access to the corresponding manager.
  • B. Create asales_regionuser attribute, and assign each manager's region as the value of their user attribute.
    Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.
  • C. Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.
  • D. Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.

Answer: B

Explanation:
Using asales_region user attributeis the best solution because it allows you to dynamically filter data based on each manager's assigned region. By adding anaccess_filterExplore filter on theregion_namedimension that references thesales_regionuser attribute, each manager sees only the sales metrics specific to their region. This approach is easy to implement, scalable, and avoids duplicating dashboards or Explores, making it both efficient and maintainable.


NEW QUESTION # 26
You work for a financial organization that stores transaction data in BigQuery. Your organization has a regulatory requirement to retain data for a minimum of seven years for auditing purposes. You need to ensure that the data is retained for seven years using an efficient and cost-optimized approach. What should you do?

  • A. Create a partition by transaction date, and set the partition expiration policy to seven years.
  • B. Set the dataset-level retention policy in BigQuery to seven years.
  • C. Export the BigQuery tables to Cloud Storage daily, and enforce a lifecycle management policy that has a seven-year retention rule.
  • D. Set the table-level retention policy in BigQuery to seven years.

Answer: D

Explanation:
Setting a table-level retention policy in BigQuery to seven years is the most efficient and cost-optimized solution to meet the regulatory requirement. A table-level retention policy ensures that the data cannot be deleted or overwritten before the specified retention period expires, providing compliance with auditing requirements while keeping the data within BigQuery for easy access and analysis. This approach avoids the complexity and additional costs of exporting data to Cloud Storage.


NEW QUESTION # 27
Your company uses Looker to visualize and analyze sales dat
a. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?

  • A. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
  • B. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
  • C. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
  • D. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.

Answer: C

Explanation:
Creating a single Explore with all the sales metrics is the Google-recommended approach. This Explore should be designed to include all relevant attributes and dimensions, enabling users to analyze sales data by region, product category, time period, and other filters like sales representatives. With a well-structured Explore, you can efficiently build a dashboard that supports filtering and drill-down functionality. This approach simplifies maintenance, provides a consistent data model, and ensures users have the flexibility to interact with and analyze the data seamlessly within a unified framework.


NEW QUESTION # 28
Your organization plans to move their on-premises environment to Google Cloud. Your organization's network bandwidth is less than 1 Gbps. You need to move over 500 TB of data to Cloud Storage securely, and only have a few days to move the dat a. What should you do?

  • A. Connect to Google Cloud using Dedicated Interconnect. Use the gcloud storage command to move the data to Cloud Storage.
  • B. Connect to Google Cloud using VPN. Use Storage Transfer Service to move the data to Cloud Storage.
  • C. Request multiple Transfer Appliances, copy the data to the appliances, and ship the appliances back to Google Cloud to upload the data to Cloud Storage.
  • D. Connect to Google Cloud using VPN. Use the gcloud storage command to move the data to Cloud Storage.

Answer: C

Explanation:
Using Transfer Appliances is the best solution for securely and efficiently moving over 500 TB of data to Cloud Storage within a limited timeframe, especially with network bandwidth below 1 Gbps. Transfer Appliances are physical devices provided by Google Cloud to securely transfer large amounts of data. After copying the data to the appliances, they are shipped back to Google, where the data is uploaded to Cloud Storage. This approach bypasses bandwidth limitations and ensures the data is migrated quickly and securely.


NEW QUESTION # 29
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