FREE ASSOCIATE-DATA-PRACTITIONER PRACTICE EXAMS | ASSOCIATE-DATA-PRACTITIONER PDF FREE

Free Associate-Data-Practitioner Practice Exams | Associate-Data-Practitioner Pdf Free

Free Associate-Data-Practitioner Practice Exams | Associate-Data-Practitioner Pdf Free

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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.

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Google Cloud Associate Data Practitioner Sample Questions (Q62-Q67):

NEW QUESTION # 62
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 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.
  • C. Use Cloud Run functions to clean the data and load it 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 # 63
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 the buckets to use the Archive storage class.
  • B. Configure the buckets to use the Autoclass feature.
  • C. Configure a lifecycle management policy on each bucket to downgrade the storage class and remove objects based on age.
  • D. Configure the buckets to use the Standard storage class and enable Object Versioning.

Answer: C

Explanation:
Configuring a lifecycle management policy on 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.


NEW QUESTION # 64
You have a BigQuery dataset containing sales data. This data is actively queried for the first 6 months. After that, the data is not queried but needs to be retained for 3 years for compliance reasons. You need to implement a data management strategy that meets access and compliance requirements, while keeping cost and administrative overhead to a minimum. What should you do?

  • A. Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.
  • B. Partition a BigQuery table by month. After 6 months, export the data to Coldline storage. Implement a lifecycle policy to delete the data from Cloud Storage after 3 years.
  • C. Set up a scheduled query to export the data to Cloud Storage after 6 months. Write a stored procedure to delete the data from BigQuery after 3 years.
  • D. Store all data in a single BigQuery table without partitioning or lifecycle policies.

Answer: B

Explanation:
Partitioning the BigQuery table by month allows efficient querying of recent data for the first 6 months, reducing query costs. After 6 months, exporting the data toColdline storageminimizes storage costs for data that is rarely accessed but needs to be retained for compliance. Implementing a lifecycle policy in Cloud Storage automates the deletion of the data after 3 years, ensuring compliance while reducing administrative overhead. This approach balances cost efficiency and compliance requirements effectively.


NEW QUESTION # 65
You recently inherited a task for managing Dataflow streaming pipelines in your organization and noticed that proper access had not been provisioned to you. You need to request a Google-provided IAM role so you can restart the pipelines. You need to follow the principle of least privilege. What should you do?

  • A. Request the Dataflow Viewer role.
  • B. Request the Dataflow Admin role.
  • C. Request the Dataflow Developer role.
  • D. Request the Dataflow Worker role.

Answer: C

Explanation:
The Dataflow Developer role provides the necessary permissions to manage Dataflow streaming pipelines, including the ability to restart pipelines. This role adheres to the principle of least privilege, as it grants only the permissions required to manage and operate Dataflow jobs without unnecessary administrative access. Other roles, such as Dataflow Admin, would grant broader permissions, which are not needed in this scenario.


NEW QUESTION # 66
You manage a web application that stores data in a Cloud SQL database. You need to improve the read performance of the application by offloading read traffic from the primary database instance. You want to implement a solution that minimizes effort and cost. What should you do?

  • A. Enable automatic backups, and create a read replica of the Cloud SQL instance.
  • B. Migrate the database to a larger Cloud SQL instance.
  • C. Store frequently accessed data in a Memorystore instance.
  • D. Use Cloud CDN to cache frequently accessed data.

Answer: A

Explanation:
Enabling automatic backups and creating a read replica of the Cloud SQL instance is the best solution to improve read performance. Read replicas allow you to offload read traffic from the primary database instance, reducing its load and improving overall performance. This approach is cost-effective and easy to implement within Cloud SQL. It ensures that the primary instance focuses on write operations while replicas handle read queries, providing a seamless performance boost with minimal effort.


NEW QUESTION # 67
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