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[General] Google Associate-Data-Practitioner PDF Dumps Files, New Associate-Data-Practitio

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【General】 Google Associate-Data-Practitioner PDF Dumps Files, New Associate-Data-Practitio

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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.

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Google Cloud Associate Data Practitioner Sample Questions (Q62-Q67):NEW QUESTION # 62
Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?
  • A. Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.
  • B. Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
  • C. Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
  • D. Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation:
For a 25 TB dataset, efficiency and cost require minimizing data movement and leveraging BigQuery's scalability within Colab Enterprise.
* Option A: Exporting 25 TB to Google Drive and loading via Pandas is impractical (size limits, transfer costs) and slow.
* Option B: BigQuery magic commands (%%bigquery) in Colab Enterprise allow direct querying of BigQuery data, keeping processing in the cloud, reducing costs, and enabling collaboration.
* Option C: Dataproc with Spark adds cluster costs and complexity, unnecessary when BigQuery can handle the workload.

NEW QUESTION # 63
Your organization needs to store historical customer order dat
a. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?
  • A. Store the data in Cloud Storaqe usinq Coldline storaqe.
  • B. Store the data in Cloud Storage using Standard storage.
  • C. Store the data in Cloud Storage using Archive storage.
  • D. Store the data in Cloud Storaqe usinq Nearline storaqe.
Answer: D
Explanation:
Using Nearline storage in Cloud Storage is the best option for data that is accessed infrequently (such as once a month) but must be readily available within seconds when needed. Nearline offers a balance between low storage costs and quick retrieval times, making it ideal for scenarios like monthly analysis of historical data. It is specifically designed for infrequent access patterns while avoiding the higher retrieval costs and longer access times of Coldline or Archive storage.

NEW QUESTION # 64
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. Migrate the database to a larger Cloud SQL instance.
  • B. Use Cloud CDN to cache frequently accessed data.
  • C. Enable automatic backups, and create a read replica of the Cloud SQL instance.
  • D. Store frequently accessed data in a Memorystore instance.
Answer: C
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 # 65
You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach. What should you do?
  • A. Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.
  • B. Use Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.
  • C. Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.
  • D. Create a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation:
For a small dataset with simple transformations (filtering, aggregation), Google recommends leveraging BigQuery's native SQL capabilities to minimize cost and complexity.
* Option A: Dataproc with Spark is overkill for a small dataset, incurring cluster management costs and setup time.
* Option B: BigQuery can load data directly from Cloud Storage (e.g., CSV, JSON) and perform transformations using SQL in a serverless manner, avoiding additional service costs. This is the most efficient and cost-effective approach.
* Option C: Cloud Data Fusion is suited for complex ETL but adds overhead (instance setup, UI design) unnecessary for simple tasks.

NEW QUESTION # 66
Your company is setting up an enterprise business intelligence platform. You need to limit data access between many different teams while following the Google-recommended approach. What should you do first?
  • A. Create a Looker (Google Cloud core) instance, and create a separate dashboard for each team.
  • B. Create a separate Looker Studio report for each team, and share each report with the individuals within each team.
  • C. Create a Looker (Google Cloud core) instance, and configure different Looker groups for each team.
  • D. Create one Looker Studio report with multiple pages, and add each team's data as a separate data source to the report.
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation:
For an enterprise BI platform with data access control across teams, Google recommends Looker (Google Cloud core) over Looker Studio for its robust access management. The "first" step focuses on setting up the foundation.
* Option A: Looker Studio reports are lightweight but lack granular access control beyond sharing.
Creating separate reports per team is inefficient and unscalable.
* Option B: One Looker Studio report with multiple pages and data sources doesn't enforce team-level access control natively-users could access all pages/data.
* Option C: Creating a Looker instance with separate dashboards per team is a step forward but skips the foundational access control setup (groups), reducing scalability.

NEW QUESTION # 67
......
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