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[General] Free PDF Quiz Google - Associate-Data-Practitioner - Latest Google Cloud Associa

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【General】 Free PDF Quiz Google - Associate-Data-Practitioner - Latest Google Cloud Associa

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Google Associate-Data-Practitioner Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 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 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

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Google Cloud Associate Data Practitioner Sample Questions (Q12-Q17):NEW QUESTION # 12
You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model.
What should you do?
  • A. Evaluate data drift.
  • B. Evaluate the data skewness.
  • C. Compare the confusion matrix.
  • D. Compare the two different models.
Answer: A
Explanation:
Evaluating data drift involves analyzing changes in the distribution of the current serving data compared to the historical data used to train the model. If significant drift is detected, it indicates that the data patterns have changed over time, which can impact the model's performance. This analysis helps determine whether retraining the model is necessary to ensure its predictions remain accurate and relevant. Data drift evaluation is a standard approach for monitoring machine learning models over time.

NEW QUESTION # 13
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 Worker role.
  • C. Request the Dataflow Developer role.
  • D. Request the Dataflow Admin 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 # 14
You are designing a BigQuery data warehouse with a team of experienced SQL developers. You need to recommend a cost-effective, fully-managed, serverless solution to build ELT processes with SQL pipelines.
Your solution must include source code control, environment parameterization, and data quality checks. What should you do?
  • A. Use Cloud Composer to orchestrate and run data workflows.
  • B. Use Dataform to build, orchestrate, and monitor the pipelines.
  • C. Use Cloud Data Fusion to visually design and manage the pipelines.
  • D. Use Dataproc to run MapReduce jobs for distributed data processing.
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation:
The solution must support SQL-based ELT, be serverless and cost-effective, and include advanced features like version control and quality checks. Let's dive in:
* Option A: Cloud Data Fusion is a visual ETL tool, not SQL-centric (uses plugins), and isn't fully serverless (requires instance management). It lacks native source code control and parameterization.
* Option B: Dataform is a serverless, SQL-based ELT platform for BigQuery. It uses SQLX scripts, integrates with Git for version control, supports environment variables (parameterization), and offers assertions for data quality-all meeting the requirements cost-effectively.
* Option C: Dataproc is for Spark/MapReduce, not SQL ELT, and requires cluster management, contradicting serverless and cost goals.

NEW QUESTION # 15
You want to process and load a daily sales CSV file stored in Cloud Storage into BigQuery for downstream reporting. You need to quickly build a scalable data pipeline that transforms the data while providing insights into data quality issues. What should you do?
  • A. Create a batch pipeline in Cloud Data Fusion by using a Cloud Storage source and a BigQuery sink.
  • B. Create a batch pipeline in Dataflow by using the Cloud Storage CSV file to BigQuery batch template.
  • C. Load the CSV file as a table in BigQuery, and use scheduled queries to run SQL transformation scripts.
  • D. Load the CSV file as a table in BigQuery. Create a batch pipeline in Cloud Data Fusion by using a BigQuery source and sink.
Answer: A
Explanation:
Using Cloud Data Fusion to create a batch pipeline with a Cloud Storage source and a BigQuery sink is the best solution because:
Scalability: Cloud Data Fusion is a scalable, fully managed data integration service.
Data transformation: It provides a visual interface to design pipelines, enabling quick transformation of data.
Data quality insights: Cloud Data Fusion includes built-in tools for monitoring and addressing data quality issues during the pipeline creation and execution process.

NEW QUESTION # 16
Your organization is conducting analysis on regional sales metrics. Data from each regional sales team is stored as separate tables in BigQuery and updated monthly. You need to create a solution that identifies the top three regions with the highest monthly sales for the next three months. You want the solution to automatically provide up-to-date results. What should you do?
  • A. Create a BigQuery materialized view that performs a cross join across all of the regional sales tables.Use the row_number() window function to query the new materialized view.
  • B. Create a BigQuery table that performs a cross join across all of the regional sales tables. Use the rank() window function to query the new table.
  • C. Create a BigQuery materialized view that performs a union across all of the regional sales tables. Use the rank() window function to query the new materialized view.
  • D. Create a BigQuery table that performs a union across all of the regional sales tables. Use the row_number() window function to query the new table.
Answer: C
Explanation:
Comprehensive and Detailed in Depth Explanation:
Why C is correct:Materialized views in BigQuery are precomputed views that periodically cache the results of a query. This ensures up-to-date results automatically.
A UNION is the correct operation to combine the data from multiple regional sales tables.
RANK() function is correct to rank the sales regions. ROW_NUMBER() would create a unique number for each row, even if sales amount is the same, this is not the desired function.
Why other options are incorrect:A and B: Standard tables do not provide automatic updates.
D: A CROSS JOIN would produce a Cartesian product, which is not appropriate for combining regional sales data.
Cross join is used when you want every combination of rows from tables, not a aggregation of data.

NEW QUESTION # 17
......
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