RELIABLE ASSOCIATE-DATA-PRACTITIONER TEST BOOK & VCE ASSOCIATE-DATA-PRACTITIONER EXAM

Reliable Associate-Data-Practitioner Test Book & Vce Associate-Data-Practitioner Exam

Reliable Associate-Data-Practitioner Test Book & Vce Associate-Data-Practitioner Exam

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Tags: Reliable Associate-Data-Practitioner Test Book, Vce Associate-Data-Practitioner Exam, Associate-Data-Practitioner Reliable Exam Syllabus, Associate-Data-Practitioner Exam Dumps.zip, Reliable Associate-Data-Practitioner Braindumps

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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 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 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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Vce Associate-Data-Practitioner Exam | Associate-Data-Practitioner Reliable Exam Syllabus

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Google Cloud Associate Data Practitioner Sample Questions (Q71-Q76):

NEW QUESTION # 71
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?

  • A.
  • B.
  • C.
  • D.

Answer: C

Explanation:
To calculate the weekly moving average of sales by location:
The query must group by store_id (partitioning the calculation by each store).
The ORDER BY date ensures the sales are evaluated chronologically.
The ROWS BETWEEN 6 PRECEDING AND CURRENT ROW specifies a rolling window of 7 rows (1 week if each row represents daily data).
The AVG(total_sales) computes the average sales over the defined rolling window.
Chosen query meets these requirements:


NEW QUESTION # 72
You are a data analyst at your organization. You have been given a BigQuery dataset that includes customer information. The dataset contains inconsistencies and errors, such as missing values, duplicates, and formatting issues. You need to effectively and quickly clean the dat a. What should you do?

  • A. Use Cloud Data Fusion to create a data pipeline to read the data from BigQuery, perform data quality transformations, and write the clean data back to BigQuery.
  • B. Use BigQuery's built-in functions to perform data quality transformations.
  • C. Develop a Dataflow pipeline to read the data from BigQuery, perform data quality rules and transformations, and write the cleaned data back to BigQuery.
  • D. Export the data from BigQuery to CSV files. Resolve the errors using a spreadsheet editor, and re-import the cleaned data into BigQuery.

Answer: B

Explanation:
Using BigQuery's built-in functions is the most effective and efficient way to clean the dataset directly within BigQuery. BigQuery provides powerful SQL capabilities to handle missing values, remove duplicates, and resolve formatting issues without needing to export data or create complex pipelines. This approach minimizes overhead and leverages the scalability of BigQuery for large datasets, making it an ideal solution for quickly addressing data quality issues.


NEW QUESTION # 73
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 Storage using Archive storage.
  • B. Store the data in Cloud Storage using Standard storage.
  • C. Store the data in Cloud Storaqe usinq Nearline storaqe.
  • D. Store the data in Cloud Storaqe usinq Coldline storaqe.

Answer: C

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 # 74
You work for a healthcare company. You have a daily ETL pipeline that extracts patient data from a legacy system, transforms it, and loads it into BigQuery for analysis. The pipeline currently runs manually using a shell script. You want to automate this process and add monitoring to ensure pipeline observability and troubleshooting insights. You want one centralized solution, using open-source tooling, without rewriting the ETL code. What should you do?

  • A. Use Cloud Scheduler to trigger a Dataproc job to execute the pipeline daily. Monitor the job's progress using the Dataproc job web interface and Cloud Monitoring.
  • B. Configure Cloud Dataflow to implement the ETL pipeline, and use Cloud Scheduler to trigger the Dataflow pipeline daily. Monitor the pipelines execution using the Dataflow job monitoring interface and Cloud Monitoring.
  • C. Create a direct acyclic graph (DAG) in Cloud Composer to orchestrate a pipeline trigger daily. Monitor the pipeline's execution using the Apache Airflow web interface and Cloud Monitoring.
  • D. Create a Cloud Run function that runs the pipeline daily. Monitor the functions execution using Cloud Monitoring.

Answer: C

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why A is correct:Cloud Composer is a managed Apache Airflow service, which is a popular open-source workflow orchestration tool.
DAGs in Airflow can be used to automate ETL pipelines.
Airflow's web interface and Cloud Monitoring provide comprehensive monitoring capabilities.
It also allows you to run existing shell scripts.
Why other options are incorrect:B: Dataflow requires rewriting the ETL pipeline using its SDK.
C: Dataproc is for big data processing, not orchestration.
D: Cloud Run functions are for stateless applications, not long-running ETL pipelines.


NEW QUESTION # 75
You have an existing weekly Storage Transfer Service transfer job from Amazon S3 to a Nearline Cloud Storage bucket in Google Cloud. Each week, the job moves a large number of relatively small files. As the number of files to be transferred each week has grown over time, you are at risk of no longer completing the transfer in the allocated time frame. You need to decrease the total transfer time by replacing the process.
Your solution should minimize costs where possible. What should you do?

  • A. Create an agent-based transfer job that utilizes multiple transfer agents on Compute Engine instances.
  • B. Create a transfer job using the Google Cloud CLI, and specify the Standard storage class with the - custom-storage-class flag.
  • C. Create a batch Dataflow job that is scheduled weekly to migrate the data from Amazon S3 to Cloud Storage.
  • D. Create parallel transfer jobs using include and exclude prefixes.

Answer: D

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why B is correct:Creating parallel transfer jobs by using include and exclude prefixes allows you to split the data into smaller chunks and transfer them in parallel.
This can significantly increase throughput and reduce the overall transfer time.
Why other options are incorrect:A: Changing the storage class to Standard will not improve transfer speed.
C: Dataflow is a complex solution for a simple file transfer task.
D: Agent-based transfer is suitable for large files or network limitations, but not for a large number of small files.


NEW QUESTION # 76
......

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