GOOGLE ASSOCIATE-DATA-PRACTITIONER RELIABLE BRAINDUMPS PDF: GOOGLE CLOUD ASSOCIATE DATA PRACTITIONER - PRACTICETORRENT FREE PDF

Google Associate-Data-Practitioner Reliable Braindumps Pdf: Google Cloud Associate Data Practitioner - PracticeTorrent Free PDF

Google Associate-Data-Practitioner Reliable Braindumps Pdf: Google Cloud Associate Data Practitioner - PracticeTorrent Free PDF

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To attempt the Google Associate-Data-Practitioner exam optimally and ace it on the first attempt, proper exam planning is crucial. Since the Google Cloud Associate Data Practitioner (Associate-Data-Practitioner) exam demands a lot of time and effort, we designed the Google Cloud Associate Data Practitioner (Associate-Data-Practitioner) exam dumps in such a way that you won't have to go through sleepless study nights or disturb your schedule. Before starting the Google Cloud Associate Data Practitioner (Associate-Data-Practitioner) preparation, plan the amount of time you will allot to each topic, determine the topics that demand more effort and prioritize the components that possess more weightage in the Google Cloud Associate Data Practitioner (Associate-Data-Practitioner) exam.

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 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 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.

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Google Cloud Associate Data Practitioner Sample Questions (Q10-Q15):

NEW QUESTION # 10
You are building a batch data pipeline to process 100 GB of structured data from multiple sources for daily reporting. You need to transform and standardize the data prior to loading the data to ensure that it is stored in a single dataset. You want to use a low-code solution that can be easily built and managed. What should you do?

  • A. Use Cloud Data Fusion to ingest data and load the data into BigQuery. Use Looker Studio to perform data cleaning and transformation.
  • B. Use Cloud Data Fusion to ingest the data, perform data cleaning and transformation, and load the data into BigQuery.
  • C. Use Cloud Storage to store the data. Use Cloud Run functions to perform data cleaning and transformation, and load the data into BigQuery.
  • D. Use Cloud Data Fusion to ingest the data, perform data cleaning and transformation, and load the data into Cloud SQL for PostgreSQL.

Answer: B

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why B is correct:Cloud Data Fusion is a fully managed, cloud-native data integration service for building and managing ETL/ELT data pipelines.
It provides a graphical interface for building pipelines without coding, making it a low-code solution.
Cloud data fusion is perfect for the ingestion, transformation and loading of data into BigQuery.
Why other options are incorrect:A: Looker studio is for visualization, not data transformation.
C: Cloud SQL is a relational database, not ideal for large-scale analytical data.
D: Cloud run is for stateless applications, not batch data processing.


NEW QUESTION # 11
Your organization needs to store historical customer order data. 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 Nearline storaqe.
  • B. Store the data in Cloud Storage using Archive storage.
  • C. Store the data in Cloud Storaqe usinq Coldline storaqe.
  • D. Store the data in Cloud Storage using Standard storage.

Answer: A

Explanation:
UsingNearline storagein 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 # 12
Your company is migrating their batch transformation pipelines to Google Cloud. You need to choose a solution that supports programmatic transformations using only SQL. You also want the technology to support Git integration for version control of your pipelines. What should you do?

  • A. Use Cloud Composer operators.
  • B. Use Dataform workflows.
  • C. Use Cloud Data Fusion pipelines.
  • D. Use Dataflow pipelines.

Answer: B

Explanation:
Dataform workflowsare the ideal solution for migrating batch transformation pipelines to Google Cloud when you want to perform programmatic transformations using only SQL. Dataform allows you to define SQL- based workflows for data transformations and supports Git integration for version control, enabling collaboration and version tracking of your pipelines. This approach is purpose-built for SQL-driven data pipeline management and aligns perfectly with your requirements.
The solution must use SQL for transformations and integrate with Git for version control, focusing on batch pipelines. Let's evaluate:
* Option A: Cloud Data Fusion uses a visual UI with plugins, not SQL-only transformations. It lacks native Git integration (requires external tools), missing a key requirement.
* Option B: Dataform is a SQL-based workflow tool for BigQuery transformations, defining pipelines as SQLX scripts. It integrates natively with Git for version control, supporting batch ELT processes with minimal overhead.
* Option C: Cloud Composer uses Python DAGs and operators, not SQL-only transformations. Git is possible but not intrinsic to its workflow design.


NEW QUESTION # 13
Your organization uses scheduled queries to perform transformations on data stored in BigQuery. You discover that one of your scheduled queries has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

  • A. Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.
  • B. Navigate to the Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.
  • C. Set up a log sink using the gcloud CLI to export BigQuery audit logs to BigQuery. Query those logs to identify the error associated with the failed job ID.
  • D. Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.

Answer: D


NEW QUESTION # 14
Your team uses Google Sheets to track budget data that is updated daily. The team wants to compare budget data against actual cost data, which is stored in a BigQuery table. You need to create a solution that calculates the difference between each day's budget and actual costs. You want to ensure that your team has access to daily-updated results in Google Sheets. What should you do?

  • A. Create a BigQuery external table by using the Drive URI of the Google sheet, and join the actual cost table with it. Save the joined table as a CSV file and open the file in Google Sheets.
  • B. Download the budget data as a CSV file and upload the CSV file to a Cloud Storage bucket. Create a new BigQuery table from Cloud Storage, and join the actual cost table with it. Open the joined BigQuery table by using Connected Sheets.
  • C. Create a BigQuery external table by using the Drive URI of the Google sheet, and join the actual cost table with it. Save the joined table, and open it by using Connected Sheets.
  • D. Download the budget data as a CSV file, and upload the CSV file to create a new BigQuery table. Join the actual cost table with the new BigQuery table, and save the results as a CSV file. Open the CSV file in Google Sheets.

Answer: C

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why D is correct:Creating a BigQuery external table directly from the Google Sheet allows for real-time updates.
Joining the external table with the actual cost table in BigQuery performs the calculation.
Connected Sheets allows the team to access and analyze the results directly in Google Sheets, with the data being updated.
Why other options are incorrect:A: Saving as a CSV file loses the live connection and daily updates.
B: Downloading and uploading as a CSV file adds unnecessary steps and loses the live connection.
C: Same issue as B, losing the live connection.


NEW QUESTION # 15
......

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