SAVE TIME AND MONEY WITH OUR GOOGLE ASSOCIATE-DATA-PRACTITIONER EXAM QUESTIONS

Save Time and Money with Our Google Associate-Data-Practitioner Exam Questions

Save Time and Money with Our Google Associate-Data-Practitioner Exam Questions

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Our Associate-Data-Practitioner practice exams lower the likelihood of failing and are optimal for self-evaluation. You can access the web-based Google Cloud Associate Data Practitioner (Associate-Data-Practitioner) practice exam online without having to install any software. All browsers and operating systems support our web-based Associate-Data-Practitioner Practice Test. Every Windows computer supports our desktop Google Associate-Data-Practitioner practice exam software, enabling you to prepare for the Associate-Data-Practitioner test without an active internet connection. You can customize the time and types of these Google Questions.

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

Google Cloud Associate Data Practitioner Sample Questions (Q13-Q18):

NEW QUESTION # 13
Your company has an on-premises file server with 5 TB of data that needs to be migrated to Google Cloud.
The network operations team has mandated that you can only use up to 250 Mbps of the total available bandwidth for the migration. You need to perform an online migration to Cloud Storage. What should you do?

  • A. Use the gcloud storage cp command to copy all files from on-premises to Cloud Storage using the --no- clobber option.
  • B. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.
  • C. Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.
  • D. Use the gcloud storage cp command to copy all files from on-premises to Cloud Storage using the -- daisy-chain option.

Answer: C

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why A is correct:Storage Transfer Service with agent-based transfer allows for online migrations and provides the ability to set bandwidth limits.
Agents are installed on-premises and can be configured to respect network constraints.
Why other options are incorrect:B: The --daisy-chain option is not related to bandwidth control.
C: Transfer Appliance is for offline migrations and is not suitable for online transfers with bandwidth constraints.
D: The --no-clobber option prevents overwriting existing files but does not control bandwidth.


NEW QUESTION # 14
Your retail company wants to predict customer churn using historical purchase data stored in BigQuery. The dataset includes customer demographics, purchase history, and a label indicating whether the customer churned or not. You want to build a machine learning model to identify customers at risk of churning. You need to create and train a logistic regression model for predicting customer churn, using the customer_data table with the churned column as the target label. Which BigQuery ML query should you use?

  • A. CREATE OR REPLACE MODEL churn_prediction_model options(model_type='logistic_reg*) as select ' except(churned) FROM customer data;
  • B. CREATE OR REPLACE MODEL churn_prediction_model OPTIONS(model_uype='logisric_reg') AS SELECT * from cusromer_data;
  • C. CREATE OR REPLACE MODEL churn_prediction_model OPTIONS (rr.odel_type=' logisric_reg *) AS select * except(churned), churned AS label FROM customer_data;
  • D. CREATE OR REPLACE MODEL churn_prediction_model options (model type='logistic_reg') AS select churned as label FROM customer_data;

Answer: C

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why B is correct:BigQuery ML requires the target label to be explicitly named label.
EXCEPT(churned) selects all columns except the churned column, which becomes the features.
churned AS label renames the churned column to label, which is required for BigQuery ML.
logistic_reg is the correct model_type option.
Why other options are incorrect:A: Does not rename the target column to label. Also has a typo in the model type.
C: Only selects the target label, not the features.
D: Has a syntax error with the single quote before except.


NEW QUESTION # 15
You work for a home insurance company. You are frequently asked to create and save risk reports with charts for specific areas using a publicly available storm event dataset. You want to be able to quickly create and re-run risk reports when new data becomes available. What should you do?

  • A. Copy the storm event dataset into your BigQuery project. Use BigQuery Studio to query and visualize the data in Looker Studio.
  • B. Reference and query the storm event dataset using SQL in a Colab Enterprise notebook. Display the table results and document with Markdown, and use Matplotlib to create charts.
  • C. Export the storm event dataset as a CSV file. Import the file to Google Sheets, and use cell data in the worksheets to create charts.
  • D. Reference and query the storm event dataset using SQL in BigQuery Studio. Export the results to Google Sheets, and use cell data in the worksheets to create charts.

Answer: A

Explanation:
Copying the storm event dataset into your BigQuery project and using BigQuery Studio to query and visualize the data in Looker Studio is the best approach. This solution allows you to create reusable and automated workflows for generating risk reports. BigQuery handles the querying efficiently, and Looker Studio provides powerful tools for creating and sharing dynamic charts and dashboards. This setup ensures that reports can be easily re-run with updated data, minimizing manual effort and providing a scalable, interactive solution for visualizing risk reports.


NEW QUESTION # 16
Your organization has a BigQuery dataset that contains sensitive employee information such as salaries and performance reviews. The payroll specialist in the HR department needs to have continuous access to aggregated performance data, but they do not need continuous access to other sensitive dat a. You need to grant the payroll specialist access to the performance data without granting them access to the entire dataset using the simplest and most secure approach. What should you do?

  • A. Use authorized views to share query results with the payroll specialist.
  • B. Create a SQL query with the aggregated performance data. Export the results to an Avro file in a Cloud Storage bucket. Share the bucket with the payroll specialist.
  • C. Create row-level and column-level permissions and policies on the table that contains performance data in the dataset. Provide the payroll specialist with the appropriate permission set.
  • D. Create a table with the aggregated performance data. Use table-level permissions to grant access to the payroll specialist.

Answer: A

Explanation:
Using authorized views is the simplest and most secure way to grant the payroll specialist access to aggregated performance data without exposing the entire dataset. Authorized views allow you to create a view in BigQuery that contains only the query results for the aggregated performance data. The payroll specialist can query the view without being granted access to the underlying sensitive data. This approach ensures security, adheres to the principle of least privilege, and eliminates the need to manage complex row-level or column-level permissions.


NEW QUESTION # 17
You are using your own data to demonstrate the capabilities of BigQuery to your organization's leadership team. You need to perform a one-time load of the files stored on your local machine into BigQuery using as little effort as possible. What should you do?

  • A. Write and execute a Python script using the BigQuery Storage Write API library.
  • B. Execute the bq load command on your local machine.
  • C. Create a Dataflow job using the Apache Beam FileIO and BigQueryIO connectors with a local runner.
  • D. Create a Dataproc cluster, copy the files to Cloud Storage, and write an Apache Spark job using the spark-bigquery-connector.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation:
A one-time load with minimal effort points to a simple, out-of-the-box tool. The files are local, so the solution must bridge on-premises to BigQuery easily.
* Option A: A Python script with the Storage Write API requires coding, setup (authentication, libraries), and debugging-more effort than necessary for a one-time task.
* Option B: Dataproc with Spark involves cluster creation, file transfer to Cloud Storage, and job scripting-far too complex for a simple load.
* Option C: The bq load command (part of the Google Cloud SDK) is a CLI tool that uploads local files (e.g., CSV, JSON) directly to BigQuery with one command (e.g., bq load --source_format=CSV dataset.
table file.csv). It's pre-built, requires no coding, and leverages existing SDK installation, minimizing effort.


NEW QUESTION # 18
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