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Google ADP Exam Syllabus Topics:

Google Associate Data Practitioner Sample Questions:

1. You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?

A) Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
B) Use Cloud Composer to orchestrate the Spark job and email the report.
C) Use Cloud Run functions to trigger the Spark job and email the report.
D) Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.

2. You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need toclean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?

A) Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
B) Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
C) Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.
D) Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.

3. You are a data analyst working with sensitive customer data in BigQuery. You need to ensure that only authorized personnel within your organization can query this data, while following the principle of least privilege. What should you do?

A) Enable access control by using IAM roles.
B) Update dataset privileges by using the SQL GRANT statement.
C) Export the data to Cloud Storage, and use signed URLs to authorize access.
D) Encrypt the data by using customer-managed encryption keys (CMEK).

4. You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?

A) Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
B) Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
C) Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
D) Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.

5. 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) 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.
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) Copy the storm event dataset into your BigQuery project. Use BigQuery Studio to query and visualize the data in Looker Studio.
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.

Solutions: