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Google ADP Exam Syllabus Topics:

Google Associate Data Practitioner Sample Questions:

1. 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) 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.
C) Create a Cloud Run function that runs the pipeline daily. Monitor the functions execution using Cloud Monitoring.
D) 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.

2. Your company uses Looker to visualize and analyze sales dat
a. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?

A) Create a single Explore with all sales metrics. Build the dashboard using this Explore.
B) Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
C) Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
D) Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.

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

4. You created a customer support application that sends several forms of data to Google Cloud. Your application is sending:
1. Audio files from phone interactions with support agents that will be accessed during trainings.
2. CSV files of users' personally identifiable information (PII) that will be analyzed with SQL.
3. A large volume of small document files that will power other applications.
You need to select the appropriate tool for each data type given the required use case, while following Google- recommended practices. Which should you choose?

A) Cloud Storage BigQuery Firestore
B) Cloud Storage CloudSQL for PostgreSQL Bigtable
C) Filestore Cloud SQL for PostgreSQL Datastore
D) Filestore Bigtable BigQuery

5. You manage a Cloud Storage bucket that stores temporary files created during data processing. These temporary files are only needed for seven days, after which they are no longer needed. To reduce storage costs and keep your bucket organized, you want to automatically delete these files once they are older than seven days. What should you do?

A) Create a Cloud Run function that runs daily and deletes files older than seven days.
B) Develop a batch process using Dataflow that runs weekly and deletes files based on their age.
C) Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.
D) Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.

Solutions: