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

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

3. Your organization uses Dataflow pipelines to process real-time financial transactions. You discover that one of your Dataflow jobs has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

A) Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.
B) Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.
C) Create a custom script to periodically poll the Dataflow API for job status updates, and send email alerts if any errors are identified.
D) Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.

4. You need to create a data pipeline that streams event information from applications in multiple Google Cloud regions into BigQuery for near real-time analysis. The data requires transformation before loading. You want to create the pipeline using a visual interface. What should you do?

A) Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.
B) Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.
C) Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
D) Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.

5. 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 BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
B) Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for 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.

Solutions: