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Google Associate Data Practitioner Sample Questions:

1. You are working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of dat a. What should you do?

A) Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.
B) Load the raw data into BigQuery using Cloud Storage as a staging area, and use SQL queries in BigQuery to validate and clean the data.
C) Use Dataflow to create a streaming pipeline that includes validation and transformation steps.
D) Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.

2. Your organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one- time SQL-based analysis of the files and join them to data that already resides in BigQuery. What should you do?

A) Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.
B) Launch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.
C) Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.
D) Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.

3. You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach. What should you do?

A) Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.
B) Use Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.
C) Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.
D) Create a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.

4. Your company stores historical data in Cloud Storage. You need to ensure that all data is saved in a bucket for at least three years. What should you do?

A) Set temporary object holds.
B) Change the bucket storage class to Archive.
C) Set a bucket retention policy.
D) Enable Object Versioning.

5. 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) Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.
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) Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.

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