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Google ADP Exam Syllabus Topics:

Google Associate Data Practitioner Sample Questions:

1. Your company is building a near real-time streaming pipeline to process JSON telemetry data from small appliances. You need to process messages arriving at a Pub/Sub topic, capitalize letters in the serial number field, and write results to BigQuery. You want to use a managed service and write a minimal amount of code for underlying transformations. What should you do?

A) Use a Pub/Sub to BigQuery subscription, write results directly to BigQuery, and schedule a transformation query to run every five minutes.
B) Use a Pub/Sub to Cloud Storage subscription, write a Cloud Run service that is triggered when objects arrive in the bucket, performs the transformations, and writes the results to BigQuery.
C) Use the "Pub/Sub to BigQuery" Dataflow template with a UDF, and write the results to BigQuery.
D) Use a Pub/Sub push subscription, write a Cloud Run service that accepts the messages, performs the transformations, and writes the results to BigQuery.

2. 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.

3. Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?

A) Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
B) Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.
C) Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
D) Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.

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

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

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