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

2. Your company's customer support audio files are stored in a Cloud Storage bucket. You plan to analyze the audio files' metadata and file content within BigQuery to create inference by using BigQuery ML. You need to create a corresponding table in BigQuery that represents the bucket containing the audio files. What should you do?

A) Create an object table.
B) Create an external table.
C) Create a native table.
D) Create a temporary table.

3. Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?

A) Use Cloud Tasks to schedule and run the jobs asynchronously.
B) Create directed acyclic graphs (DAGS) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
C) Use Cloud Scheduler to schedule the jobs to run.
D) Create directed acyclic graphs (DAGS) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.

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

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

Solutions: