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Google Associate Data Practitioner Sample Questions:

1. You are designing a pipeline to process data files that arrive in Cloud Storage by 3:00 am each day. Data processing is performed in stages, where the output of one stage becomes the input of the next. Each stage takes a long time to run. Occasionally a stage fails, and you have to address the problem. You need to ensure that the final output is generated as quickly as possible. What should you do?

A) Design the processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task after correcting any stage output data errors.
B) Design the workflow as a Cloud Workflow instance. Code the workflow to jump to a given stage based on an input parameter. Rerun the workflow after correcting any stage output data errors.
C) Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.
D) Design a Spark program that runs under Dataproc. Code the program to wait for user input when an error is detected. Rerun the last action after correcting any stage output data errors.

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

3. 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.

4. You are working on a project that requires analyzing daily social media dat a. You have 100 GB of JSON formatted data stored in Cloud Storage that keeps growing.
You need to transform and load this data into BigQuery for analysis. You want to follow the Google-recommended approach. What should you do?

A) Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
B) Use Cloud Run functions to transform and load the data into BigQuery.
C) Use Dataflow to transform the data and write the transformed data to BigQuery.
D) Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.

5. You need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage.
The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis.
The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team's existing skillset, processes data at scale, and minimizes cost. What should you do?

A) Use Cloud Data Fusion to visually design and manage the pipeline.
B) Use Dataform to define the transformations in SQLX.
C) Use Dataflow with a custom template for the transformation logic.
D) Use Dataproc to run the transformations on a cluster.

Solutions: