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Google Associate Data Practitioner Sample Questions:

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

2. You are designing a BigQuery data warehouse with a team of experienced SQL developers. You need to recommend a cost- effective, fully-managed, serverless solution to build ELT processes with SQL pipelines.
Your solution must include source code control, environment parameterization, and data quality checks. What should you do?

A) Use Cloud Data Fusion to visually design and manage the pipelines.
B) Use Cloud Composer to orchestrate and run data workflows.
C) Use Dataproc to run MapReduce jobs for distributed data processing.
D) Use Dataform to build, orchestrate, and monitor the pipelines.

3. Your organization's website uses an on-premises MySQL as a backend database. You need to migrate the on-premises MySQL database to Google Cloud while maintaining MySQL features. You want to minimize administrative overhead and downtime. What should you do?

A) Install MySQL on a Compute Engine virtual machine. Export the database files using the mysqldump command. Upload the files to Cloud Storage, and import them into the MySQL instance on Compute Engine.
B) Export the database tables to CSV files, and upload the files to Cloud Storage. Convert the MySQL schema to a Spanner schema, create a JSON manifest file, and run a Google-provided Dataflow template to load the data into Spanner.
C) Use a Google-provided Dataflow template to replicate the MySQL database in BigQuery.
D) Use Database Migration Service to transfer the data to Cloud SQL for MySQL, and configure the on premises MySQL database as the source.

4. Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards.
You need to implement a solution quickly and efficiently. What should you do?

A) Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
B) Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
C) Apply a filter to only show products with a positive profit margin.
D) Define a new measure that calculates the profit margin by using the existing revenue and cost fields.

5. 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 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.
B) Use a Pub/Sub push subscription, write a Cloud Run service that accepts the messages, performs the transformations, and writes the results to BigQuery.
C) Use a Pub/Sub to BigQuery subscription, write results directly to BigQuery, and schedule a transformation query to run every five minutes.
D) Use the "Pub/Sub to BigQuery" Dataflow template with a UDF, and write the results to BigQuery.

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