Google Associate Data Practitioner Sample Questions:

1. Your retail company wants to analyze customer reviews to understand sentiment and identify areas for improvement. Your company has a large dataset of customer feedback text stored in BigQuery that includes diverse language patterns, emojis, and slang. You want to build a solution to classify customer sentiment from the feedback text. What should you do?

A) Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.
B) Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.
C) Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
D) Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.

2. You created a customer support application that sends several forms of data to Google Cloud. Your application is sending:
1. Audio files from phone interactions with support agents that will be accessed during trainings.
2. CSV files of users' personally identifiable information (PII) that will be analyzed with SQL.
3. A large volume of small document files that will power other applications.
You need to select the appropriate tool for each data type given the required use case, while following Google- recommended practices. Which should you choose?

A) Cloud Storage CloudSQL for PostgreSQL Bigtable
B) Filestore Bigtable BigQuery
C) Filestore Cloud SQL for PostgreSQL Datastore
D) Cloud Storage BigQuery Firestore

3. You manage data at an ecommerce company. You have a Dataflow pipeline that processes order data from Pub/Sub, enriches the data with product information from Bigtable, and writes the processed data to BigQuery for analysis. The pipeline runs continuously and processes thousands of orders every minute. You need to monitor the pipeline's performance and be alerted if errors occur. What should you do?

A) Use Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.
B) Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.
C) Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.
D) Use BigQuery to analyze the processed data in Cloud Storage and identify anomalies or inconsistencies. Set up scheduled alerts based when anomalies or inconsistencies occur.

4. Your company wants to implement a data transformation (ETL) pipeline for their BigQuery data warehouse.
You need to identify a managed transformation solution that allows users to develop with SQL and JavaScript, has version control, allows for modular code, and has data quality checks. What should you do?

A) Use Dataform to define the transformations in SQLX.
B) Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.
C) Create BigQuery scheduled queries to define the transformations in SQL.
D) Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.

5. 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 a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
B) 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.
C) 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.
D) Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.

Solutions: