Google ADP : Associate Data Practitioner

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Google ADP Exam Syllabus Topics:

Google Associate Data Practitioner Sample Questions:

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

2. Your organization's ecommerce website collects user activity logs using a Pub/Sub topic. Your organization's leadership team wants a dashboard that contains aggregated user engagement metrics. You need to create a solution that transforms the user activity logs into aggregated metrics, while ensuring that the raw data can be easily queried. What should you do?

A) Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.
B) Create a BigQuery subscription to the Pub/Sub topic, and load the activity logs into the table. Create a materialized view in BigQuery using SQL to transform the data for reporting
C) Create an event-driven Cloud Run function to trigger a data transformation pipeline to run. Load the transformed activity logs into a BigQuery table for reporting.
D) Create a Cloud Storage subscription to the Pub/Sub topic. Load the activity logs into a bucket using the Avro file format. Use Dataflow to transform the data, and load it into a BigQuery table for reporting.

3. You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?

A) Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
B) Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
C) Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.
D) Use the BigQuery Python client library in a Jupyter notebook to query and preprocess the data in BigQuery. Use the CREATE MODEL statement in BigQueryML to train the churn prediction model.

4. You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach. What should you do?

A) Create a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.
B) Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.
C) Use Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.
D) Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.

5. Your organization is building a new application on Google Cloud. Several data files will need to be stored in Cloud Storage. Your organization has approved only two specific cloud regions where these data files can reside. You need to determine a Cloud Storage bucket strategy that includes automated high availability.
What should you do?

A) Create a single-region bucket in each of the two regions, and use the gcloud storage command to replicate the data across the buckets in both regions.
B) Create a multi-region bucket, and upload the files to this bucket.
C) Create a dual-region bucket, and upload the files to this bucket.
D) Create a single-region bucket in each of the two regions, and use Storage Transfer Service to replicate the data across the buckets in both regions.

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