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Google Associate Data Practitioner Sample Questions:

1. Your organization has a BigQuery dataset that contains sensitive employee information such as salaries and performance reviews. The payroll specialist in the HR department needs to have continuous access to aggregated performance data, but they do not need continuous access to other sensitive dat a. You need to grant the payroll specialist access to the performance data without granting them access to the entire dataset using the simplest and most secure approach. What should you do?

A) Create row-level and column-level permissions and policies on the table that contains performance data in the dataset. Provide the payroll specialist with the appropriate permission set.
B) Create a table with the aggregated performance data. Use table-level permissions to grant access to the payroll specialist.
C) Use authorized views to share query results with the payroll specialist.
D) Create a SQL query with the aggregated performance data. Export the results to an Avro file in a Cloud Storage bucket. Share the bucket with the payroll specialist.

2. Your organization uses a BigQuery table that is partitioned by ingestion time. You need to remove data that is older than one year to reduce your organization's storage costs. You want to use the most efficient approach while minimizing cost. What should you do?

A) Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.
B) Create a scheduled query that periodically runs an update statement in SQL that sets the "deleted" column to "yes" for data that is more than one year old. Create a view that filters out rows that have been marked deleted.
C) Create a view that filters out rows that are older than one year.
D) Require users to specify a partition filter using the alter table statement in SQL.

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 the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.
B) Use Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when 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 organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one- time SQL-based analysis of the files and join them to data that already resides in BigQuery. What should you do?

A) Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.
B) Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.
C) Launch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.
D) Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.

5. 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 pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.
B) 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.
C) 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.
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.

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