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Google ADP Exam Syllabus Topics:

Google Associate Data Practitioner Sample Questions:

1. You manage a Cloud Storage bucket that stores temporary files created during data processing. These temporary files are only needed for seven days, after which they are no longer needed. To reduce storage costs and keep your bucket organized, you want to automatically delete these files once they are older than seven days. What should you do?

A) Create a Cloud Run function that runs daily and deletes files older than seven days.
B) Develop a batch process using Dataflow that runs weekly and deletes files based on their age.
C) Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.
D) Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.

2. Your organization has decided to move their on-premises Apache Spark-based workload to Google Cloud.
You want to be able to manage the code without needing to provision and manage your own cluster. What should you do?

A) Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
B) Migrate the Spark jobs to Dataproc on Compute Engine.
C) Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
D) Migrate the Spark jobs to Dataproc Serverless.

3. You have a Dataflow pipeline that processes website traffic logs stored in Cloud Storage and writes the processed data to BigQuery. You noticed that the pipeline is failing intermittently. You need to troubleshoot the issue. What should you do?

A) Use Cloud Logging to identify error groups in the pipeline's logs. Use Cloud Monitoring to create a dashboard that tracks the number of errors in each group.
B) Use Cloud Logging to view error messages in the pipeline's logs. Use Cloud Monitoring to analyze the pipeline's metrics, such as CPU utilization and memory usage.
C) Use Cloud Logging to create a chart displaying the pipeline's error logs. Use Metrics Explorer to validate the findings from the chart.
D) Use the Dataflow job monitoring interface to check the pipeline's status every hour. Use Cloud Profiler to analyze the pipeline's metrics, such as CPU utilization and memory usage.

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

5. 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 a table with the aggregated performance data. Use table-level permissions to grant access to the payroll specialist.
B) Use authorized views to share query results with the payroll specialist.
C) 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.
D) 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.

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