Google ADP

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Google ADP Exam Syllabus Topics:

Google Associate Data Practitioner Sample Questions:

1. 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 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.
C) 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.
D) 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.

2. 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 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.
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 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.

3. Your company uses Looker to generate and share reports with various stakeholders. You have a complex dashboard with several visualizations that needs to be delivered to specific stakeholders on a recurring basis, with customized filters applied for each recipient. You need an efficient and scalable solution to automate the delivery of this customized dashboard. You want to follow the Google- recommended approach. What should you do?

A) Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.
B) Use the Looker Scheduler with a user attribute filter on the dashboard, and send the dashboard with personalized filters to each stakeholder based on their attributes.
C) Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.
D) Embed the Looker dashboard in a custom web application, and use the application's scheduling features to send the report with personalized filters.

4. Your organization needs to store historical customer order dat
a. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?

A) Store the data in Cloud Storage using Coldline storage.
B) Store the data in Cloud Storage using Archive storage.
C) Store the data in Cloud Storage using Standard storage.
D) Store the data in Cloud Storage using Nearline storage.

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 Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.
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 a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
D) Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.

Solutions: