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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Data Processing and Analytics	20-30%	- Use BigQuery and SQL for analytics - Aggregate and summarize data - Query and analyze datasets - Apply statistical methods for analysis - Build and maintain data pipelines
Data Visualization and Insights	20-30%	- Interpret and communicate findings - Present data insights to stakeholders - Create dashboards and reports - Choose appropriate visualization types - Build visualizations using Looker Studio
Data Preparation and Exploration	20-30%	- Identify data quality issues - Explore data through visualization and queries - Transform and prepare data for analysis - Perform exploratory data analysis (EDA) - Ingest and acquire data
Data-Driven Decision Making	10-20%	- Define success metrics - Assess data quality and completeness - Translate business requirements into data solutions - Identify stakeholders and requirements

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Google Associate Data Practitioner Sample Questions (Q10-Q15):

NEW QUESTION # 10
Your organization uses Dataflow pipelines to process real-time financial transactions. You discover that one of your Dataflow jobs has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

A. Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.
B. Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.
C. Create a custom script to periodically poll the Dataflow API for job status updates, and send email alerts if any errors are identified.
D. Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.

Answer: B

NEW QUESTION # 11
You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rows and transactional dat a. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost. What should you do?

A. Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.
B. Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.
C. Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.
D. Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.

Answer: A

NEW QUESTION # 12
Your company currently uses an on-premises network file system (NFS) and is migrating data to Google Cloud. You want to be able to control how much bandwidth is used by the data migration while capturing detailed reporting on the migration status. What should you do?

A. Use Cloud Storage FUSE.
B. Use Storage Transfer Service.
C. Use gcloud storage commands.
D. Use a Transfer Appliance.

Answer: B

NEW QUESTION # 13
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 processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task after correcting any stage output data errors.
B. 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.
C. Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline 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.

Answer: A

NEW QUESTION # 14
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. Filestore Bigtable BigQuery
B. Filestore Cloud SQL for PostgreSQL Datastore
C. Cloud Storage CloudSQL for PostgreSQL Bigtable
D. Cloud Storage BigQuery Firestore

Answer: D

NEW QUESTION # 15
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