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Google ADP Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Management and Governance | 25% | - Data quality and maintenance
|
| Data Preparation and Ingestion | 30% | - Storage solutions selection
|
| Data Analysis and Presentation | 27% | - Data exploration and analysis
|
| Data Pipeline Orchestration | 18% | - Pipeline automation and scheduling
|
Google Associate Data Practitioner Sample Questions:
Question 1
You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage.
The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery. Which data manipulation methodology should you choose?
A. ETLT
B. EL
C. ETL
D. ELT
Question 2
Your organization plans to move their on-premises environment to Google Cloud. Your organization's network bandwidth is less than 1 Gbps. You need to move over 500 ## of data to Cloud Storage securely, and only have a few days to move the dat a. What should you do?
A. Connect to Google Cloud using Dedicated Interconnect. Use the gcloud storage command to move the data to Cloud Storage.
B. Connect to Google Cloud using VPN. Use Storage Transfer Service to move the data to Cloud Storage.
C. Connect to Google Cloud using VPN. Use the gcloud storage command to move the data to Cloud Storage.
D. Request multiple Transfer Appliances, copy the data to the appliances, and ship the appliances back to Google Cloud to upload the data to Cloud Storage.
Question 3
You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?
A. Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
B. Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
C. Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
D. Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
Question 4
You created a curated dataset of market trends in BigQuery that you want to share with multiple external partners. You want to control the rows and columns that each partner has access to. You want to follow Google-recommended practices. What should you do?
A. Create a separate Cloud Storage bucket for each partner. Export the dataset to each bucket and assign each partner to their respective bucket. Grant bucket-level access by using 1AM roles.
B. Grant each partner read access to the BigQuery dataset by using 1AM roles.
C. Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
D. Create a separate project for each partner and copy the dataset into each project. Publish each dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
Question 5
Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?
A. Create directed acyclic graphs (DAGS) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
B. Use Cloud Scheduler to schedule the jobs to run.
C. Use Cloud Tasks to schedule and run the jobs asynchronously.
D. Create directed acyclic graphs (DAGS) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
Solutions:
| Question 1 Answer: C | Question 2 Answer: D | Question 3 Answer: C | Question 4 Answer: C | Question 5 Answer: A |
