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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Preparation and Exploration | 20-30% | - Explore data through visualization and queries - Transform and prepare data for analysis - Ingest and acquire data - Perform exploratory data analysis (EDA) - Identify data quality issues |
| Topic 2: Data Processing and Analytics | 20-30% | - Query and analyze datasets - Use BigQuery and SQL for analytics - Build and maintain data pipelines - Aggregate and summarize data - Apply statistical methods for analysis |
| Topic 3: Data Visualization and Insights | 20-30% | - Create dashboards and reports - Build visualizations using Looker Studio - Present data insights to stakeholders - Interpret and communicate findings - Choose appropriate visualization types |
| Topic 4: Data-Driven Decision Making | 10-20% | - Translate business requirements into data solutions - Define success metrics - Identify stakeholders and requirements - Assess data quality and completeness |
1. You are designing a BigQuery data warehouse with a team of experienced SQL developers. You need to recommend a cost- effective, fully-managed, serverless solution to build ELT processes with SQL pipelines.
Your solution must include source code control, environment parameterization, and data quality checks. What should you do?
A) Use Cloud Data Fusion to visually design and manage the pipelines.
B) Use Cloud Composer to orchestrate and run data workflows.
C) Use Dataproc to run MapReduce jobs for distributed data processing.
D) Use Dataform to build, orchestrate, and monitor the pipelines.
2. Your company has an on-premises file server with 5 TB of data that needs to be migrated to Google Cloud.
The network operations team has mandated that you can only use up to 250 Mbps of the total available bandwidth for the migration. You need to perform an online migration to Cloud Storage. What should you do?
A) Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the -- daisy-chain option.
B) Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.
C) Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.
D) Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the --no- clobber option.
3. You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?
A) Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
B) Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.
C) Use Cloud Composer to orchestrate the Spark job and email the report.
D) Use Cloud Run functions to trigger the Spark job and email the report.
4. Your organization has decided to migrate their existing enterprise data warehouse to BigQuery. The existing data pipeline tools already support connectors to BigQuery. You need to identify a data migration approach that optimizes migration speed. What should you do?
A) Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.
B) Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.
C) Create a temporary file system to facilitate data transfer from the existing environment to Cloud Storage. Use Storage Transfer Service to migrate the data into BigQuery.
D) Use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping.
5. Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?
A) Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.
B) Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
C) Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
D) Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: B | Question # 3 Answer: B | Question # 4 Answer: D | Question # 5 Answer: C |
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