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Google ADP Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Pipeline Orchestration18%- Pipeline automation and scheduling
  • 1. Schedule and monitor data jobs
  • 2. Error handling and monitoring
- Data transformation concepts
  • 1. ETL vs ELT processes
  • 2. Data transformation requirements
- Transformation tools selection
  • 1. Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer, Dataform
Topic 2: Data Management and Governance25%- Compliance and governance
  • 1. Data stewardship and cataloging
  • 2. Data privacy and regulatory requirements
- Data security and access control
  • 1. Data encryption and protection
  • 2. IAM roles and permissions
- Data quality and maintenance
  • 1. Data lifecycle management
  • 2. Data validation and cleaning
Topic 3: Data Analysis and Presentation27%- Data visualization and reporting
  • 1. Visualization best practices
  • 2. Looker dashboards and reports
- Business intelligence and decision support
  • 1. Answer business questions with data
  • 2. Communicate findings effectively
- Data exploration and analysis
  • 1. BigQuery SQL queries
  • 2. Identify trends, patterns, insights
  • 3. Jupyter notebooks for analysis
Topic 4: Data Preparation and Ingestion30%- Data extraction and transfer tools
  • 1. Cloud Data Fusion, Storage Transfer Service
  • 2. Dataflow, BigQuery Data Transfer Service, Database Migration Service
- Storage solutions selection
  • 1. Storage location types: regional, dual-regional, multi-regional, zonal
  • 2. Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, Spanner
- Data formats and classification
  • 1. Formats: CSV, JSON, Parquet, Avro, database tables
  • 2. Structured, semi-structured, unstructured data
- Data loading methods
  • 1. Batch and streaming ingestion
  • 2. gcloud, BQ CLI, client libraries

Google Associate Data Practitioner Sample Questions:

1. Your organization uses scheduled queries to perform transformations on data stored in BigQuery. You discover that one of your scheduled queries has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

A) Navigate to the Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.
B) Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.
C) Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.
D) Set up a log sink using the gcloud CLI to export BigQuery audit logs to BigQuery. Query those logs to identify the error associated with the failed job I


2. Your organization uses a BigQuery table that is partitioned by ingestion time. You need to remove data that is older than one year to reduce your organization's storage costs. You want to use the most efficient approach while minimizing cost. What should you do?

A) Create a scheduled query that periodically runs an update statement in SQL that sets the "deleted" column to "yes" for data that is more than one year old. Create a view that filters out rows that have been marked deleted.
B) Create a view that filters out rows that are older than one year.
C) Require users to specify a partition filter using the alter table statement in SQL.
D) Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.


3. You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model.
What should you do?

A) Compare the confusion matrix.
B) Compare the two different models.
C) Evaluate data drift.
D) Evaluate the data skewness.


4. You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report. What should you do?

A) Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
B) Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
C) Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
D) Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.


5. Your company stores historical data in Cloud Storage. You need to ensure that all data is saved in a bucket for at least three years. What should you do?

A) Set temporary object holds.
B) Enable Object Versioning.
C) Set a bucket retention policy.
D) Change the bucket storage class to Archive.


Solutions:

Question # 1
Answer: C
Question # 2
Answer: D
Question # 3
Answer: C
Question # 4
Answer: A
Question # 5
Answer: C

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