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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Data Management and Governance	25%	- Compliance and governance 1. Data stewardship and cataloging 2. Data privacy and regulatory requirements - Data security and access control 1. IAM roles and permissions 2. Data encryption and protection - Data quality and maintenance 1. Data lifecycle management 2. Data validation and cleaning
Data Preparation and Ingestion	30%	- 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 extraction and transfer tools 1. Cloud Data Fusion, Storage Transfer Service 2. Dataflow, BigQuery Data Transfer Service, Database Migration Service - Data loading methods 1. Batch and streaming ingestion 2. gcloud, BQ CLI, client libraries
Data Pipeline Orchestration	18%	- Data transformation concepts 1. Data transformation requirements 2. ETL vs ELT processes - Transformation tools selection 1. Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer, Dataform - Pipeline automation and scheduling 1. Error handling and monitoring 2. Schedule and monitor data jobs
Data Analysis and Presentation	27%	- Data exploration and analysis 1. Identify trends, patterns, insights 2. Jupyter notebooks for analysis 3. BigQuery SQL queries - Data visualization and reporting 1. Looker dashboards and reports 2. Visualization best practices - Business intelligence and decision support 1. Communicate findings effectively 2. Answer business questions with data

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Google Associate Data Practitioner Sample Questions (Q57-Q62):

NEW QUESTION # 57
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 view that filters out rows that are older than one year.
B. Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.
C. 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.
D. Require users to specify a partition filter using the alter table statement in SQL.

Answer: B

NEW QUESTION # 58
Your company uses Looker to visualize and analyze sales dat
a. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?

A. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
B. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
C. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
D. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.

Answer: C

NEW QUESTION # 59
You are a data analyst working with sensitive customer data in BigQuery. You need to ensure that only authorized personnel within your organization can query this data, while following the principle of least privilege. What should you do?

A. Encrypt the data by using customer-managed encryption keys (CMEK).
B. Export the data to Cloud Storage, and use signed URLs to authorize access.
C. Enable access control by using IAM roles.
D. Update dataset privileges by using the SQL GRANT statement.

Answer: C

NEW QUESTION # 60
You manage data at an ecommerce company. You have a Dataflow pipeline that processes order data from Pub/Sub, enriches the data with product information from Bigtable, and writes the processed data to BigQuery for analysis. The pipeline runs continuously and processes thousands of orders every minute. You need to monitor the pipeline's performance and be alerted if errors occur. What should you do?

A. Use Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.
B. Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.
C. Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.
D. Use BigQuery to analyze the processed data in Cloud Storage and identify anomalies or inconsistencies. Set up scheduled alerts based when anomalies or inconsistencies occur.

Answer: A

NEW QUESTION # 61
Your organization has decided to move their on-premises Apache Spark-based workload to Google Cloud.
You want to be able to manage the code without needing to provision and manage your own cluster. What should you do?

A. Migrate the Spark jobs to Dataproc on Compute Engine.
B. Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
C. Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
D. Migrate the Spark jobs to Dataproc Serverless.

Answer: D

NEW QUESTION # 62
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