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Google Associate-Data-Practitioner Exam Syllabus Topics:

SectionObjectives
Machine Learning- Machine learning fundamentals
  • 1. Feature engineering and selection
  • 2. Understanding ML concepts and workflows
  • 3. Choosing appropriate ML models
- Implementing ML solutions
  • 1. AutoML and custom training
  • 2. ML model deployment and serving
  • 3. Training and evaluating ML models
  • 4. Using Vertex AI for ML workflows
Data Management and Governance- Data storage and management
  • 1. Choosing appropriate data storage solutions (e.g., BigQuery, Cloud Storage, Firestore, Bigtable)
  • 2. Data lifecycle management
  • 3. Data warehouse and data lake design
- Data governance and quality
  • 1. Regulatory compliance and data privacy
  • 2. Data cataloging and metadata management (e.g., Data Catalog)
  • 3. Data quality monitoring and validation
  • 4. Data lineage and provenance
Data Analysis and Data Visualization- Analyzing data
  • 1. Using Looker for data analysis
  • 2. Statistical analysis and data modeling
  • 3. Performing data exploration and analysis
  • 4. Using BigQuery for data analysis
- Visualizing data
  • 1. Sharing and collaboration of data insights
  • 2. Data visualization best practices
  • 3. Creating dashboards and reports (e.g., Looker, Looker Studio)
Data Preparation and Data Processing- Ingesting data
  • 1. Streaming data ingestion (e.g., Pub/Sub, Dataflow)
  • 2. Data pipeline orchestration (e.g., Cloud Composer, Workflows)
  • 3. Batch data ingestion (e.g., Cloud Storage, Transfer Service, Datastream)
- Transforming data
  • 1. Data wrangling and enrichment
  • 2. Using SQL for data transformation (e.g., BigQuery)
  • 3. Using Apache Beam/Dataflow for data transformation
  • 4. Data cleansing and preprocessing

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Google Cloud Associate Data Practitioner Sample Questions (Q84-Q89):

NEW QUESTION # 84
You need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage.
The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis.
The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team's existing skillset, processes data at scale, and minimizes cost. What should you do?

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation:
The pipeline must handle large-scale log processing with existing Spark scripts, prioritizing skillset reuse, scalability, and cost. Let's break it down:
* Option A: Dataflow uses Apache Beam, not Spark, requiring script rewrites (losing skillset leverage).
Custom templates scale well but increase development cost and effort.
* Option B: Cloud Data Fusion is a visual ETL tool, not Spark-based. It doesn't reuse existing scripts, requiring redesign, and is less cost-efficient for complex, code-driven transformations.
* Option C: Dataform uses SQLX for BigQuery ELT, not Spark. It's unsuitable for pre-load transformations of raw logs and doesn't leverage Spark skills.


NEW QUESTION # 85
You work for a financial services company that handles highly sensitive dat a. Due to regulatory requirements, your company is required to have complete and manual control of data encryption. Which type of keys should you recommend to use for data storage?

Answer: A

Explanation:
For regulatory requirements that mandate complete and manual control of data encryption, you should use customer-supplied encryption keys (CSEK). With CSEK, your company provides the encryption keys for data storage, and Google Cloud does not store or manage these keys. This approach ensures that your organization retains full control and responsibility over the encryption process, meeting strict regulatory compliance requirements.


NEW QUESTION # 86
You are a database administrator managing sales transaction data by region stored in a BigQuery table. You need to ensure that each sales representative can only see the transactions in their region. What should you do?

Answer: B


NEW QUESTION # 87
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 TB of data to Cloud Storage securely, and only have a few days to move the dat a. What should you do?

Answer: B

Explanation:
Using Transfer Appliances is the best solution for securely and efficiently moving over 500 TB of data to Cloud Storage within a limited timeframe, especially with network bandwidth below 1 Gbps. Transfer Appliances are physical devices provided by Google Cloud to securely transfer large amounts of data. After copying the data to the appliances, they are shipped back to Google, where the data is uploaded to Cloud Storage. This approach bypasses bandwidth limitations and ensures the data is migrated quickly and securely.


NEW QUESTION # 88
Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards. You need to implement a solution quickly and efficiently. What should you do?

Answer: A

Explanation:
Defining a new measure in LookML to calculate the profit margin using the existing revenue and cost fields is the most efficient and straightforward solution. This approach allows you to dynamically compute the profit margin directly within your Looker Explores and dashboards without needing to pre-calculate or create additional tables. The measure can be defined using LookML syntax, such as:
measure: profit_margin {
type: number
sql: (revenue - cost) / revenue ;;
value_format: "0.0%"
}
This method is quick to implement and integrates seamlessly into your existing Looker model, enabling accurate visualization of profit margins across your products.


NEW QUESTION # 89
......

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