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Google Associate-Data-Practitioner Exam Syllabus Topics:

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

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Google Cloud Associate Data Practitioner Sample Questions (Q103-Q108):

NEW QUESTION # 103
You are building a batch data pipeline to process 100 GB of structured data from multiple sources for daily reporting. You need to transform and standardize the data prior to loading the data to ensure that it is stored in a single dataset. You want to use a low-code solution that can be easily built and managed. What should you do?

Answer: B

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why B is correct:Cloud Data Fusion is a fully managed, cloud-native data integration service for building and managing ETL/ELT data pipelines.
It provides a graphical interface for building pipelines without coding, making it a low-code solution.
Cloud data fusion is perfect for the ingestion, transformation and loading of data into BigQuery.
Why other options are incorrect:A: Looker studio is for visualization, not data transformation.
C: Cloud SQL is a relational database, not ideal for large-scale analytical data.
D: Cloud run is for stateless applications, not batch data processing.


NEW QUESTION # 104
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 # 105
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?

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation:
The solution must support SQL-based ELT, be serverless and cost-effective, and include advanced features like version control and quality checks. Let's dive in:
* Option A: Cloud Data Fusion is a visual ETL tool, not SQL-centric (uses plugins), and isn't fully serverless (requires instance management). It lacks native source code control and parameterization.
* Option B: Dataform is a serverless, SQL-based ELT platform for BigQuery. It uses SQLX scripts, integrates with Git for version control, supports environment variables (parameterization), and offers assertions for data quality-all meeting the requirements cost-effectively.
* Option C: Dataproc is for Spark/MapReduce, not SQL ELT, and requires cluster management, contradicting serverless and cost goals.


NEW QUESTION # 106
You are designing a pipeline to process data files that arrive in Cloud Storage by 3:00 am each day. Data processing is performed in stages, where the output of one stage becomes the input of the next. Each stage takes a long time to run. Occasionally a stage fails, and you have to address the problem. You need to ensure that the final output is generated as quickly as possible. What should you do?

Answer: C

Explanation:
Using Cloud Composer to design the processing pipeline as a Directed Acyclic Graph (DAG) is the most suitable approach because:
Fault tolerance: Cloud Composer (based on Apache Airflow) allows for handling failures at specific stages. You can clear the state of a failed task and rerun it without reprocessing the entire pipeline.
Stage-based processing: DAGs are ideal for workflows with interdependent stages where the output of one stage serves as input to the next.
Efficiency: This approach minimizes downtime and ensures that only failed stages are rerun, leading to faster final output generation.


NEW QUESTION # 107
You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?

Answer: B

Explanation:
Using the BigQuery Python client library to query and preprocess data directly in BigQuery and then leveraging BigQueryML to train the churn prediction model is the Google-recommended approach for this scenario. BigQueryML allows you to build machine learning models directly within BigQuery using SQL, eliminating the need to export data or manage additional infrastructure. This minimizes overhead, scales effectively for a dataset as large as 50 PB, and simplifies the end-to-end process of building and training the churn prediction model.


NEW QUESTION # 108
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