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Google Associate-Data-Practitioner Exam Syllabus Topics:

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

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Google Cloud Associate Data Practitioner Sample Questions (Q73-Q78):

NEW QUESTION # 73
Your retail organization stores sensitive application usage data in Cloud Storage. You need to encrypt the data without the operational overhead of managing encryption keys. What should you do?

Answer: C

Explanation:
Using Google-managed encryption keys (GMEK) is the best choice when you want to encrypt sensitive data in Cloud Storage without the operational overhead of managing encryption keys. GMEK is the default encryption mechanism in Google Cloud, and it ensures that data is automatically encrypted at rest with no additional setup or maintenance required. It provides strong security while eliminating the need for manual key management.
Google Cloud encrypts all data at rest by default, and the simplest way to avoid key management overhead is to use Google-managed encryption keys (GMEK).
* Option A: GMEK is fully managed by Google, requiring no user intervention, and meets the requirement of no operational overhead while ensuring encryption.
* Option B: CMEK requires managing keys in Cloud KMS, adding operational overhead.
* Option C: CSEK requires users to supply and manage keys externally, increasing complexity significantly.


NEW QUESTION # 74
Your organization needs to implement near real-time analytics for thousands of events arriving each second in Pub/Sub. The incoming messages require transformations. You need to configure a pipeline that processes, transforms, and loads the data into BigQuery while minimizing development time. What should you do?

Answer: B

Explanation:
Using a Google-provided Dataflow template is the most efficient and development-friendly approach to implement near real-time analytics for Pub/Sub messages. Dataflow templates are pre-built and optimized for processing streaming data, allowing you to quickly configure and deploy a pipeline with minimal development effort. These templates can handle message ingestion from Pub/Sub, perform necessary transformations, and load the processed data into BigQuery, ensuring scalability and low latency for near real-time analytics.


NEW QUESTION # 75
You manage a large amount of data in Cloud Storage, including raw data, processed data, and backups. Your organization is subject to strict compliance regulations that mandate data immutability for specific data types. You want to use an efficient process to reduce storage costs while ensuring that your storage strategy meets retention requirements. What should you do?

Answer: B

Explanation:
Using object holds and lifecycle management rules is the most efficient and compliant strategy for this scenario because:
Immutability: Object holds (temporary or event-based) ensure that objects cannot be deleted or overwritten, meeting strict compliance regulations for data immutability.
Cost efficiency: Lifecycle management rules automatically transition objects to more cost-effective storage classes based on their age and access patterns.
Compliance and automation: This approach ensures compliance with retention requirements while reducing manual effort, leveraging built-in Cloud Storage features.


NEW QUESTION # 76
You manage an ecommerce website that has a diverse range of products. You need to forecast future product demand accurately to ensure that your company has sufficient inventory to meet customer needs and avoid stockouts. Your company's historical sales data is stored in a BigQuery table. You need to create a scalable solution that takes into account the seasonality and historical data to predict product demand. What should you do?

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation:
Forecasting product demand with seasonality requires a time series model, and BigQuery ML offers a scalable, serverless solution. Let's analyze:
* Option A: BigQuery ML's time series models (e.g., ARIMA_PLUS) are designed for forecasting with seasonality and trends. The ML.FORECAST function generates predictions based on historical data, storing them in a table. This is scalable (no infrastructure) and integrates natively with BigQuery, ideal for ecommerce demand prediction.
* Option B: Colab Enterprise with a custom Python model (e.g., Prophet) is flexible but requires coding, maintenance, and potentially exporting data, reducing scalability compared to BigQuery ML's in-place processing.
* Option C: Linear regression predicts continuous values but doesn't handle seasonality or time series patterns effectively, making it unsuitable for demand forecasting.


NEW QUESTION # 77
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?

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation:
For a 25 TB dataset, efficiency and cost require minimizing data movement and leveraging BigQuery's scalability within Colab Enterprise.
* Option A: Exporting 25 TB to Google Drive and loading via Pandas is impractical (size limits, transfer costs) and slow.
* Option B: BigQuery magic commands (%%bigquery) in Colab Enterprise allow direct querying of BigQuery data, keeping processing in the cloud, reducing costs, and enabling collaboration.
* Option C: Dataproc with Spark adds cluster costs and complexity, unnecessary when BigQuery can handle the workload.


NEW QUESTION # 78
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