Google Associate-Data-Practitioner Exam With Confidence Using Practice Dumps

Comprehensive and Detailed In-Depth Explanation:

Transferring 300 TB over a 100 Mbps connection would take an impractical amount of time (over 300 days at theoretical maximum speed, ignoring real-world constraints like latency). Google Cloud provides the Transfer Appliance for large-scale, time-sensitive transfers.

Option A: Cloud Client Libraries over the internet would be slow and unreliable for 300 TB due to bandwidth limitations.

Option B: The gcloud storage command is similarly constrained by internet speed and not designed for such large transfers.

Option C: Compressing and splitting across multiple providers adds complexity and isn’t a Google-supported method for Cloud Storage ingestion.

UsingCloud Composerto 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.

Comprehensive and Detailed In-Depth Explanation:

Cost minimization requires matching storage classes to access patterns using lifecycle rules. Let’s assess:

Option A: Nearline during training (frequent access) incurs high retrieval costs and latency, unsuitable for ML workloads. Coldline after 30 days and Archive after 90 days are reasonable but misaligned initially.

Option B: Standard storage (no retrieval fees, low latency) is ideal for frequent access during training. Transitioning to Nearline (30-day minimum, low access) after 30 days and Coldline (90-day minimum, rare access) after 90 days matches the pattern and minimizes costs effectively.

Option C: Nearline during training is costly for frequent access, and Archive to Coldline is illogical (Archive is cheaper than Coldline).