A curated list of AWS resources to prepare for the AWS Certifications
A curated list of awesome AWS resources you need to prepare for the all 5 AWS Certifications. This gist will include: open source repos, blogs & blogposts, ebooks, PDF, whitepapers, video courses, free lecture, slides, sample test and many other resources.
The traditional technical interview process is designed to ferret out a candidate's weaknesses whereas the process should be designed to find a candidate's strengths.
No one can possibly master all of the arcana of today's technology landscape, let alone bring that mastery to bear on a problem under pressure and with no tools other than a whiteboard.
Under those circumstances, everyone can make anyone look like an idiot.
The fundamental problem with the traditional technical interview process is that it is based on a chain of inference that seems reasonable but is in fact deeply flawed. That chain goes something like this:
| set -o errexit | |
| TABLE_NAME="${1:?You must pass a TABLE_NAME as first argument}" | |
| STARTID="${2:?You must pass a STARTID as 2nd argument}" | |
| ENDID="${3:?You must pass a ENDID as 3rd argument}" | |
| [[ -z "$4" ]] && LIMIT="" || LIMIT="LIMIT $4" | |
| . logins.sh | |
| INSERT_COMMAND="INSERT INTO clickhouse_table(column1,column2,...) FORMAT TSV" |
| Latency Comparison Numbers (~2012) | |
| ---------------------------------- | |
| L1 cache reference 0.5 ns | |
| Branch mispredict 5 ns | |
| L2 cache reference 7 ns 14x L1 cache | |
| Mutex lock/unlock 25 ns | |
| Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
| Compress 1K bytes with Zippy 3,000 ns 3 us | |
| Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
| Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
| import anthropic | |
| client = anthropic.Anthropic( | |
| api_key="my_api_key", | |
| ) | |
| message = client.messages.create( | |
| model="claude-3-opus-20240229", | |
| max_tokens=1000, | |
| temperature=0, | |
| messages=[ |